Class: Aws::SageMaker::Client

Inherits:
Seahorse::Client::Base
  • Object
show all
Includes:
ClientStubs
Defined in:
lib/aws-sdk-sagemaker/client.rb

Overview

An API client for SageMaker. To construct a client, you need to configure a ‘:region` and `:credentials`.

client = Aws::SageMaker::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

For details on configuring region and credentials see the [developer guide](/sdk-for-ruby/v3/developer-guide/setup-config.html).

See #initialize for a full list of supported configuration options.

Class Attribute Summary collapse

API Operations collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(options) ⇒ Client

Returns a new instance of Client.

Parameters:

  • options (Hash)

Options Hash (options):

  • :plugins (Array<Seahorse::Client::Plugin>) — default: []] —

    A list of plugins to apply to the client. Each plugin is either a class name or an instance of a plugin class.

  • :credentials (required, Aws::CredentialProvider) —

    Your AWS credentials. This can be an instance of any one of the following classes:

    • ‘Aws::Credentials` - Used for configuring static, non-refreshing credentials.

    • ‘Aws::SharedCredentials` - Used for loading static credentials from a shared file, such as `~/.aws/config`.

    • ‘Aws::AssumeRoleCredentials` - Used when you need to assume a role.

    • ‘Aws::AssumeRoleWebIdentityCredentials` - Used when you need to assume a role after providing credentials via the web.

    • ‘Aws::SSOCredentials` - Used for loading credentials from AWS SSO using an access token generated from `aws login`.

    • ‘Aws::ProcessCredentials` - Used for loading credentials from a process that outputs to stdout.

    • ‘Aws::InstanceProfileCredentials` - Used for loading credentials from an EC2 IMDS on an EC2 instance.

    • ‘Aws::ECSCredentials` - Used for loading credentials from instances running in ECS.

    • ‘Aws::CognitoIdentityCredentials` - Used for loading credentials from the Cognito Identity service.

    When ‘:credentials` are not configured directly, the following locations will be searched for credentials:

    • ‘Aws.config`

    • The ‘:access_key_id`, `:secret_access_key`, and `:session_token` options.

    • ENV, ENV

    • ‘~/.aws/credentials`

    • ‘~/.aws/config`

    • EC2/ECS IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of ‘Aws::InstanceProfileCredentails` or `Aws::ECSCredentials` to enable retries and extended timeouts. Instance profile credential fetching can be disabled by setting ENV to true.

  • :region (required, String) —

    The AWS region to connect to. The configured ‘:region` is used to determine the service `:endpoint`. When not passed, a default `:region` is searched for in the following locations:

  • :access_key_id (String)
  • :active_endpoint_cache (Boolean) — default: false —

    When set to ‘true`, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to `false`.

  • :adaptive_retry_wait_to_fill (Boolean) — default: true —

    Used only in ‘adaptive` retry mode. When true, the request will sleep until there is sufficent client side capacity to retry the request. When false, the request will raise a `RetryCapacityNotAvailableError` and will not retry instead of sleeping.

  • :client_side_monitoring (Boolean) — default: false —

    When ‘true`, client-side metrics will be collected for all API requests from this client.

  • :client_side_monitoring_client_id (String) — default: "" —

    Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string.

  • :client_side_monitoring_host (String) — default: "127.0.0.1" —

    Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_port (Integer) — default: 31000 —

    Required for publishing client metrics. The port that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) — default: Aws::ClientSideMonitoring::Publisher —

    Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher.

  • :convert_params (Boolean) — default: true —

    When ‘true`, an attempt is made to coerce request parameters into the required types.

  • :correct_clock_skew (Boolean) — default: true —

    Used only in ‘standard` and adaptive retry modes. Specifies whether to apply a clock skew correction and retry requests with skewed client clocks.

  • :defaults_mode (String) — default: "legacy" —

    See DefaultsModeConfiguration for a list of the accepted modes and the configuration defaults that are included.

  • :disable_host_prefix_injection (Boolean) — default: false —

    Set to true to disable SDK automatically adding host prefix to default service endpoint when available.

  • :disable_request_compression (Boolean) — default: false —

    When set to ‘true’ the request body will not be compressed for supported operations.

  • :endpoint (String, URI::HTTPS, URI::HTTP) —

    Normally you should not configure the ‘:endpoint` option directly. This is normally constructed from the `:region` option. Configuring `:endpoint` is normally reserved for connecting to test or custom endpoints. The endpoint should be a URI formatted like:

    'http://example.com'
'https://example.com'
'http://example.com:123'
  • :endpoint_cache_max_entries (Integer) — default: 1000 —

    Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000.

  • :endpoint_cache_max_threads (Integer) — default: 10 —

    Used for the maximum threads in use for polling endpoints to be cached, defaults to 10.

  • :endpoint_cache_poll_interval (Integer) — default: 60 —

    When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec.

  • :endpoint_discovery (Boolean) — default: false —

    When set to ‘true`, endpoint discovery will be enabled for operations when available.

  • :ignore_configured_endpoint_urls (Boolean) —

    Setting to true disables use of endpoint URLs provided via environment variables and the shared configuration file.

  • :log_formatter (Aws::Log::Formatter) — default: Aws::Log::Formatter.default —

    The log formatter.

  • :log_level (Symbol) — default: :info —

    The log level to send messages to the ‘:logger` at.

  • :logger (Logger) —

    The Logger instance to send log messages to. If this option is not set, logging will be disabled.

  • :max_attempts (Integer) — default: 3 —

    An integer representing the maximum number attempts that will be made for a single request, including the initial attempt. For example, setting this value to 5 will result in a request being retried up to 4 times. Used in ‘standard` and `adaptive` retry modes.

  • :profile (String) — default: "default" —

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, ‘default’ is used.

  • :request_min_compression_size_bytes (Integer) — default: 10240 —

    The minimum size in bytes that triggers compression for request bodies. The value must be non-negative integer value between 0 and 10485780 bytes inclusive.

  • :retry_backoff (Proc) —

    A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay. This option is only used in the ‘legacy` retry mode.

  • :retry_base_delay (Float) — default: 0.3 —

    The base delay in seconds used by the default backoff function. This option is only used in the ‘legacy` retry mode.

  • :retry_jitter (Symbol) — default: :none —

    A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number. This option is only used in the ‘legacy` retry mode.

    @see www.awsarchitectureblog.com/2015/03/backoff.html

  • :retry_limit (Integer) — default: 3 —

    The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors, auth errors, endpoint discovery, and errors from expired credentials. This option is only used in the ‘legacy` retry mode.

  • :retry_max_delay (Integer) — default: 0 —

    The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function. This option is only used in the ‘legacy` retry mode.

  • :retry_mode (String) — default: "legacy" —

    Specifies which retry algorithm to use. Values are:

    • ‘legacy` - The pre-existing retry behavior. This is default value if no retry mode is provided.

    • ‘standard` - A standardized set of retry rules across the AWS SDKs. This includes support for retry quotas, which limit the number of unsuccessful retries a client can make.

    • ‘adaptive` - An experimental retry mode that includes all the functionality of `standard` mode along with automatic client side throttling. This is a provisional mode that may change behavior in the future.

  • :sdk_ua_app_id (String) —

    A unique and opaque application ID that is appended to the User-Agent header as app/sdk_ua_app_id. It should have a maximum length of 50. This variable is sourced from environment variable AWS_SDK_UA_APP_ID or the shared config profile attribute sdk_ua_app_id.

  • :secret_access_key (String)
  • :session_token (String)
  • :sigv4a_signing_region_set (Array) —

    A list of regions that should be signed with SigV4a signing. When not passed, a default ‘:sigv4a_signing_region_set` is searched for in the following locations:

  • :simple_json (Boolean) — default: false —

    Disables request parameter conversion, validation, and formatting. Also disables response data type conversions. The request parameters hash must be formatted exactly as the API expects.This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures.

  • :stub_responses (Boolean) — default: false —

    Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling ClientStubs#stub_responses. See ClientStubs for more information.

    ** Please note ** When response stubbing is enabled, no HTTP requests are made, and retries are disabled.

  • :telemetry_provider (Aws::Telemetry::TelemetryProviderBase) — default: Aws::Telemetry::NoOpTelemetryProvider —

    Allows you to provide a telemetry provider, which is used to emit telemetry data. By default, uses ‘NoOpTelemetryProvider` which will not record or emit any telemetry data. The SDK supports the following telemetry providers:

    • OpenTelemetry (OTel) - To use the OTel provider, install and require the

    ‘opentelemetry-sdk` gem and then, pass in an instance of a `Aws::Telemetry::OTelProvider` for telemetry provider.

  • :token_provider (Aws::TokenProvider) —

    A Bearer Token Provider. This can be an instance of any one of the following classes:

    • ‘Aws::StaticTokenProvider` - Used for configuring static, non-refreshing tokens.

    • ‘Aws::SSOTokenProvider` - Used for loading tokens from AWS SSO using an access token generated from `aws login`.

    When ‘:token_provider` is not configured directly, the `Aws::TokenProviderChain` will be used to search for tokens configured for your profile in shared configuration files.

  • :use_dualstack_endpoint (Boolean) —

    When set to ‘true`, dualstack enabled endpoints (with `.aws` TLD) will be used if available.

  • :use_fips_endpoint (Boolean) —

    When set to ‘true`, fips compatible endpoints will be used if available. When a `fips` region is used, the region is normalized and this config is set to `true`.

  • :validate_params (Boolean) — default: true —

    When ‘true`, request parameters are validated before sending the request.

  • :endpoint_provider (Aws::SageMaker::EndpointProvider) —

    The endpoint provider used to resolve endpoints. Any object that responds to ‘#resolve_endpoint(parameters)` where `parameters` is a Struct similar to `Aws::SageMaker::EndpointParameters`

  • :http_continue_timeout (Float) — default: 1 —

    The number of seconds to wait for a 100-continue response before sending the request body. This option has no effect unless the request has “Expect” header set to “100-continue”. Defaults to ‘nil` which disables this behaviour. This value can safely be set per request on the session.

  • :http_idle_timeout (Float) — default: 5 —

    The number of seconds a connection is allowed to sit idle before it is considered stale. Stale connections are closed and removed from the pool before making a request.

  • :http_open_timeout (Float) — default: 15 —

    The default number of seconds to wait for response data. This value can safely be set per-request on the session.

  • :http_proxy (URI::HTTP, String) —

    A proxy to send requests through. Formatted like ‘proxy.com:123’.

  • :http_read_timeout (Float) — default: 60 —

    The default number of seconds to wait for response data. This value can safely be set per-request on the session.

  • :http_wire_trace (Boolean) — default: false —

    When ‘true`, HTTP debug output will be sent to the `:logger`.

  • :on_chunk_received (Proc) —

    When a Proc object is provided, it will be used as callback when each chunk of the response body is received. It provides three arguments: the chunk, the number of bytes received, and the total number of bytes in the response (or nil if the server did not send a ‘content-length`).

  • :on_chunk_sent (Proc) —

    When a Proc object is provided, it will be used as callback when each chunk of the request body is sent. It provides three arguments: the chunk, the number of bytes read from the body, and the total number of bytes in the body.

  • :raise_response_errors (Boolean) — default: true —

    When ‘true`, response errors are raised.

  • :ssl_ca_bundle (String) —

    Full path to the SSL certificate authority bundle file that should be used when verifying peer certificates. If you do not pass ‘:ssl_ca_bundle` or `:ssl_ca_directory` the the system default will be used if available.

  • :ssl_ca_directory (String) —

    Full path of the directory that contains the unbundled SSL certificate authority files for verifying peer certificates. If you do not pass ‘:ssl_ca_bundle` or `:ssl_ca_directory` the the system default will be used if available.

  • :ssl_ca_store (String) —

    Sets the X509::Store to verify peer certificate.

  • :ssl_timeout (Float) —

    Sets the SSL timeout in seconds

  • :ssl_verify_peer (Boolean) — default: true —

    When ‘true`, SSL peer certificates are verified when establishing a connection.



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# File 'lib/aws-sdk-sagemaker/client.rb', line 441

def initialize(*args)
  super
end

Class Attribute Details

.identifier ⇒ Object (readonly)

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future. 



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27890

def identifier
  @identifier
end

Class Method Details

.errors_module ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future. 



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27893

def errors_module
  Errors
end

Instance Method Details

#add_association(params = {}) ⇒ Types::AddAssociationResponse

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see [Amazon SageMaker ML Lineage Tracking].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

Examples:

Request syntax with placeholder values


resp = client.add_association({
  source_arn: "AssociationEntityArn", # required
  destination_arn: "AssociationEntityArn", # required
  association_type: "ContributedTo", # accepts ContributedTo, AssociatedWith, DerivedFrom, Produced, SameAs
})

Response structure


resp.source_arn #=> String
resp.destination_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_arn (required, String) —

    The ARN of the source.

  • :destination_arn (required, String) —

    The Amazon Resource Name (ARN) of the destination.

  • :association_type (String) —

    The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.

    • ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.

    • AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.

    • DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.

    • Produced - The source generated the destination. For example, a training job produced a model artifact.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 503

def add_association(params = {}, options = {})
  req = build_request(:add_association, params)
  req.send_request(options)
end

#add_tags(params = {}) ⇒ Types::AddTagsOutput

Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see [Amazon Web Services Tagging Strategies].

<note markdown=“1”> Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the ‘Tags` parameter of

CreateHyperParameterTuningJob][2

</note>

<note markdown=“1”> Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the ‘Tags` parameter of [CreateDomain] or [CreateUserProfile].

</note>

[1]: aws.amazon.com/answers/account-management/aws-tagging-strategies/ [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateDomain.html [4]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateUserProfile.html

Examples:

Request syntax with placeholder values


resp = client.add_tags({
  resource_arn: "ResourceArn", # required
  tags: [ # required
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :resource_arn (required, String) —

    The Amazon Resource Name (ARN) of the resource that you want to tag.

  • :tags (required, Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

  • (Types::AddTagsOutput) —

    Returns a response object which responds to the following methods:

    • #tags => Array&lt;Types::Tag&gt;

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 586

def add_tags(params = {}, options = {})
  req = build_request(:add_tags, params)
  req.send_request(options)
end

#associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse

Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the [DisassociateTrialComponent] API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DisassociateTrialComponent.html

Examples:

Request syntax with placeholder values


resp = client.associate_trial_component({
  trial_component_name: "ExperimentEntityName", # required
  trial_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_component_arn #=> String
resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component to associated with the trial.

  • :trial_name (required, String) —

    The name of the trial to associate with.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 626

def associate_trial_component(params = {}, options = {})
  req = build_request(:associate_trial_component, params)
  req.send_request(options)
end

#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput

This action batch describes a list of versioned model packages

Examples:

Request syntax with placeholder values


resp = client.batch_describe_model_package({
  model_package_arn_list: ["ModelPackageArn"], # required
})

Response structure


resp.model_package_summaries #=> Hash
resp.model_package_summaries["ModelPackageArn"].model_package_group_name #=> String
resp.model_package_summaries["ModelPackageArn"].model_package_version #=> Integer
resp.model_package_summaries["ModelPackageArn"].model_package_arn #=> String
resp.model_package_summaries["ModelPackageArn"].model_package_description #=> String
resp.model_package_summaries["ModelPackageArn"].creation_time #=> Time
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].container_hostname #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].image #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].image_digest #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_url #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].product_id #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].environment #=> Hash
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].environment["EnvironmentKey"] #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_input.data_input_config #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].framework #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].framework_version #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].nearest_model_name #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.s3_data_type #=> String, one of "S3Object", "S3Prefix"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_transform_instance_types #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_realtime_inference_instance_types #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_content_types #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_content_types[0] #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_response_mime_types #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_response_mime_types[0] #=> String
resp.model_package_summaries["ModelPackageArn"].model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.model_package_summaries["ModelPackageArn"].model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval"
resp.batch_describe_model_package_error_map #=> Hash
resp.batch_describe_model_package_error_map["ModelPackageArn"].error_code #=> String
resp.batch_describe_model_package_error_map["ModelPackageArn"].error_response #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_arn_list (required, Array<String>) —

    The list of Amazon Resource Name (ARN) of the model package groups.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 693

def batch_describe_model_package(params = {}, options = {})
  req = build_request(:batch_describe_model_package, params)
  req.send_request(options)
end

#build_request(operation_name, params = {}) ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Parameters:

  • params ({}) (defaults to: {}) 


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# File 'lib/aws-sdk-sagemaker/client.rb', line 27726

def build_request(operation_name, params = {})
  handlers = @handlers.for(operation_name)
  tracer = config.telemetry_provider.tracer_provider.tracer(
    Aws::Telemetry.module_to_tracer_name('Aws::SageMaker')
  )
  context = Seahorse::Client::RequestContext.new(
    operation_name: operation_name,
    operation: config.api.operation(operation_name),
    client: self,
    params: params,
    config: config,
    tracer: tracer
  )
  context[:gem_name] = 'aws-sdk-sagemaker'
  context[:gem_version] = '1.259.0'
  Seahorse::Client::Request.new(handlers, context)
end

#create_action(params = {}) ⇒ Types::CreateActionResponse

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see [Amazon SageMaker ML Lineage Tracking].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

Examples:

Request syntax with placeholder values


resp = client.create_action({
  action_name: "ExperimentEntityName", # required
  source: { # required
    source_uri: "SourceUri", # required
    source_type: "String256",
    source_id: "String256",
  },
  action_type: "String256", # required
  description: "ExperimentDescription",
  status: "Unknown", # accepts Unknown, InProgress, Completed, Failed, Stopping, Stopped
  properties: {
    "StringParameterValue" => "StringParameterValue",
  },
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.action_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :action_name (required, String) —

    The name of the action. Must be unique to your account in an Amazon Web Services Region.

  • :source (required, Types::ActionSource) —

    The source type, ID, and URI.

  • :action_type (required, String) —

    The action type.

  • :description (String) —

    The description of the action.

  • :status (String) —

    The status of the action.

  • :properties (Hash<String,String>) —

    A list of properties to add to the action.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the action.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 774

def create_action(params = {}, options = {})
  req = build_request(:create_action, params)
  req.send_request(options)
end

#create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

Examples:

Request syntax with placeholder values


resp = client.create_algorithm({
  algorithm_name: "EntityName", # required
  algorithm_description: "EntityDescription",
  training_specification: { # required
    training_image: "ContainerImage", # required
    training_image_digest: "ImageDigest",
    supported_hyper_parameters: [
      {
        name: "ParameterName", # required
        description: "EntityDescription",
        type: "Integer", # required, accepts Integer, Continuous, Categorical, FreeText
        range: {
          integer_parameter_range_specification: {
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
          },
          continuous_parameter_range_specification: {
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
          },
          categorical_parameter_range_specification: {
            values: ["ParameterValue"], # required
          },
        },
        is_tunable: false,
        is_required: false,
        default_value: "HyperParameterValue",
      },
    ],
    supported_training_instance_types: ["ml.m4.xlarge"], # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
    supports_distributed_training: false,
    metric_definitions: [
      {
        name: "MetricName", # required
        regex: "MetricRegex", # required
      },
    ],
    training_channels: [ # required
      {
        name: "ChannelName", # required
        description: "EntityDescription",
        is_required: false,
        supported_content_types: ["ContentType"], # required
        supported_compression_types: ["None"], # accepts None, Gzip
        supported_input_modes: ["Pipe"], # required, accepts Pipe, File, FastFile
      },
    ],
    supported_tuning_job_objective_metrics: [
      {
        type: "Maximize", # required, accepts Maximize, Minimize
        metric_name: "MetricName", # required
      },
    ],
    additional_s3_data_source: {
      s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
      s3_uri: "S3Uri", # required
      compression_type: "None", # accepts None, Gzip
    },
  },
  inference_specification: {
    containers: [ # required
      {
        container_hostname: "ContainerHostname",
        image: "ContainerImage", # required
        image_digest: "ImageDigest",
        model_data_url: "Url",
        model_data_source: {
          s3_data_source: {
            s3_uri: "S3ModelUri", # required
            s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
            compression_type: "None", # required, accepts None, Gzip
            model_access_config: {
              accept_eula: false, # required
            },
            hub_access_config: {
              hub_content_arn: "HubContentArn", # required
            },
          },
        },
        product_id: "ProductId",
        environment: {
          "EnvironmentKey" => "EnvironmentValue",
        },
        model_input: {
          data_input_config: "DataInputConfig", # required
        },
        framework: "String",
        framework_version: "ModelPackageFrameworkVersion",
        nearest_model_name: "String",
        additional_s3_data_source: {
          s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
          s3_uri: "S3Uri", # required
          compression_type: "None", # accepts None, Gzip
        },
      },
    ],
    supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
    supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
    supported_content_types: ["ContentType"],
    supported_response_mime_types: ["ResponseMIMEType"],
  },
  validation_specification: {
    validation_role: "RoleArn", # required
    validation_profiles: [ # required
      {
        profile_name: "EntityName", # required
        training_job_definition: { # required
          training_input_mode: "Pipe", # required, accepts Pipe, File, FastFile
          hyper_parameters: {
            "HyperParameterKey" => "HyperParameterValue",
          },
          input_data_config: [ # required
            {
              channel_name: "ChannelName", # required
              data_source: { # required
                s3_data_source: {
                  s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
                  s3_uri: "S3Uri", # required
                  s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
                  attribute_names: ["AttributeName"],
                  instance_group_names: ["InstanceGroupName"],
                },
                file_system_data_source: {
                  file_system_id: "FileSystemId", # required
                  file_system_access_mode: "rw", # required, accepts rw, ro
                  file_system_type: "EFS", # required, accepts EFS, FSxLustre
                  directory_path: "DirectoryPath", # required
                },
              },
              content_type: "ContentType",
              compression_type: "None", # accepts None, Gzip
              record_wrapper_type: "None", # accepts None, RecordIO
              input_mode: "Pipe", # accepts Pipe, File, FastFile
              shuffle_config: {
                seed: 1, # required
              },
            },
          ],
          output_data_config: { # required
            kms_key_id: "KmsKeyId",
            s3_output_path: "S3Uri", # required
            compression_type: "GZIP", # accepts GZIP, NONE
          },
          resource_config: { # required
            instance_type: "ml.m4.xlarge", # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
            instance_count: 1,
            volume_size_in_gb: 1, # required
            volume_kms_key_id: "KmsKeyId",
            keep_alive_period_in_seconds: 1,
            instance_groups: [
              {
                instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
                instance_count: 1, # required
                instance_group_name: "InstanceGroupName", # required
              },
            ],
          },
          stopping_condition: { # required
            max_runtime_in_seconds: 1,
            max_wait_time_in_seconds: 1,
            max_pending_time_in_seconds: 1,
          },
        },
        transform_job_definition: {
          max_concurrent_transforms: 1,
          max_payload_in_mb: 1,
          batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord
          environment: {
            "TransformEnvironmentKey" => "TransformEnvironmentValue",
          },
          transform_input: { # required
            data_source: { # required
              s3_data_source: { # required
                s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
                s3_uri: "S3Uri", # required
              },
            },
            content_type: "ContentType",
            compression_type: "None", # accepts None, Gzip
            split_type: "None", # accepts None, Line, RecordIO, TFRecord
          },
          transform_output: { # required
            s3_output_path: "S3Uri", # required
            accept: "Accept",
            assemble_with: "None", # accepts None, Line
            kms_key_id: "KmsKeyId",
          },
          transform_resources: { # required
            instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
            instance_count: 1, # required
            volume_kms_key_id: "KmsKeyId",
          },
        },
      },
    ],
  },
  certify_for_marketplace: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.algorithm_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :algorithm_name (required, String) —

    The name of the algorithm.

  • :algorithm_description (String) —

    A description of the algorithm.

  • :training_specification (required, Types::TrainingSpecification) —

    Specifies details about training jobs run by this algorithm, including the following:

    • The Amazon ECR path of the container and the version digest of the algorithm.

    • The hyperparameters that the algorithm supports.

    • The instance types that the algorithm supports for training.

    • Whether the algorithm supports distributed training.

    • The metrics that the algorithm emits to Amazon CloudWatch.

    • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

    • The input channels that the algorithm supports for training data. For example, an algorithm might support ‘train`, `validation`, and `test` channels.

  • :inference_specification (Types::InferenceSpecification) —

    Specifies details about inference jobs that the algorithm runs, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the algorithm supports for inference.

  • :validation_specification (Types::AlgorithmValidationSpecification) —

    Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm’s training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm’s inference code.

  • :certify_for_marketplace (Boolean) —

    Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1062

def create_algorithm(params = {}, options = {})
  req = build_request(:create_algorithm, params)
  req.send_request(options)
end

#create_app(params = {}) ⇒ Types::CreateAppResponse

Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

Examples:

Request syntax with placeholder values


resp = client.create_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName",
  space_name: "SpaceName",
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
  app_name: "AppName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  resource_spec: {
    sage_maker_image_arn: "ImageArn",
    sage_maker_image_version_arn: "ImageVersionArn",
    sage_maker_image_version_alias: "ImageVersionAlias",
    instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
    lifecycle_config_arn: "StudioLifecycleConfigArn",
  },
})

Response structure


resp.app_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (String) —

    The user profile name. If this value is not set, then ‘SpaceName` must be set.

  • :space_name (String) —

    The name of the space. If this value is not set, then ‘UserProfileName` must be set.

  • :app_type (required, String) —

    The type of app.

  • :app_name (required, String) —

    The name of the app.

  • :tags (Array<Types::Tag>) —

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • :resource_spec (Types::ResourceSpec) —

    The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

    <note markdown=“1”> The value of ‘InstanceType` passed as part of the `ResourceSpec` in the `CreateApp` call overrides the value passed as part of the `ResourceSpec` configured for the user profile or the domain. If `InstanceType` is not specified in any of those three `ResourceSpec` values for a `KernelGateway` app, the `CreateApp` call fails with a request validation error.

    </note>

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1141

def create_app(params = {}, options = {})
  req = build_request(:create_app, params)
  req.send_request(options)
end

#create_app_image_config(params = {}) ⇒ Types::CreateAppImageConfigResponse

Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.

Examples:

Request syntax with placeholder values


resp = client.create_app_image_config({
  app_image_config_name: "AppImageConfigName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  kernel_gateway_image_config: {
    kernel_specs: [ # required
      {
        name: "KernelName", # required
        display_name: "KernelDisplayName",
      },
    ],
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
  },
  jupyter_lab_app_image_config: {
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
    container_config: {
      container_arguments: ["NonEmptyString64"],
      container_entrypoint: ["NonEmptyString256"],
      container_environment_variables: {
        "NonEmptyString256" => "String256",
      },
    },
  },
  code_editor_app_image_config: {
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
    container_config: {
      container_arguments: ["NonEmptyString64"],
      container_entrypoint: ["NonEmptyString256"],
      container_environment_variables: {
        "NonEmptyString256" => "String256",
      },
    },
  },
})

Response structure


resp.app_image_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :app_image_config_name (required, String) —

    The name of the AppImageConfig. Must be unique to your account.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the AppImageConfig.

  • :kernel_gateway_image_config (Types::KernelGatewayImageConfig) —

    The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

  • :jupyter_lab_app_image_config (Types::JupyterLabAppImageConfig) —

    The ‘JupyterLabAppImageConfig`. You can only specify one image kernel in the `AppImageConfig` API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.

  • :code_editor_app_image_config (Types::CodeEditorAppImageConfig) —

    The ‘CodeEditorAppImageConfig`. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1240

def create_app_image_config(params = {}, options = {})
  req = build_request(:create_app_image_config, params)
  req.send_request(options)
end

#create_artifact(params = {}) ⇒ Types::CreateArtifactResponse

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see [Amazon SageMaker ML Lineage Tracking].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

Examples:

Request syntax with placeholder values


resp = client.create_artifact({
  artifact_name: "ExperimentEntityName",
  source: { # required
    source_uri: "SourceUri", # required
    source_types: [
      {
        source_id_type: "MD5Hash", # required, accepts MD5Hash, S3ETag, S3Version, Custom
        value: "String256", # required
      },
    ],
  },
  artifact_type: "String256", # required
  properties: {
    "StringParameterValue" => "ArtifactPropertyValue",
  },
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.artifact_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :artifact_name (String) —

    The name of the artifact. Must be unique to your account in an Amazon Web Services Region.

  • :source (required, Types::ArtifactSource) —

    The ID, ID type, and URI of the source.

  • :artifact_type (required, String) —

    The artifact type.

  • :properties (Hash<String,String>) —

    A list of properties to add to the artifact.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the artifact.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1316

def create_artifact(params = {}, options = {})
  req = build_request(:create_artifact, params)
  req.send_request(options)
end

#create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see

docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html][1

in the SageMaker developer guide.

<note markdown=“1”> We recommend using the new versions [CreateAutoMLJobV2] and [DescribeAutoMLJobV2], which offer backward compatibility.

`CreateAutoMLJobV2` can manage tabular problem types identical to

those of its previous version ‘CreateAutoMLJob`, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a `CreateAutoMLJob` to

‘CreateAutoMLJobV2` in [Migrate a CreateAutoMLJob to CreateAutoMLJobV2].

</note>

You can find the best-performing model after you run an AutoML job by calling [DescribeAutoMLJobV2] (recommended) or [DescribeAutoMLJob].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html [4]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2 [5]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html

Examples:

Request syntax with placeholder values


resp = client.create_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
  input_data_config: [ # required
    {
      data_source: {
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
        },
      },
      compression_type: "None", # accepts None, Gzip
      target_attribute_name: "TargetAttributeName", # required
      content_type: "ContentType",
      channel_type: "training", # accepts training, validation
      sample_weight_attribute_name: "SampleWeightAttributeName",
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
  },
  problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression
  auto_ml_job_objective: {
    metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro, AUC, RMSE, BalancedAccuracy, R2, Recall, RecallMacro, Precision, PrecisionMacro, MAE, MAPE, MASE, WAPE, AverageWeightedQuantileLoss
  },
  auto_ml_job_config: {
    completion_criteria: {
      max_candidates: 1,
      max_runtime_per_training_job_in_seconds: 1,
      max_auto_ml_job_runtime_in_seconds: 1,
    },
    security_config: {
      volume_kms_key_id: "KmsKeyId",
      enable_inter_container_traffic_encryption: false,
      vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
    },
    candidate_generation_config: {
      feature_specification_s3_uri: "S3Uri",
      algorithms_config: [
        {
          auto_ml_algorithms: ["xgboost"], # required, accepts xgboost, linear-learner, mlp, lightgbm, catboost, randomforest, extra-trees, nn-torch, fastai, cnn-qr, deepar, prophet, npts, arima, ets
        },
      ],
    },
    data_split_config: {
      validation_fraction: 1.0,
    },
    mode: "AUTO", # accepts AUTO, ENSEMBLING, HYPERPARAMETER_TUNING
  },
  role_arn: "RoleArn", # required
  generate_candidate_definitions_only: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  model_deploy_config: {
    auto_generate_endpoint_name: false,
    endpoint_name: "EndpointName",
  },
})

Response structure


resp.auto_ml_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1515

def create_auto_ml_job(params = {}, options = {})
  req = build_request(:create_auto_ml_job, params)
  req.send_request(options)
end

#create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see

docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html][1

in the SageMaker developer guide.

AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.

<note markdown=“1”> [CreateAutoMLJobV2] and [DescribeAutoMLJobV2] are new versions of [CreateAutoMLJob] and [DescribeAutoMLJob] which offer backward compatibility.

`CreateAutoMLJobV2` can manage tabular problem types identical to

those of its previous version ‘CreateAutoMLJob`, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a `CreateAutoMLJob` to

‘CreateAutoMLJobV2` in [Migrate a CreateAutoMLJob to CreateAutoMLJobV2].

</note>

For the list of available problem types supported by ‘CreateAutoMLJobV2`, see [AutoMLProblemTypeConfig].

You can find the best-performing model after you run an AutoML job V2 by calling [DescribeAutoMLJobV2].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html [4]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html [5]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html [6]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2 [7]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html

Examples:

Request syntax with placeholder values


resp = client.create_auto_ml_job_v2({
  auto_ml_job_name: "AutoMLJobName", # required
  auto_ml_job_input_data_config: [ # required
    {
      channel_type: "training", # accepts training, validation
      content_type: "ContentType",
      compression_type: "None", # accepts None, Gzip
      data_source: {
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
        },
      },
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
  },
  auto_ml_problem_type_config: { # required
    image_classification_job_config: {
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
    },
    text_classification_job_config: {
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      content_column: "ContentColumn", # required
      target_label_column: "TargetLabelColumn", # required
    },
    time_series_forecasting_job_config: {
      feature_specification_s3_uri: "S3Uri",
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      forecast_frequency: "ForecastFrequency", # required
      forecast_horizon: 1, # required
      forecast_quantiles: ["ForecastQuantile"],
      transformations: {
        filling: {
          "TransformationAttributeName" => {
            "frontfill" => "FillingTransformationValue",
          },
        },
        aggregation: {
          "TransformationAttributeName" => "sum", # accepts sum, avg, first, min, max
        },
      },
      time_series_config: { # required
        target_attribute_name: "TargetAttributeName", # required
        timestamp_attribute_name: "TimestampAttributeName", # required
        item_identifier_attribute_name: "ItemIdentifierAttributeName", # required
        grouping_attribute_names: ["GroupingAttributeName"],
      },
      holiday_config: [
        {
          country_code: "CountryCode",
        },
      ],
      candidate_generation_config: {
        algorithms_config: [
          {
            auto_ml_algorithms: ["xgboost"], # required, accepts xgboost, linear-learner, mlp, lightgbm, catboost, randomforest, extra-trees, nn-torch, fastai, cnn-qr, deepar, prophet, npts, arima, ets
          },
        ],
      },
    },
    tabular_job_config: {
      candidate_generation_config: {
        algorithms_config: [
          {
            auto_ml_algorithms: ["xgboost"], # required, accepts xgboost, linear-learner, mlp, lightgbm, catboost, randomforest, extra-trees, nn-torch, fastai, cnn-qr, deepar, prophet, npts, arima, ets
          },
        ],
      },
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      feature_specification_s3_uri: "S3Uri",
      mode: "AUTO", # accepts AUTO, ENSEMBLING, HYPERPARAMETER_TUNING
      generate_candidate_definitions_only: false,
      problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression
      target_attribute_name: "TargetAttributeName", # required
      sample_weight_attribute_name: "SampleWeightAttributeName",
    },
    text_generation_job_config: {
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      base_model_name: "BaseModelName",
      text_generation_hyper_parameters: {
        "TextGenerationHyperParameterKey" => "TextGenerationHyperParameterValue",
      },
      model_access_config: {
        accept_eula: false, # required
      },
    },
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  security_config: {
    volume_kms_key_id: "KmsKeyId",
    enable_inter_container_traffic_encryption: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  auto_ml_job_objective: {
    metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro, AUC, RMSE, BalancedAccuracy, R2, Recall, RecallMacro, Precision, PrecisionMacro, MAE, MAPE, MASE, WAPE, AverageWeightedQuantileLoss
  },
  model_deploy_config: {
    auto_generate_endpoint_name: false,
    endpoint_name: "EndpointName",
  },
  data_split_config: {
    validation_fraction: 1.0,
  },
  auto_ml_compute_config: {
    emr_serverless_compute_config: {
      execution_role_arn: "RoleArn", # required
    },
  },
})

Response structure


resp.auto_ml_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

  • :auto_ml_job_input_data_config (required, Array<Types::AutoMLJobChannel>) —

    An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the

    InputDataConfig][1

    attribute in the ‘CreateAutoMLJob` input

    parameters. The supported formats depend on the problem type:

    • For tabular problem types: ‘S3Prefix`, `ManifestFile`.

    • For image classification: ‘S3Prefix`, `ManifestFile`, `AugmentedManifestFile`.

    • For text classification: ‘S3Prefix`.

    • For time-series forecasting: ‘S3Prefix`.

    • For text generation (LLMs fine-tuning): ‘S3Prefix`.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig

  • :output_data_config (required, Types::AutoMLOutputDataConfig) —

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

  • :auto_ml_problem_type_config (required, Types::AutoMLProblemTypeConfig) —

    Defines the configuration settings of one of the supported problem types.

  • :role_arn (required, String) —

    The ARN of the role that is used to access the data.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see [Tagging Amazon Web ServicesResources]. Tag keys must be unique per resource.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

  • :security_config (Types::AutoMLSecurityConfig) —

    The security configuration for traffic encryption or Amazon VPC settings.

  • :auto_ml_job_objective (Types::AutoMLJobObjective) —

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see [AutoMLJobObjective].

    <note markdown=“1”> * For tabular problem types: You must either provide both the

    `AutoMLJobObjective` and indicate the type of supervised learning
problem in `AutoMLProblemTypeConfig`
(`TabularJobConfig.ProblemType`), or none at all.

    • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the ‘AutoMLJobObjective` field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see [Metrics for fine-tuning LLMs in Autopilot].

    </note>

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html

  • :model_deploy_config (Types::ModelDeployConfig) —

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

  • :data_split_config (Types::AutoMLDataSplitConfig) —

    This structure specifies how to split the data into train and validation datasets.

    The validation and training datasets must contain the same headers. For jobs created by calling ‘CreateAutoMLJob`, the validation dataset must be less than 2 GB in size.

    <note markdown=“1”> This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

    </note>
  • :auto_ml_compute_config (Types::AutoMLComputeConfig) —

    Specifies the compute configuration for the AutoML job V2.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1833

def create_auto_ml_job_v2(params = {}, options = {})
  req = build_request(:create_auto_ml_job_v2, params)
  req.send_request(options)
end

#create_cluster(params = {}) ⇒ Types::CreateClusterResponse

Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see [Amazon SageMaker HyperPod] in the *Amazon SageMaker Developer Guide*.

[1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod.html

Examples:

Request syntax with placeholder values


resp = client.create_cluster({
  cluster_name: "ClusterName", # required
  instance_groups: [ # required
    {
      instance_count: 1, # required
      instance_group_name: "ClusterInstanceGroupName", # required
      instance_type: "ml.p4d.24xlarge", # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge
      life_cycle_config: { # required
        source_s3_uri: "S3Uri", # required
        on_create: "ClusterLifeCycleConfigFileName", # required
      },
      execution_role: "IAMRoleArn", # required
      threads_per_core: 1,
      instance_storage_configs: [
        {
          ebs_volume_config: {
            volume_size_in_gb: 1, # required
          },
        },
      ],
    },
  ],
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.cluster_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The name for the new SageMaker HyperPod cluster.

  • :instance_groups (required, Array<Types::ClusterInstanceGroupSpecification>) —

    The instance groups to be created in the SageMaker HyperPod cluster.

  • :vpc_config (Types::VpcConfig) —

    Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see [Give SageMaker Access to Resources in your Amazon VPC].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/infrastructure-give-access.html

  • :tags (Array<Types::Tag>) —

    Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see [Tagging Amazon Web Services Resources User Guide].

    [1]: docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1924

def create_cluster(params = {}, options = {})
  req = build_request(:create_cluster, params)
  req.send_request(options)
end

#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput

Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

The repository can be hosted either in [Amazon Web Services CodeCommit] or in any other Git repository.

[1]: docs.aws.amazon.com/codecommit/latest/userguide/welcome.html

Examples:

Request syntax with placeholder values


resp = client.create_code_repository({
  code_repository_name: "EntityName", # required
  git_config: { # required
    repository_url: "GitConfigUrl", # required
    branch: "Branch",
    secret_arn: "SecretArn",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.code_repository_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :code_repository_name (required, String) —

    The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

  • :git_config (required, Types::GitConfig) —

    Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 1992

def create_code_repository(params = {}, options = {})
  req = build_request(:create_code_repository, params)
  req.send_request(options)
end

#create_compilation_job(params = {}) ⇒ Types::CreateCompilationJobResponse

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

  • A name for the compilation job

  • Information about the input model artifacts

  • The output location for the compiled model and the device (target) that the model runs on

  • The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.

You can also provide a ‘Tag` to track the model compilation job’s resource use and costs. The response body contains the ‘CompilationJobArn` for the compiled job.

To stop a model compilation job, use [StopCompilationJob]. To get information about a particular model compilation job, use [DescribeCompilationJob]. To get information about multiple model compilation jobs, use [ListCompilationJobs].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_StopCompilationJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeCompilationJob.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListCompilationJobs.html

Examples:

Request syntax with placeholder values


resp = client.create_compilation_job({
  compilation_job_name: "EntityName", # required
  role_arn: "RoleArn", # required
  model_package_version_arn: "ModelPackageArn",
  input_config: {
    s3_uri: "S3Uri", # required
    data_input_config: "DataInputConfig",
    framework: "TENSORFLOW", # required, accepts TENSORFLOW, KERAS, MXNET, ONNX, PYTORCH, XGBOOST, TFLITE, DARKNET, SKLEARN
    framework_version: "FrameworkVersion",
  },
  output_config: { # required
    s3_output_location: "S3Uri", # required
    target_device: "lambda", # accepts lambda, ml_m4, ml_m5, ml_m6g, ml_c4, ml_c5, ml_c6g, ml_p2, ml_p3, ml_g4dn, ml_inf1, ml_inf2, ml_trn1, ml_eia2, jetson_tx1, jetson_tx2, jetson_nano, jetson_xavier, rasp3b, rasp4b, imx8qm, deeplens, rk3399, rk3288, aisage, sbe_c, qcs605, qcs603, sitara_am57x, amba_cv2, amba_cv22, amba_cv25, x86_win32, x86_win64, coreml, jacinto_tda4vm, imx8mplus
    target_platform: {
      os: "ANDROID", # required, accepts ANDROID, LINUX
      arch: "X86_64", # required, accepts X86_64, X86, ARM64, ARM_EABI, ARM_EABIHF
      accelerator: "INTEL_GRAPHICS", # accepts INTEL_GRAPHICS, MALI, NVIDIA, NNA
    },
    compiler_options: "CompilerOptions",
    kms_key_id: "KmsKeyId",
  },
  vpc_config: {
    security_group_ids: ["NeoVpcSecurityGroupId"], # required
    subnets: ["NeoVpcSubnetId"], # required
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
    max_pending_time_in_seconds: 1,
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.compilation_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :compilation_job_name (required, String) —

    A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

    During model compilation, Amazon SageMaker needs your permission to:

    • Read input data from an S3 bucket

    • Write model artifacts to an S3 bucket

    • Write logs to Amazon CloudWatch Logs

    • Publish metrics to Amazon CloudWatch

    You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the ‘iam:PassRole` permission. For more information, see [Amazon SageMaker Roles.]

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

  • :model_package_version_arn (String) —

    The Amazon Resource Name (ARN) of a versioned model package. Provide either a ‘ModelPackageVersionArn` or an `InputConfig` object in the request syntax. The presence of both objects in the `CreateCompilationJob` request will return an exception.

  • :input_config (Types::InputConfig) —

    Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

  • :output_config (required, Types::OutputConfig) —

    Provides information about the output location for the compiled model and the target device the model runs on.

  • :vpc_config (Types::NeoVpcConfig) —

    A [VpcConfig] object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see [Protect Compilation Jobs by Using an Amazon Virtual Private Cloud].

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html

  • :stopping_condition (required, Types::StoppingCondition) —

    Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 2154

def create_compilation_job(params = {}, options = {})
  req = build_request(:create_compilation_job, params)
  req.send_request(options)
end

#create_context(params = {}) ⇒ Types::CreateContextResponse

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see [Amazon SageMaker ML Lineage Tracking].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

Examples:

Request syntax with placeholder values


resp = client.create_context({
  context_name: "ContextName", # required
  source: { # required
    source_uri: "SourceUri", # required
    source_type: "String256",
    source_id: "String256",
  },
  context_type: "String256", # required
  description: "ExperimentDescription",
  properties: {
    "StringParameterValue" => "StringParameterValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.context_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :context_name (required, String) —

    The name of the context. Must be unique to your account in an Amazon Web Services Region.

  • :source (required, Types::ContextSource) —

    The source type, ID, and URI.

  • :context_type (required, String) —

    The context type.

  • :description (String) —

    The description of the context.

  • :properties (Hash<String,String>) —

    A list of properties to add to the context.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the context.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 2221

def create_context(params = {}, options = {})
  req = build_request(:create_context, params)
  req.send_request(options)
end

#create_data_quality_job_definition(params = {}) ⇒ Types::CreateDataQualityJobDefinitionResponse

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see [Amazon SageMaker Model Monitor].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html

Examples:

Request syntax with placeholder values


resp = client.create_data_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
  data_quality_baseline_config: {
    baselining_job_name: "ProcessingJobName",
    constraints_resource: {
      s3_uri: "S3Uri",
    },
    statistics_resource: {
      s3_uri: "S3Uri",
    },
  },
  data_quality_app_specification: { # required
    image_uri: "ImageUri", # required
    container_entrypoint: ["ContainerEntrypointString"],
    container_arguments: ["ContainerArgument"],
    record_preprocessor_source_uri: "S3Uri",
    post_analytics_processor_source_uri: "S3Uri",
    environment: {
      "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
    },
  },
  data_quality_job_input: { # required
    endpoint_input: {
      endpoint_name: "EndpointName", # required
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    batch_transform_input: {
      data_captured_destination_s3_uri: "DestinationS3Uri", # required
      dataset_format: { # required
        csv: {
          header: false,
        },
        json: {
          line: false,
        },
        parquet: {
        },
      },
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
  },
  data_quality_job_output_config: { # required
    monitoring_outputs: [ # required
      {
        s3_output: { # required
          s3_uri: "MonitoringS3Uri", # required
          local_path: "ProcessingLocalPath", # required
          s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
        },
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  job_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.job_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 2386

def create_data_quality_job_definition(params = {}, options = {})
  req = build_request(:create_data_quality_job_definition, params)
  req.send_request(options)
end

#create_device_fleet(params = {}) ⇒ Struct

Creates a device fleet.

Examples:

Request syntax with placeholder values


resp = client.create_device_fleet({
  device_fleet_name: "EntityName", # required
  role_arn: "RoleArn",
  description: "DeviceFleetDescription",
  output_config: { # required
    s3_output_location: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component
    preset_deployment_config: "String",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  enable_iot_role_alias: false,
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet that the device belongs to.

  • :role_arn (String) —

    The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).

  • :description (String) —

    A description of the fleet.

  • :output_config (required, Types::EdgeOutputConfig) —

    The output configuration for storing sample data collected by the fleet.

  • :tags (Array<Types::Tag>) —

    Creates tags for the specified fleet.

  • :enable_iot_role_alias (Boolean) —

    Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: “SageMakerEdge-{DeviceFleetName\}”.

    For example, if your device fleet is called “demo-fleet”, the name of the role alias will be “SageMakerEdge-demo-fleet”.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 2445

def create_device_fleet(params = {}, options = {})
  req = build_request(:create_device_fleet, params)
  req.send_request(options)
end

#create_domain(params = {}) ⇒ Types::CreateDomainResponse

Creates a ‘Domain`. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.

**EFS storage**

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see [Protect Data at Rest Using Encryption].

**VPC configuration**

All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the ‘AppNetworkAccessType` parameter. `AppNetworkAccessType` corresponds to the network access type that you choose when you onboard to the domain. The following options are available:

  • ‘PublicInternetOnly` - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.

  • ‘VpcOnly` - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.

    When internet access is disabled, you won’t be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.

NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.

For more information, see [Connect Amazon SageMaker Studio Notebooks to Resources in a VPC].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/studio-notebooks-and-internet-access.html

Examples:

Request syntax with placeholder values


resp = client.create_domain({
  domain_name: "DomainName", # required
  auth_mode: "SSO", # required, accepts SSO, IAM
  default_user_settings: { # required
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    sharing_settings: {
      notebook_output_option: "Allowed", # accepts Allowed, Disabled
      s3_output_path: "S3Uri",
      s3_kms_key_id: "KmsKeyId",
    },
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    r_studio_server_pro_app_settings: {
      access_status: "ENABLED", # accepts ENABLED, DISABLED
      user_group: "R_STUDIO_ADMIN", # accepts R_STUDIO_ADMIN, R_STUDIO_USER
    },
    r_session_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
    },
    canvas_app_settings: {
      time_series_forecasting_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        amazon_forecast_role_arn: "RoleArn",
      },
      model_register_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        cross_account_model_register_role_arn: "RoleArn",
      },
      workspace_settings: {
        s3_artifact_path: "S3Uri",
        s3_kms_key_id: "KmsKeyId",
      },
      identity_provider_o_auth_settings: [
        {
          data_source_name: "SalesforceGenie", # accepts SalesforceGenie, Snowflake
          status: "ENABLED", # accepts ENABLED, DISABLED
          secret_arn: "SecretArn",
        },
      ],
      direct_deploy_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      kendra_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      generative_ai_settings: {
        amazon_bedrock_role_arn: "RoleArn",
      },
      emr_serverless_settings: {
        execution_role_arn: "RoleArn",
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    default_landing_uri: "LandingUri",
    studio_web_portal: "ENABLED", # accepts ENABLED, DISABLED
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
      },
    ],
    studio_web_portal_settings: {
      hidden_ml_tools: ["DataWrangler"], # accepts DataWrangler, FeatureStore, EmrClusters, AutoMl, Experiments, Training, ModelEvaluation, Pipelines, Models, JumpStart, InferenceRecommender, Endpoints, Projects, InferenceOptimization
      hidden_app_types: ["JupyterServer"], # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
    },
    auto_mount_home_efs: "Enabled", # accepts Enabled, Disabled, DefaultAsDomain
  },
  domain_settings: {
    security_group_ids: ["SecurityGroupId"],
    r_studio_server_pro_domain_settings: {
      domain_execution_role_arn: "RoleArn", # required
      r_studio_connect_url: "String",
      r_studio_package_manager_url: "String",
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    execution_role_identity_config: "USER_PROFILE_NAME", # accepts USER_PROFILE_NAME, DISABLED
    docker_settings: {
      enable_docker_access: "ENABLED", # accepts ENABLED, DISABLED
      vpc_only_trusted_accounts: ["AccountId"],
    },
    amazon_q_settings: {
      status: "ENABLED", # accepts ENABLED, DISABLED
      q_profile_arn: "QProfileArn",
    },
  },
  subnet_ids: ["SubnetId"], # required
  vpc_id: "VpcId", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  app_network_access_type: "PublicInternetOnly", # accepts PublicInternetOnly, VpcOnly
  home_efs_file_system_kms_key_id: "KmsKeyId",
  kms_key_id: "KmsKeyId",
  app_security_group_management: "Service", # accepts Service, Customer
  default_space_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
      },
    ],
  },
})

Response structure


resp.domain_arn #=> String
resp.url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_name (required, String) —

    A name for the domain.

  • :auth_mode (required, String) —

    The mode of authentication that members use to access the domain.

  • :default_user_settings (required, Types::UserSettings) —

    The default settings to use to create a user profile when ‘UserSettings` isn’t specified in the call to the ‘CreateUserProfile` API.

    ‘SecurityGroups` is aggregated when specified in both calls. For all other settings in `UserSettings`, the values specified in `CreateUserProfile` take precedence over those specified in `CreateDomain`.

  • :domain_settings (Types::DomainSettings) —

    A collection of ‘Domain` settings.

  • :subnet_ids (required, Array<String>) —

    The VPC subnets that the domain uses for communication.

  • :vpc_id (required, String) —

    The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

  • :tags (Array<Types::Tag>) —

    Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the ‘Search` API.

    Tags that you specify for the Domain are also added to all Apps that the Domain launches.

  • :app_network_access_type (String) —

    Specifies the VPC used for non-EFS traffic. The default value is ‘PublicInternetOnly`.

    • ‘PublicInternetOnly` - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access

    • ‘VpcOnly` - All traffic is through the specified VPC and subnets

  • :home_efs_file_system_kms_key_id (String) —

    Use ‘KmsKeyId`.

  • :kms_key_id (String) —

    SageMaker uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.

  • :app_security_group_management (String) —

    The entity that creates and manages the required security groups for inter-app communication in ‘VPCOnly` mode. Required when `CreateDomain.AppNetworkAccessType` is `VPCOnly` and `DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn` is provided. If setting up the domain for use with RStudio, this value must be set to `Service`.

  • :default_space_settings (Types::DefaultSpaceSettings) —

    The default settings used to create a space.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 2897

def create_domain(params = {}, options = {})
  req = build_request(:create_domain, params)
  req.send_request(options)
end

#create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse

Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.

Examples:

Request syntax with placeholder values


resp = client.create_edge_deployment_plan({
  edge_deployment_plan_name: "EntityName", # required
  model_configs: [ # required
    {
      model_handle: "EntityName", # required
      edge_packaging_job_name: "EntityName", # required
    },
  ],
  device_fleet_name: "EntityName", # required
  stages: [
    {
      stage_name: "EntityName", # required
      device_selection_config: { # required
        device_subset_type: "PERCENTAGE", # required, accepts PERCENTAGE, SELECTION, NAMECONTAINS
        percentage: 1,
        device_names: ["DeviceName"],
        device_name_contains: "DeviceName",
      },
      deployment_config: {
        failure_handling_policy: "ROLLBACK_ON_FAILURE", # required, accepts ROLLBACK_ON_FAILURE, DO_NOTHING
      },
    },
  ],
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.edge_deployment_plan_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan.

  • :model_configs (required, Array<Types::EdgeDeploymentModelConfig>) —

    List of models associated with the edge deployment plan.

  • :device_fleet_name (required, String) —

    The device fleet used for this edge deployment plan.

  • :stages (Array<Types::DeploymentStage>) —

    List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.

  • :tags (Array<Types::Tag>) —

    List of tags with which to tag the edge deployment plan.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 2966

def create_edge_deployment_plan(params = {}, options = {})
  req = build_request(:create_edge_deployment_plan, params)
  req.send_request(options)
end

#create_edge_deployment_stage(params = {}) ⇒ Struct

Creates a new stage in an existing edge deployment plan.

Examples:

Request syntax with placeholder values


resp = client.create_edge_deployment_stage({
  edge_deployment_plan_name: "EntityName", # required
  stages: [ # required
    {
      stage_name: "EntityName", # required
      device_selection_config: { # required
        device_subset_type: "PERCENTAGE", # required, accepts PERCENTAGE, SELECTION, NAMECONTAINS
        percentage: 1,
        device_names: ["DeviceName"],
        device_name_contains: "DeviceName",
      },
      deployment_config: {
        failure_handling_policy: "ROLLBACK_ON_FAILURE", # required, accepts ROLLBACK_ON_FAILURE, DO_NOTHING
      },
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan.

  • :stages (required, Array<Types::DeploymentStage>) —

    List of stages to be added to the edge deployment plan.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3005

def create_edge_deployment_stage(params = {}, options = {})
  req = build_request(:create_edge_deployment_stage, params)
  req.send_request(options)
end

#create_edge_packaging_job(params = {}) ⇒ Struct

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

Examples:

Request syntax with placeholder values


resp = client.create_edge_packaging_job({
  edge_packaging_job_name: "EntityName", # required
  compilation_job_name: "EntityName", # required
  model_name: "EntityName", # required
  model_version: "EdgeVersion", # required
  role_arn: "RoleArn", # required
  output_config: { # required
    s3_output_location: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component
    preset_deployment_config: "String",
  },
  resource_key: "KmsKeyId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_packaging_job_name (required, String) —

    The name of the edge packaging job.

  • :compilation_job_name (required, String) —

    The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.

  • :model_name (required, String) —

    The name of the model.

  • :model_version (required, String) —

    The version of the model.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.

  • :output_config (required, Types::EdgeOutputConfig) —

    Provides information about the output location for the packaged model.

  • :resource_key (String) —

    The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.

  • :tags (Array<Types::Tag>) —

    Creates tags for the packaging job.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3072

def create_edge_packaging_job(params = {}, options = {})
  req = build_request(:create_edge_packaging_job, params)
  req.send_request(options)
end

#create_endpoint(params = {}) ⇒ Types::CreateEndpointOutput

Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the

CreateEndpointConfig][1

API.

Use this API to deploy models using SageMaker hosting services.

<note markdown=“1”> You must not delete an ‘EndpointConfig` that is in use by an endpoint that is live or while the `UpdateEndpoint` or `CreateEndpoint` operations are being performed on the endpoint. To update an endpoint, you must create a new `EndpointConfig`.

</note>

The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.

When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

<note markdown=“1”> When you call [CreateEndpoint], a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting [ ‘Eventually Consistent Reads` ][3], the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call

DescribeEndpointConfig][4

before calling [CreateEndpoint] to

minimize the potential impact of a DynamoDB eventually consistent read.

</note>

When SageMaker receives the request, it sets the endpoint status to ‘Creating`. After it creates the endpoint, it sets the status to `InService`. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the

DescribeEndpoint][5

API.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see [Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region] in the *Amazon Web Services Identity and Access Management User Guide*.

<note markdown=“1”> To add the IAM role policies for using this API operation, go to the [IAM console], and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the

CreateEndpoint][2

and [CreateEndpointConfig] API operations, add

the following policies to the role.

* Option 1: For a full SageMaker access, search and attach the
 `AmazonSageMakerFullAccess` policy.

  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

    ‘“Action”: [“sagemaker:CreateEndpoint”, “sagemaker:CreateEndpointConfig”]`

    ‘“Resource”: [`

    ‘“arn:aws:sagemaker:region:account-id:endpoint/endpointName”`

    ‘“arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName”`

    ‘]`

    For more information, see [SageMaker API Permissions: Actions, Permissions, and Resources Reference].

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html [3]: docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html [4]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpointConfig.html [5]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpoint.html [6]: docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html [7]: console.aws.amazon.com/iam/ [8]: docs.aws.amazon.com/sagemaker/latest/dg/api-permissions-reference.html

Examples:

Request syntax with placeholder values


resp = client.create_endpoint({
  endpoint_name: "EndpointName", # required
  endpoint_config_name: "EndpointConfigName", # required
  deployment_config: {
    blue_green_update_policy: {
      traffic_routing_configuration: { # required
        type: "ALL_AT_ONCE", # required, accepts ALL_AT_ONCE, CANARY, LINEAR
        wait_interval_in_seconds: 1, # required
        canary_size: {
          type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
          value: 1, # required
        },
        linear_step_size: {
          type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
          value: 1, # required
        },
      },
      termination_wait_in_seconds: 1,
      maximum_execution_timeout_in_seconds: 1,
    },
    rolling_update_policy: {
      maximum_batch_size: { # required
        type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
        value: 1, # required
      },
      wait_interval_in_seconds: 1, # required
      maximum_execution_timeout_in_seconds: 1,
      rollback_maximum_batch_size: {
        type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
        value: 1, # required
      },
    },
    auto_rollback_configuration: {
      alarms: [
        {
          alarm_name: "AlarmName",
        },
      ],
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.endpoint_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3263

def create_endpoint(params = {}, options = {})
  req = build_request(:create_endpoint, params)
  req.send_request(options)
end

#create_endpoint_config(params = {}) ⇒ Types::CreateEndpointConfigOutput

Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the ‘CreateModel` API, to deploy and the resources that you want SageMaker to provision. Then you call the

CreateEndpoint][1

API.

<note markdown=“1”> Use this API if you want to use SageMaker hosting services to deploy models into production.

</note>

In the request, you define a ‘ProductionVariant`, for each model that you want to deploy. Each `ProductionVariant` parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a ‘VariantWeight` to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

<note markdown=“1”> When you call [CreateEndpoint], a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting [ ‘Eventually Consistent Reads` ][2], the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call

DescribeEndpointConfig][3

before calling [CreateEndpoint] to

minimize the potential impact of a DynamoDB eventually consistent read.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html [2]: docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpointConfig.html

Examples:

Request syntax with placeholder values


resp = client.create_endpoint_config({
  endpoint_config_name: "EndpointConfigName", # required
  production_variants: [ # required
    {
      variant_name: "VariantName", # required
      model_name: "ModelName",
      initial_instance_count: 1,
      instance_type: "ml.t2.medium", # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
      initial_variant_weight: 1.0,
      accelerator_type: "ml.eia1.medium", # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
      core_dump_config: {
        destination_s3_uri: "DestinationS3Uri", # required
        kms_key_id: "KmsKeyId",
      },
      serverless_config: {
        memory_size_in_mb: 1, # required
        max_concurrency: 1, # required
        provisioned_concurrency: 1,
      },
      volume_size_in_gb: 1,
      model_data_download_timeout_in_seconds: 1,
      container_startup_health_check_timeout_in_seconds: 1,
      enable_ssm_access: false,
      managed_instance_scaling: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        min_instance_count: 1,
        max_instance_count: 1,
      },
      routing_config: {
        routing_strategy: "LEAST_OUTSTANDING_REQUESTS", # required, accepts LEAST_OUTSTANDING_REQUESTS, RANDOM
      },
      inference_ami_version: "al2-ami-sagemaker-inference-gpu-2", # accepts al2-ami-sagemaker-inference-gpu-2
    },
  ],
  data_capture_config: {
    enable_capture: false,
    initial_sampling_percentage: 1, # required
    destination_s3_uri: "DestinationS3Uri", # required
    kms_key_id: "KmsKeyId",
    capture_options: [ # required
      {
        capture_mode: "Input", # required, accepts Input, Output, InputAndOutput
      },
    ],
    capture_content_type_header: {
      csv_content_types: ["CsvContentType"],
      json_content_types: ["JsonContentType"],
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  kms_key_id: "KmsKeyId",
  async_inference_config: {
    client_config: {
      max_concurrent_invocations_per_instance: 1,
    },
    output_config: { # required
      kms_key_id: "KmsKeyId",
      s3_output_path: "DestinationS3Uri",
      notification_config: {
        success_topic: "SnsTopicArn",
        error_topic: "SnsTopicArn",
        include_inference_response_in: ["SUCCESS_NOTIFICATION_TOPIC"], # accepts SUCCESS_NOTIFICATION_TOPIC, ERROR_NOTIFICATION_TOPIC
      },
      s3_failure_path: "DestinationS3Uri",
    },
  },
  explainer_config: {
    clarify_explainer_config: {
      enable_explanations: "ClarifyEnableExplanations",
      inference_config: {
        features_attribute: "ClarifyFeaturesAttribute",
        content_template: "ClarifyContentTemplate",
        max_record_count: 1,
        max_payload_in_mb: 1,
        probability_index: 1,
        label_index: 1,
        probability_attribute: "ClarifyProbabilityAttribute",
        label_attribute: "ClarifyLabelAttribute",
        label_headers: ["ClarifyHeader"],
        feature_headers: ["ClarifyHeader"],
        feature_types: ["numerical"], # accepts numerical, categorical, text
      },
      shap_config: { # required
        shap_baseline_config: { # required
          mime_type: "ClarifyMimeType",
          shap_baseline: "ClarifyShapBaseline",
          shap_baseline_uri: "Url",
        },
        number_of_samples: 1,
        use_logit: false,
        seed: 1,
        text_config: {
          language: "af", # required, accepts af, sq, ar, hy, eu, bn, bg, ca, zh, hr, cs, da, nl, en, et, fi, fr, de, el, gu, he, hi, hu, is, id, ga, it, kn, ky, lv, lt, lb, mk, ml, mr, ne, nb, fa, pl, pt, ro, ru, sa, sr, tn, si, sk, sl, es, sv, tl, ta, tt, te, tr, uk, ur, yo, lij, xx
          granularity: "token", # required, accepts token, sentence, paragraph
        },
      },
    },
  },
  shadow_production_variants: [
    {
      variant_name: "VariantName", # required
      model_name: "ModelName",
      initial_instance_count: 1,
      instance_type: "ml.t2.medium", # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
      initial_variant_weight: 1.0,
      accelerator_type: "ml.eia1.medium", # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
      core_dump_config: {
        destination_s3_uri: "DestinationS3Uri", # required
        kms_key_id: "KmsKeyId",
      },
      serverless_config: {
        memory_size_in_mb: 1, # required
        max_concurrency: 1, # required
        provisioned_concurrency: 1,
      },
      volume_size_in_gb: 1,
      model_data_download_timeout_in_seconds: 1,
      container_startup_health_check_timeout_in_seconds: 1,
      enable_ssm_access: false,
      managed_instance_scaling: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        min_instance_count: 1,
        max_instance_count: 1,
      },
      routing_config: {
        routing_strategy: "LEAST_OUTSTANDING_REQUESTS", # required, accepts LEAST_OUTSTANDING_REQUESTS, RANDOM
      },
      inference_ami_version: "al2-ami-sagemaker-inference-gpu-2", # accepts al2-ami-sagemaker-inference-gpu-2
    },
  ],
  execution_role_arn: "RoleArn",
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  enable_network_isolation: false,
})

Response structure


resp.endpoint_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_config_name (required, String) —

    The name of the endpoint configuration. You specify this name in a

    CreateEndpoint][1

    request.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html

  • :production_variants (required, Array<Types::ProductionVariant>) —

    An array of ‘ProductionVariant` objects, one for each model that you want to host at this endpoint.

  • :data_capture_config (Types::DataCaptureConfig) —

    Configuration to control how SageMaker captures inference data.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

  • :kms_key_id (String) —

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

    The KmsKeyId can be any of the following formats:

    • Key ID: ‘1234abcd-12ab-34cd-56ef-1234567890ab`

    • Key ARN: ‘arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab`

    • Alias name: ‘alias/ExampleAlias`

    • Alias name ARN: ‘arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias`

    The KMS key policy must grant permission to the IAM role that you specify in your ‘CreateEndpoint`, `UpdateEndpoint` requests. For more information, refer to the Amazon Web Services Key Management Service section[ Using Key Policies in Amazon Web Services KMS ][1]

    <note markdown=“1”> Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can’t request a ‘KmsKeyId` when using an instance type with local storage. If any of the models that you specify in the `ProductionVariants` parameter use nitro-based instances with local storage, do not specify a value for the `KmsKeyId` parameter. If you specify a value for `KmsKeyId` when using any nitro-based instances with local storage, the call to `CreateEndpointConfig` fails.

    For a list of instance types that support local instance storage, see

    [Instance Store Volumes].

    For more information about local instance storage encryption, see [SSD

    Instance Store Volumes].

    </note>

    [1]: docs.aws.amazon.com/kms/latest/developerguide/key-policies.html [2]: docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes [3]: docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html

  • :async_inference_config (Types::AsyncInferenceConfig) —

    Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using [InvokeEndpointAsync].

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpointAsync.html

  • :explainer_config (Types::ExplainerConfig) —

    A member of ‘CreateEndpointConfig` that enables explainers.

  • :shadow_production_variants (Array<Types::ProductionVariant>) —

    An array of ‘ProductionVariant` objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on `ProductionVariants`. If you use this field, you can only specify one variant for `ProductionVariants` and one variant for `ShadowProductionVariants`.

  • :execution_role_arn (String) —

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see [SageMaker Roles].

    <note markdown=“1”> To be able to pass this role to Amazon SageMaker, the caller of this action must have the ‘iam:PassRole` permission.

    </note>

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

  • :vpc_config (Types::VpcConfig) —

    Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see [Give SageMaker Access to Resources in your Amazon VPC].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/infrastructure-give-access.html

  • :enable_network_isolation (Boolean) —

    Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3589

def create_endpoint_config(params = {}, options = {})
  req = build_request(:create_endpoint_config, params)
  req.send_request(options)
end

#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse

Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called *trial components*, that produce a machine learning model.

<note markdown=“1”> In the Studio UI, trials are referred to as *run groups* and trial components are referred to as runs.

</note>

The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to experiments, trials, trial components and then use the [Search] API to search for the tags.

To add a description to an experiment, specify the optional ‘Description` parameter. To add a description later, or to change the description, call the [UpdateExperiment] API.

To get a list of all your experiments, call the [ListExperiments] API. To view an experiment’s properties, call the

DescribeExperiment][4

API. To get a list of all the trials

associated with an experiment, call the [ListTrials] API. To create a trial call the [CreateTrial] API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateExperiment.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListExperiments.html [4]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeExperiment.html [5]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListTrials.html [6]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrial.html

Examples:

Request syntax with placeholder values


resp = client.create_experiment({
  experiment_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  description: "ExperimentDescription",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • :display_name (String) —

    The name of the experiment as displayed. The name doesn’t need to be unique. If you don’t specify ‘DisplayName`, the value in `ExperimentName` is displayed.

  • :description (String) —

    The description of the experiment.

  • :tags (Array<Types::Tag>) —

    A list of tags to associate with the experiment. You can use

    Search][1

    API to search on the tags.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3682

def create_experiment(params = {}, options = {})
  req = build_request(:create_experiment, params)
  req.send_request(options)
end

#create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse

Create a new ‘FeatureGroup`. A `FeatureGroup` is a group of `Features` defined in the `FeatureStore` to describe a `Record`.

The ‘FeatureGroup` defines the schema and features contained in the `FeatureGroup`. A `FeatureGroup` definition is composed of a list of `Features`, a `RecordIdentifierFeatureName`, an `EventTimeFeatureName` and configurations for its `OnlineStore` and `OfflineStore`. Check

Amazon Web Services service quotas][1

to see the ‘FeatureGroup`s

quota for your Amazon Web Services account.

Note that it can take approximately 10-15 minutes to provision an ‘OnlineStore` `FeatureGroup` with the `InMemory` `StorageType`.

You must include at least one of ‘OnlineStoreConfig` and `OfflineStoreConfig` to create a `FeatureGroup`.

[1]: docs.aws.amazon.com/general/latest/gr/aws_service_limits.html

Examples:

Request syntax with placeholder values


resp = client.create_feature_group({
  feature_group_name: "FeatureGroupName", # required
  record_identifier_feature_name: "FeatureName", # required
  event_time_feature_name: "FeatureName", # required
  feature_definitions: [ # required
    {
      feature_name: "FeatureName", # required
      feature_type: "Integral", # required, accepts Integral, Fractional, String
      collection_type: "List", # accepts List, Set, Vector
      collection_config: {
        vector_config: {
          dimension: 1, # required
        },
      },
    },
  ],
  online_store_config: {
    security_config: {
      kms_key_id: "KmsKeyId",
    },
    enable_online_store: false,
    ttl_duration: {
      unit: "Seconds", # accepts Seconds, Minutes, Hours, Days, Weeks
      value: 1,
    },
    storage_type: "Standard", # accepts Standard, InMemory
  },
  offline_store_config: {
    s3_storage_config: { # required
      s3_uri: "S3Uri", # required
      kms_key_id: "KmsKeyId",
      resolved_output_s3_uri: "S3Uri",
    },
    disable_glue_table_creation: false,
    data_catalog_config: {
      table_name: "TableName", # required
      catalog: "Catalog", # required
      database: "Database", # required
    },
    table_format: "Default", # accepts Default, Glue, Iceberg
  },
  throughput_config: {
    throughput_mode: "OnDemand", # required, accepts OnDemand, Provisioned
    provisioned_read_capacity_units: 1,
    provisioned_write_capacity_units: 1,
  },
  role_arn: "RoleArn",
  description: "Description",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.feature_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :feature_group_name (required, String) —

    The name of the ‘FeatureGroup`. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

    The name:

    • Must start with an alphanumeric character.

    • Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.

  • :record_identifier_feature_name (required, String) —

    The name of the ‘Feature` whose value uniquely identifies a `Record` defined in the `FeatureStore`. Only the latest record per identifier value will be stored in the `OnlineStore`. `RecordIdentifierFeatureName` must be one of feature definitions’ names.

    You use the ‘RecordIdentifierFeatureName` to access data in a `FeatureStore`.

    This name:

    • Must start with an alphanumeric character.

    • Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.

  • :event_time_feature_name (required, String) —

    The name of the feature that stores the ‘EventTime` of a `Record` in a `FeatureGroup`.

    An ‘EventTime` is a point in time when a new event occurs that corresponds to the creation or update of a `Record` in a `FeatureGroup`. All `Records` in the `FeatureGroup` must have a corresponding `EventTime`.

    An ‘EventTime` can be a `String` or `Fractional`.

    • ‘Fractional`: `EventTime` feature values must be a Unix timestamp in seconds.

    • ‘String`: `EventTime` feature values must be an ISO-8601 string in the format. The following formats are supported `yyyy-MM-dd’T’HH:mm:ssZ` and ‘yyyy-MM-dd’T’HH:mm:ss.SSSZ` where ‘yyyy`, `MM`, and `dd` represent the year, month, and day respectively and `HH`, `mm`, `ss`, and if applicable, `SSS` represent the hour, month, second and milliseconds respsectively. `’T’‘ and `Z` are constants.

  • :feature_definitions (required, Array<Types::FeatureDefinition>) —

    A list of ‘Feature` names and types. `Name` and `Type` is compulsory per `Feature`.

    Valid feature ‘FeatureType`s are `Integral`, `Fractional` and `String`.

    ‘FeatureName`s cannot be any of the following: `is_deleted`, `write_time`, `api_invocation_time`

    You can create up to 2,500 ‘FeatureDefinition`s per `FeatureGroup`.

  • :online_store_config (Types::OnlineStoreConfig) —

    You can turn the ‘OnlineStore` on or off by specifying `True` for the `EnableOnlineStore` flag in `OnlineStoreConfig`.

    You can also include an Amazon Web Services KMS key ID (‘KMSKeyId`) for at-rest encryption of the `OnlineStore`.

    The default value is ‘False`.

  • :offline_store_config (Types::OfflineStoreConfig) —

    Use this to configure an ‘OfflineFeatureStore`. This parameter allows you to specify:

    • The Amazon Simple Storage Service (Amazon S3) location of an ‘OfflineStore`.

    • A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.

    • An KMS encryption key to encrypt the Amazon S3 location used for ‘OfflineStore`. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your [bucket-level key] for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.

    • Format for the offline store table. Supported formats are Glue (Default) and [Apache Iceberg].

    To learn more about this parameter, see [OfflineStoreConfig].

    [1]: docs.aws.amazon.com/AmazonS3/latest/userguide/bucket-key.html [2]: iceberg.apache.org/ [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_OfflineStoreConfig.html

  • :throughput_config (Types::ThroughputConfig) —

    Used to set feature group throughput configuration. There are two modes: ‘ON_DEMAND` and `PROVISIONED`. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.

    Note: ‘PROVISIONED` throughput mode is supported only for feature groups that are offline-only, or use the [ `Standard` ][1] tier online store.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_OnlineStoreConfig.html#sagemaker-Type-OnlineStoreConfig-StorageType

  • :role_arn (String) —

    The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the ‘OfflineStore` if an `OfflineStoreConfig` is provided.

  • :description (String) —

    A free-form description of a ‘FeatureGroup`.

  • :tags (Array<Types::Tag>) —

    Tags used to identify ‘Features` in each `FeatureGroup`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3907

def create_feature_group(params = {}, options = {})
  req = build_request(:create_feature_group, params)
  req.send_request(options)
end

#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse

Creates a flow definition.

Examples:

Request syntax with placeholder values


resp = client.create_flow_definition({
  flow_definition_name: "FlowDefinitionName", # required
  human_loop_request_source: {
    aws_managed_human_loop_request_source: "AWS/Rekognition/DetectModerationLabels/Image/V3", # required, accepts AWS/Rekognition/DetectModerationLabels/Image/V3, AWS/Textract/AnalyzeDocument/Forms/V1
  },
  human_loop_activation_config: {
    human_loop_activation_conditions_config: { # required
      human_loop_activation_conditions: "HumanLoopActivationConditions", # required
    },
  },
  human_loop_config: {
    workteam_arn: "WorkteamArn", # required
    human_task_ui_arn: "HumanTaskUiArn", # required
    task_title: "FlowDefinitionTaskTitle", # required
    task_description: "FlowDefinitionTaskDescription", # required
    task_count: 1, # required
    task_availability_lifetime_in_seconds: 1,
    task_time_limit_in_seconds: 1,
    task_keywords: ["FlowDefinitionTaskKeyword"],
    public_workforce_task_price: {
      amount_in_usd: {
        dollars: 1,
        cents: 1,
        tenth_fractions_of_a_cent: 1,
      },
    },
  },
  output_config: { # required
    s3_output_path: "S3Uri", # required
    kms_key_id: "KmsKeyId",
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.flow_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :flow_definition_name (required, String) —

    The name of your flow definition.

  • :human_loop_request_source (Types::HumanLoopRequestSource) —

    Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

  • :human_loop_activation_config (Types::HumanLoopActivationConfig) —

    An object containing information about the events that trigger a human workflow.

  • :human_loop_config (Types::HumanLoopConfig) —

    An object containing information about the tasks the human reviewers will perform.

  • :output_config (required, Types::FlowDefinitionOutputConfig) —

    An object containing information about where the human review results will be uploaded.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, ‘arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298`.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 3998

def create_flow_definition(params = {}, options = {})
  req = build_request(:create_flow_definition, params)
  req.send_request(options)
end

#create_hub(params = {}) ⇒ Types::CreateHubResponse

Create a hub.

Examples:

Request syntax with placeholder values


resp = client.create_hub({
  hub_name: "HubName", # required
  hub_description: "HubDescription", # required
  hub_display_name: "HubDisplayName",
  hub_search_keywords: ["HubSearchKeyword"],
  s3_storage_config: {
    s3_output_path: "S3OutputPath",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.hub_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to create.

  • :hub_description (required, String) —

    A description of the hub.

  • :hub_display_name (String) —

    The display name of the hub.

  • :hub_search_keywords (Array<String>) —

    The searchable keywords for the hub.

  • :s3_storage_config (Types::HubS3StorageConfig) —

    The Amazon S3 storage configuration for the hub.

  • :tags (Array<Types::Tag>) —

    Any tags to associate with the hub.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 4053

def create_hub(params = {}, options = {})
  req = build_request(:create_hub, params)
  req.send_request(options)
end

#create_hub_content_reference(params = {}) ⇒ Types::CreateHubContentReferenceResponse

Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.

Examples:

Request syntax with placeholder values


resp = client.create_hub_content_reference({
  hub_name: "HubNameOrArn", # required
  sage_maker_public_hub_content_arn: "SageMakerPublicHubContentArn", # required
  hub_content_name: "HubContentName",
  min_version: "HubContentVersion",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.hub_arn #=> String
resp.hub_content_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to add the hub content reference to.

  • :sage_maker_public_hub_content_arn (required, String) —

    The ARN of the public hub content to reference.

  • :hub_content_name (String) —

    The name of the hub content to reference.

  • :min_version (String) —

    The minimum version of the hub content to reference.

  • :tags (Array<Types::Tag>) —

    Any tags associated with the hub content to reference.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 4105

def create_hub_content_reference(params = {}, options = {})
  req = build_request(:create_hub_content_reference, params)
  req.send_request(options)
end

#create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse

Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.

Examples:

Request syntax with placeholder values


resp = client.create_human_task_ui({
  human_task_ui_name: "HumanTaskUiName", # required
  ui_template: { # required
    content: "TemplateContent", # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.human_task_ui_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :human_task_ui_name (required, String) —

    The name of the user interface you are creating.

  • :ui_template (required, Types::UiTemplate) —

    The Liquid template for the worker user interface.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 4152

def create_human_task_ui(params = {}, options = {})
  req = build_request(:create_human_task_ui, params)
  req.send_request(options)
end

#create_hyper_parameter_tuning_job(params = {}) ⇒ Types::CreateHyperParameterTuningJobResponse

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see [View Experiments, Trials, and Trial Components].

Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

[1]: docs.aws.amazon.com/sagemaker/latest/dg/experiments-view-compare.html#experiments-view

Examples:

Request syntax with placeholder values


resp = client.create_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
  hyper_parameter_tuning_job_config: { # required
    strategy: "Bayesian", # required, accepts Bayesian, Random, Hyperband, Grid
    strategy_config: {
      hyperband_strategy_config: {
        min_resource: 1,
        max_resource: 1,
      },
    },
    hyper_parameter_tuning_job_objective: {
      type: "Maximize", # required, accepts Maximize, Minimize
      metric_name: "MetricName", # required
    },
    resource_limits: { # required
      max_number_of_training_jobs: 1,
      max_parallel_training_jobs: 1, # required
      max_runtime_in_seconds: 1,
    },
    parameter_ranges: {
      integer_parameter_ranges: [
        {
          name: "ParameterKey", # required
          min_value: "ParameterValue", # required
          max_value: "ParameterValue", # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      continuous_parameter_ranges: [
        {
          name: "ParameterKey", # required
          min_value: "ParameterValue", # required
          max_value: "ParameterValue", # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      categorical_parameter_ranges: [
        {
          name: "ParameterKey", # required
          values: ["ParameterValue"], # required
        },
      ],
      auto_parameters: [
        {
          name: "ParameterKey", # required
          value_hint: "ParameterValue", # required
        },
      ],
    },
    training_job_early_stopping_type: "Off", # accepts Off, Auto
    tuning_job_completion_criteria: {
      target_objective_metric_value: 1.0,
      best_objective_not_improving: {
        max_number_of_training_jobs_not_improving: 1,
      },
      convergence_detected: {
        complete_on_convergence: "Disabled", # accepts Disabled, Enabled
      },
    },
    random_seed: 1,
  },
  training_job_definition: {
    definition_name: "HyperParameterTrainingJobDefinitionName",
    tuning_objective: {
      type: "Maximize", # required, accepts Maximize, Minimize
      metric_name: "MetricName", # required
    },
    hyper_parameter_ranges: {
      integer_parameter_ranges: [
        {
          name: "ParameterKey", # required
          min_value: "ParameterValue", # required
          max_value: "ParameterValue", # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      continuous_parameter_ranges: [
        {
          name: "ParameterKey", # required
          min_value: "ParameterValue", # required
          max_value: "ParameterValue", # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      categorical_parameter_ranges: [
        {
          name: "ParameterKey", # required
          values: ["ParameterValue"], # required
        },
      ],
      auto_parameters: [
        {
          name: "ParameterKey", # required
          value_hint: "ParameterValue", # required
        },
      ],
    },
    static_hyper_parameters: {
      "HyperParameterKey" => "HyperParameterValue",
    },
    algorithm_specification: { # required
      training_image: "AlgorithmImage",
      training_input_mode: "Pipe", # required, accepts Pipe, File, FastFile
      algorithm_name: "ArnOrName",
      metric_definitions: [
        {
          name: "MetricName", # required
          regex: "MetricRegex", # required
        },
      ],
    },
    role_arn: "RoleArn", # required
    input_data_config: [
      {
        channel_name: "ChannelName", # required
        data_source: { # required
          s3_data_source: {
            s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
            s3_uri: "S3Uri", # required
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
            attribute_names: ["AttributeName"],
            instance_group_names: ["InstanceGroupName"],
          },
          file_system_data_source: {
            file_system_id: "FileSystemId", # required
            file_system_access_mode: "rw", # required, accepts rw, ro
            file_system_type: "EFS", # required, accepts EFS, FSxLustre
            directory_path: "DirectoryPath", # required
          },
        },
        content_type: "ContentType",
        compression_type: "None", # accepts None, Gzip
        record_wrapper_type: "None", # accepts None, RecordIO
        input_mode: "Pipe", # accepts Pipe, File, FastFile
        shuffle_config: {
          seed: 1, # required
        },
      },
    ],
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
    output_data_config: { # required
      kms_key_id: "KmsKeyId",
      s3_output_path: "S3Uri", # required
      compression_type: "GZIP", # accepts GZIP, NONE
    },
    resource_config: {
      instance_type: "ml.m4.xlarge", # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
      instance_count: 1,
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
      keep_alive_period_in_seconds: 1,
      instance_groups: [
        {
          instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
          instance_count: 1, # required
          instance_group_name: "InstanceGroupName", # required
        },
      ],
    },
    hyper_parameter_tuning_resource_config: {
      instance_type: "ml.m4.xlarge", # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
      instance_count: 1,
      volume_size_in_gb: 1,
      volume_kms_key_id: "KmsKeyId",
      allocation_strategy: "Prioritized", # accepts Prioritized
      instance_configs: [
        {
          instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
          instance_count: 1, # required
          volume_size_in_gb: 1, # required
        },
      ],
    },
    stopping_condition: { # required
      max_runtime_in_seconds: 1,
      max_wait_time_in_seconds: 1,
      max_pending_time_in_seconds: 1,
    },
    enable_network_isolation: false,
    enable_inter_container_traffic_encryption: false,
    enable_managed_spot_training: false,
    checkpoint_config: {
      s3_uri: "S3Uri", # required
      local_path: "DirectoryPath",
    },
    retry_strategy: {
      maximum_retry_attempts: 1, # required
    },
    environment: {
      "HyperParameterTrainingJobEnvironmentKey" => "HyperParameterTrainingJobEnvironmentValue",
    },
  },
  training_job_definitions: [
    {
      definition_name: "HyperParameterTrainingJobDefinitionName",
      tuning_objective: {
        type: "Maximize", # required, accepts Maximize, Minimize
        metric_name: "MetricName", # required
      },
      hyper_parameter_ranges: {
        integer_parameter_ranges: [
          {
            name: "ParameterKey", # required
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
            scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
          },
        ],
        continuous_parameter_ranges: [
          {
            name: "ParameterKey", # required
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
            scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
          },
        ],
        categorical_parameter_ranges: [
          {
            name: "ParameterKey", # required
            values: ["ParameterValue"], # required
          },
        ],
        auto_parameters: [
          {
            name: "ParameterKey", # required
            value_hint: "ParameterValue", # required
          },
        ],
      },
      static_hyper_parameters: {
        "HyperParameterKey" => "HyperParameterValue",
      },
      algorithm_specification: { # required
        training_image: "AlgorithmImage",
        training_input_mode: "Pipe", # required, accepts Pipe, File, FastFile
        algorithm_name: "ArnOrName",
        metric_definitions: [
          {
            name: "MetricName", # required
            regex: "MetricRegex", # required
          },
        ],
      },
      role_arn: "RoleArn", # required
      input_data_config: [
        {
          channel_name: "ChannelName", # required
          data_source: { # required
            s3_data_source: {
              s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
              s3_uri: "S3Uri", # required
              s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
              attribute_names: ["AttributeName"],
              instance_group_names: ["InstanceGroupName"],
            },
            file_system_data_source: {
              file_system_id: "FileSystemId", # required
              file_system_access_mode: "rw", # required, accepts rw, ro
              file_system_type: "EFS", # required, accepts EFS, FSxLustre
              directory_path: "DirectoryPath", # required
            },
          },
          content_type: "ContentType",
          compression_type: "None", # accepts None, Gzip
          record_wrapper_type: "None", # accepts None, RecordIO
          input_mode: "Pipe", # accepts Pipe, File, FastFile
          shuffle_config: {
            seed: 1, # required
          },
        },
      ],
      vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
      output_data_config: { # required
        kms_key_id: "KmsKeyId",
        s3_output_path: "S3Uri", # required
        compression_type: "GZIP", # accepts GZIP, NONE
      },
      resource_config: {
        instance_type: "ml.m4.xlarge", # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
        instance_count: 1,
        volume_size_in_gb: 1, # required
        volume_kms_key_id: "KmsKeyId",
        keep_alive_period_in_seconds: 1,
        instance_groups: [
          {
            instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
            instance_count: 1, # required
            instance_group_name: "InstanceGroupName", # required
          },
        ],
      },
      hyper_parameter_tuning_resource_config: {
        instance_type: "ml.m4.xlarge", # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
        instance_count: 1,
        volume_size_in_gb: 1,
        volume_kms_key_id: "KmsKeyId",
        allocation_strategy: "Prioritized", # accepts Prioritized
        instance_configs: [
          {
            instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
            instance_count: 1, # required
            volume_size_in_gb: 1, # required
          },
        ],
      },
      stopping_condition: { # required
        max_runtime_in_seconds: 1,
        max_wait_time_in_seconds: 1,
        max_pending_time_in_seconds: 1,
      },
      enable_network_isolation: false,
      enable_inter_container_traffic_encryption: false,
      enable_managed_spot_training: false,
      checkpoint_config: {
        s3_uri: "S3Uri", # required
        local_path: "DirectoryPath",
      },
      retry_strategy: {
        maximum_retry_attempts: 1, # required
      },
      environment: {
        "HyperParameterTrainingJobEnvironmentKey" => "HyperParameterTrainingJobEnvironmentValue",
      },
    },
  ],
  warm_start_config: {
    parent_hyper_parameter_tuning_jobs: [ # required
      {
        hyper_parameter_tuning_job_name: "HyperParameterTuningJobName",
      },
    ],
    warm_start_type: "IdenticalDataAndAlgorithm", # required, accepts IdenticalDataAndAlgorithm, TransferLearning
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  autotune: {
    mode: "Enabled", # required, accepts Enabled
  },
})

Response structure


resp.hyper_parameter_tuning_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



4649
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# File 'lib/aws-sdk-sagemaker/client.rb', line 4649

def create_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:create_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#create_image(params = {}) ⇒ Types::CreateImageResponse

Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see [Bring your own SageMaker image].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html

Examples:

Request syntax with placeholder values


resp = client.create_image({
  description: "ImageDescription",
  display_name: "ImageDisplayName",
  image_name: "ImageName", # required
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.image_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :description (String) —

    The description of the image.

  • :display_name (String) —

    The display name of the image. If not provided, ‘ImageName` is displayed.

  • :image_name (required, String) —

    The name of the image. Must be unique to your account.

  • :role_arn (required, String) —

    The ARN of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the image.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 4707

def create_image(params = {}, options = {})
  req = build_request(:create_image, params)
  req.send_request(options)
end

#create_image_version(params = {}) ⇒ Types::CreateImageVersionResponse

Creates a version of the SageMaker image specified by ‘ImageName`. The version represents the Amazon ECR container image specified by `BaseImage`.

Examples:

Request syntax with placeholder values


resp = client.create_image_version({
  base_image: "ImageBaseImage", # required
  client_token: "ClientToken", # required
  image_name: "ImageName", # required
  aliases: ["SageMakerImageVersionAlias"],
  vendor_guidance: "NOT_PROVIDED", # accepts NOT_PROVIDED, STABLE, TO_BE_ARCHIVED, ARCHIVED
  job_type: "TRAINING", # accepts TRAINING, INFERENCE, NOTEBOOK_KERNEL
  ml_framework: "MLFramework",
  programming_lang: "ProgrammingLang",
  processor: "CPU", # accepts CPU, GPU
  horovod: false,
  release_notes: "ReleaseNotes",
})

Response structure


resp.image_version_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :base_image (required, String) —

    The registry path of the container image to use as the starting point for this version. The path is an Amazon ECR URI in the following format:

    ‘<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name or [@digest]>`

  • :client_token (required, String) —

    A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

  • :image_name (required, String) —

    The ‘ImageName` of the `Image` to create a version of.

  • :aliases (Array<String>) —

    A list of aliases created with the image version.

  • :vendor_guidance (String) —

    The stability of the image version, specified by the maintainer.

    • ‘NOT_PROVIDED`: The maintainers did not provide a status for image version stability.

    • ‘STABLE`: The image version is stable.

    • ‘TO_BE_ARCHIVED`: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.

    • ‘ARCHIVED`: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

  • :job_type (String) —

    Indicates SageMaker job type compatibility.

    • ‘TRAINING`: The image version is compatible with SageMaker training jobs.

    • ‘INFERENCE`: The image version is compatible with SageMaker inference jobs.

    • ‘NOTEBOOK_KERNEL`: The image version is compatible with SageMaker notebook kernels.

  • :ml_framework (String) —

    The machine learning framework vended in the image version.

  • :programming_lang (String) —

    The supported programming language and its version.

  • :processor (String) —

    Indicates CPU or GPU compatibility.

    • ‘CPU`: The image version is compatible with CPU.

    • ‘GPU`: The image version is compatible with GPU.

  • :horovod (Boolean) —

    Indicates Horovod compatibility.

  • :release_notes (String) —

    The maintainer description of the image version.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 4812

def create_image_version(params = {}, options = {})
  req = build_request(:create_image_version, params)
  req.send_request(options)
end

#create_inference_component(params = {}) ⇒ Types::CreateInferenceComponentOutput

Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.

Examples:

Request syntax with placeholder values


resp = client.create_inference_component({
  inference_component_name: "InferenceComponentName", # required
  endpoint_name: "EndpointName", # required
  variant_name: "VariantName", # required
  specification: { # required
    model_name: "ModelName",
    container: {
      image: "ContainerImage",
      artifact_url: "Url",
      environment: {
        "EnvironmentKey" => "EnvironmentValue",
      },
    },
    startup_parameters: {
      model_data_download_timeout_in_seconds: 1,
      container_startup_health_check_timeout_in_seconds: 1,
    },
    compute_resource_requirements: { # required
      number_of_cpu_cores_required: 1.0,
      number_of_accelerator_devices_required: 1.0,
      min_memory_required_in_mb: 1, # required
      max_memory_required_in_mb: 1,
    },
  },
  runtime_config: { # required
    copy_count: 1, # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.inference_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :inference_component_name (required, String) —

    A unique name to assign to the inference component.

  • :endpoint_name (required, String) —

    The name of an existing endpoint where you host the inference component.

  • :variant_name (required, String) —

    The name of an existing production variant where you host the inference component.

  • :specification (required, Types::InferenceComponentSpecification) —

    Details about the resources to deploy with this inference component, including the model, container, and compute resources.

  • :runtime_config (required, Types::InferenceComponentRuntimeConfig) —

    Runtime settings for a model that is deployed with an inference component.

  • :tags (Array<Types::Tag>) —

    A list of key-value pairs associated with the model. For more information, see [Tagging Amazon Web Services resources] in the *Amazon Web Services General Reference*.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 4906

def create_inference_component(params = {}, options = {})
  req = build_request(:create_inference_component, params)
  req.send_request(options)
end

#create_inference_experiment(params = {}) ⇒ Types::CreateInferenceExperimentResponse

Creates an inference experiment using the configurations specified in the request.

Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see [Shadow tests].

Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint’s model variants based on your specified configuration.

While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see [View, monitor, and edit shadow tests].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests-view-monitor-edit.html

Examples:

Request syntax with placeholder values


resp = client.create_inference_experiment({
  name: "InferenceExperimentName", # required
  type: "ShadowMode", # required, accepts ShadowMode
  schedule: {
    start_time: Time.now,
    end_time: Time.now,
  },
  description: "InferenceExperimentDescription",
  role_arn: "RoleArn", # required
  endpoint_name: "EndpointName", # required
  model_variants: [ # required
    {
      model_name: "ModelName", # required
      variant_name: "ModelVariantName", # required
      infrastructure_config: { # required
        infrastructure_type: "RealTimeInference", # required, accepts RealTimeInference
        real_time_inference_config: { # required
          instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
          instance_count: 1, # required
        },
      },
    },
  ],
  data_storage_config: {
    destination: "DestinationS3Uri", # required
    kms_key: "KmsKeyId",
    content_type: {
      csv_content_types: ["CsvContentType"],
      json_content_types: ["JsonContentType"],
    },
  },
  shadow_mode_config: { # required
    source_model_variant_name: "ModelVariantName", # required
    shadow_model_variants: [ # required
      {
        shadow_model_variant_name: "ModelVariantName", # required
        sampling_percentage: 1, # required
      },
    ],
  },
  kms_key: "KmsKeyId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.inference_experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name for the inference experiment.

  • :type (required, String) —

    The type of the inference experiment that you want to run. The following types of experiments are possible:

    • ‘ShadowMode`: You can use this type to validate a shadow variant. For more information, see [Shadow tests].

    ^

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html

  • :schedule (Types::InferenceExperimentSchedule) —

    The duration for which you want the inference experiment to run. If you don’t specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.

  • :description (String) —

    A description for the inference experiment.

  • :role_arn (required, String) —

    The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

  • :endpoint_name (required, String) —

    The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.

  • :model_variants (required, Array<Types::ModelVariantConfig>) —

    An array of ‘ModelVariantConfig` objects. There is one for each variant in the inference experiment. Each `ModelVariantConfig` object in the array describes the infrastructure configuration for the corresponding variant.

  • :data_storage_config (Types::InferenceExperimentDataStorageConfig) —

    The Amazon S3 location and configuration for storing inference request and response data.

    This is an optional parameter that you can use for data capture. For more information, see [Capture data].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html

  • :shadow_mode_config (required, Types::ShadowModeConfig) —

    The configuration of ‘ShadowMode` inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

  • :kms_key (String) —

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The ‘KmsKey` can be any of the following formats:

    • KMS key ID

      ‘“1234abcd-12ab-34cd-56ef-1234567890ab”`

    • Amazon Resource Name (ARN) of a KMS key

      ‘“arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab”`

    • KMS key Alias

      ‘“alias/ExampleAlias”`

    • Amazon Resource Name (ARN) of a KMS key Alias

      ‘“arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias”`

    If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call ‘kms:Encrypt`. If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account. Amazon SageMaker uses server-side encryption with KMS managed keys for ‘OutputDataConfig`. If you use a bucket policy with an `s3:PutObject` permission that only allows objects with server-side encryption, set the condition key of `s3:x-amz-server-side-encryption` to `“aws:kms”`. For more information, see [KMS managed Encryption Keys] in the *Amazon Simple Storage Service Developer Guide.*

    The KMS key policy must grant permission to the IAM role that you specify in your ‘CreateEndpoint` and `UpdateEndpoint` requests. For more information, see [Using Key Policies in Amazon Web Services KMS] in the *Amazon Web Services Key Management Service Developer Guide*.

    [1]: docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html [2]: docs.aws.amazon.com/kms/latest/developerguide/key-policies.html

  • :tags (Array<Types::Tag>) —

    Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging your Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/ARG/latest/userguide/tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 5105

def create_inference_experiment(params = {}, options = {})
  req = build_request(:create_inference_experiment, params)
  req.send_request(options)
end

#create_inference_recommendations_job(params = {}) ⇒ Types::CreateInferenceRecommendationsJobResponse

Starts a recommendation job. You can create either an instance recommendation or load test job.

Examples:

Request syntax with placeholder values


resp = client.create_inference_recommendations_job({
  job_name: "RecommendationJobName", # required
  job_type: "Default", # required, accepts Default, Advanced
  role_arn: "RoleArn", # required
  input_config: { # required
    model_package_version_arn: "ModelPackageArn",
    model_name: "ModelName",
    job_duration_in_seconds: 1,
    traffic_pattern: {
      traffic_type: "PHASES", # accepts PHASES, STAIRS
      phases: [
        {
          initial_number_of_users: 1,
          spawn_rate: 1,
          duration_in_seconds: 1,
        },
      ],
      stairs: {
        duration_in_seconds: 1,
        number_of_steps: 1,
        users_per_step: 1,
      },
    },
    resource_limit: {
      max_number_of_tests: 1,
      max_parallel_of_tests: 1,
    },
    endpoint_configurations: [
      {
        instance_type: "ml.t2.medium", # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
        serverless_config: {
          memory_size_in_mb: 1, # required
          max_concurrency: 1, # required
          provisioned_concurrency: 1,
        },
        inference_specification_name: "InferenceSpecificationName",
        environment_parameter_ranges: {
          categorical_parameter_ranges: [
            {
              name: "String64", # required
              value: ["String128"], # required
            },
          ],
        },
      },
    ],
    volume_kms_key_id: "KmsKeyId",
    container_config: {
      domain: "String",
      task: "String",
      framework: "String",
      framework_version: "RecommendationJobFrameworkVersion",
      payload_config: {
        sample_payload_url: "S3Uri",
        supported_content_types: ["RecommendationJobSupportedContentType"],
      },
      nearest_model_name: "String",
      supported_instance_types: ["String"],
      supported_endpoint_type: "RealTime", # accepts RealTime, Serverless
      data_input_config: "RecommendationJobDataInputConfig",
      supported_response_mime_types: ["RecommendationJobSupportedResponseMIMEType"],
    },
    endpoints: [
      {
        endpoint_name: "EndpointName",
      },
    ],
    vpc_config: {
      security_group_ids: ["RecommendationJobVpcSecurityGroupId"], # required
      subnets: ["RecommendationJobVpcSubnetId"], # required
    },
  },
  job_description: "RecommendationJobDescription",
  stopping_conditions: {
    max_invocations: 1,
    model_latency_thresholds: [
      {
        percentile: "String64",
        value_in_milliseconds: 1,
      },
    ],
    flat_invocations: "Continue", # accepts Continue, Stop
  },
  output_config: {
    kms_key_id: "KmsKeyId",
    compiled_output_config: {
      s3_output_uri: "S3Uri",
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_name (required, String) —

    A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.

  • :job_type (required, String) —

    Defines the type of recommendation job. Specify ‘Default` to initiate an instance recommendation and `Advanced` to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (`DEFAULT`) job.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

  • :input_config (required, Types::RecommendationJobInputConfig) —

    Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.

  • :job_description (String) —

    Description of the recommendation job.

  • :stopping_conditions (Types::RecommendationJobStoppingConditions) —

    A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.

  • :output_config (Types::RecommendationJobOutputConfig) —

    Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.

  • :tags (Array<Types::Tag>) —

    The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see [Tagging Amazon Web Services Resources] in the Amazon Web Services General Reference.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 5268

def create_inference_recommendations_job(params = {}, options = {})
  req = build_request(:create_inference_recommendations_job, params)
  req.send_request(options)
end

#create_labeling_job(params = {}) ⇒ Types::CreateLabelingJobResponse

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

  • A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.

  • One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.

  • The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use *automated data labeling* to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses *active learning* to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see [Using Automated Data Labeling].

The data objects to be labeled are contained in an Amazon S3 bucket. You create a *manifest file* that describes the location of each object. For more information, see [Using Input and Output Data].

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ‘ManifestS3Uri` have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (`InProgress`) streaming labeling job in real time. To learn how to create a static labeling job, see [Create a Labeling Job (API) ][3] in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see [Create a Streaming Labeling Job].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html [3]: docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html [4]: docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html

Examples:

Request syntax with placeholder values


resp = client.create_labeling_job({
  labeling_job_name: "LabelingJobName", # required
  label_attribute_name: "LabelAttributeName", # required
  input_config: { # required
    data_source: { # required
      s3_data_source: {
        manifest_s3_uri: "S3Uri", # required
      },
      sns_data_source: {
        sns_topic_arn: "SnsTopicArn", # required
      },
    },
    data_attributes: {
      content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent
    },
  },
  output_config: { # required
    s3_output_path: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    sns_topic_arn: "SnsTopicArn",
  },
  role_arn: "RoleArn", # required
  label_category_config_s3_uri: "S3Uri",
  stopping_conditions: {
    max_human_labeled_object_count: 1,
    max_percentage_of_input_dataset_labeled: 1,
  },
  labeling_job_algorithms_config: {
    labeling_job_algorithm_specification_arn: "LabelingJobAlgorithmSpecificationArn", # required
    initial_active_learning_model_arn: "ModelArn",
    labeling_job_resource_config: {
      volume_kms_key_id: "KmsKeyId",
      vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
    },
  },
  human_task_config: { # required
    workteam_arn: "WorkteamArn", # required
    ui_config: { # required
      ui_template_s3_uri: "S3Uri",
      human_task_ui_arn: "HumanTaskUiArn",
    },
    pre_human_task_lambda_arn: "LambdaFunctionArn", # required
    task_keywords: ["TaskKeyword"],
    task_title: "TaskTitle", # required
    task_description: "TaskDescription", # required
    number_of_human_workers_per_data_object: 1, # required
    task_time_limit_in_seconds: 1, # required
    task_availability_lifetime_in_seconds: 1,
    max_concurrent_task_count: 1,
    annotation_consolidation_config: { # required
      annotation_consolidation_lambda_arn: "LambdaFunctionArn", # required
    },
    public_workforce_task_price: {
      amount_in_usd: {
        dollars: 1,
        cents: 1,
        tenth_fractions_of_a_cent: 1,
      },
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.labeling_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :labeling_job_name (required, String) —

    The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. ‘LabelingJobName` is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.

  • :label_attribute_name (required, String) —

    The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The ‘LabelAttributeName` must meet the following requirements.

    • The name can’t end with “-metadata”.

    • If you are using one of the following [built-in task types], the attribute name must end with “-ref”. If the task type you are using is not listed below, the attribute name *must not* end with “-ref”.

      • Image semantic segmentation (‘SemanticSegmentation)`, and adjustment (`AdjustmentSemanticSegmentation`) and verification (`VerificationSemanticSegmentation`) labeling jobs for this task type.

      • Video frame object detection (‘VideoObjectDetection`), and adjustment and verification (`AdjustmentVideoObjectDetection`) labeling jobs for this task type.

      • Video frame object tracking (‘VideoObjectTracking`), and adjustment and verification (`AdjustmentVideoObjectTracking`) labeling jobs for this task type.

      • 3D point cloud semantic segmentation (‘3DPointCloudSemanticSegmentation`), and adjustment and verification (`Adjustment3DPointCloudSemanticSegmentation`) labeling jobs for this task type.

      • 3D point cloud object tracking (‘3DPointCloudObjectTracking`), and adjustment and verification (`Adjustment3DPointCloudObjectTracking`) labeling jobs for this task type.

    If you are creating an adjustment or verification labeling job, you must use a different ‘LabelAttributeName` than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see [Verify and Adjust Labels].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html

  • :input_config (required, Types::LabelingJobInputConfig) —

    Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

    You must specify at least one of the following: ‘S3DataSource` or `SnsDataSource`.

    • Use ‘SnsDataSource` to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled.

    • Use ‘S3DataSource` to specify an input manifest file for both streaming and one-time labeling jobs. Adding an `S3DataSource` is optional if you use `SnsDataSource` to create a streaming labeling job.

    If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ‘ContentClassifiers` to specify that your data is free of personally identifiable information and adult content.

  • :output_config (required, Types::LabelingJobOutputConfig) —

    The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.

  • :role_arn (required, String) —

    The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

  • :label_category_config_s3_uri (String) —

    The S3 URI of the file, referred to as a *label category configuration file*, that defines the categories used to label the data objects.

    For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see [Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs].

    For named entity recognition jobs, in addition to ‘“labels”`, you must provide worker instructions in the label category configuration file using the `“instructions”` parameter: `“instructions”: header</h1><p>Add Instructions</p>”, “fullInstruction”:“<p>Add additional instructions.</p>”`. For details and an example, see [Create a Named Entity Recognition Labeling Job (API) ][2].

    For all other [built-in task types] and [custom tasks], your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing ‘label_1`, `label_2`,`…`,`label_n` with your label categories.

    ‘{ `

    ‘“document-version”: “2018-11-28”,`

    ‘“labels”: [“label_1”,“label_2”,…“label_n”]`

    ‘}`

    Note the following about the label category configuration file:

    • For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.

    • Each label category must be unique, you cannot specify duplicate label categories.

    • If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include ‘auditLabelAttributeName` in the label category configuration. Use this parameter to enter the [ `LabelAttributeName` ][5] of the labeling job you want to adjust or verify annotations of.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api [3]: docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html [4]: docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html [5]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName

  • :stopping_conditions (Types::LabelingJobStoppingConditions) —

    A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

  • :labeling_job_algorithms_config (Types::LabelingJobAlgorithmsConfig) —

    Configures the information required to perform automated data labeling.

  • :human_task_config (required, Types::HumanTaskConfig) —

    Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

  • :tags (Array<Types::Tag>) —

    An array of key/value pairs. For more information, see [Using Cost Allocation Tags] in the *Amazon Web Services Billing and Cost Management User Guide*.

    [1]: docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 5577

def create_labeling_job(params = {}, options = {})
  req = build_request(:create_labeling_job, params)
  req.send_request(options)
end

#create_mlflow_tracking_server(params = {}) ⇒ Types::CreateMlflowTrackingServerResponse

Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see [Create an MLflow Tracking Server].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server.html

Examples:

Request syntax with placeholder values


resp = client.create_mlflow_tracking_server({
  tracking_server_name: "TrackingServerName", # required
  artifact_store_uri: "S3Uri", # required
  tracking_server_size: "Small", # accepts Small, Medium, Large
  mlflow_version: "MlflowVersion",
  role_arn: "RoleArn", # required
  automatic_model_registration: false,
  weekly_maintenance_window_start: "WeeklyMaintenanceWindowStart",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.tracking_server_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    A unique string identifying the tracking server name. This string is part of the tracking server ARN.

  • :artifact_store_uri (required, String) —

    The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

  • :tracking_server_size (String) —

    The size of the tracking server you want to create. You can choose between ‘“Small”`, `“Medium”`, and `“Large”`. The default MLflow Tracking Server configuration size is `“Small”`. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.

    We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.

  • :mlflow_version (String) —

    The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see [How it works].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html#mlflow-create-tracking-server-how-it-works

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have ‘AmazonS3FullAccess` permissions. For more information on IAM permissions for tracking server creation, see [Set up IAM permissions for MLflow].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-iam.html

  • :automatic_model_registration (Boolean) —

    Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to ‘True`. To disable automatic model registration, set this value to `False`. If not specified, `AutomaticModelRegistration` defaults to `False`.

  • :weekly_maintenance_window_start (String) —

    The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.

  • :tags (Array<Types::Tag>) —

    Tags consisting of key-value pairs used to manage metadata for the tracking server.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 5674

def create_mlflow_tracking_server(params = {}, options = {})
  req = build_request(:create_mlflow_tracking_server, params)
  req.send_request(options)
end

#create_model(params = {}) ⇒ Types::CreateModelOutput

Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the ‘CreateEndpointConfig` API, and then create an endpoint with the `CreateEndpoint` API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

To run a batch transform using your model, you start a job with the ‘CreateTransformJob` API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

Examples:

Request syntax with placeholder values


resp = client.create_model({
  model_name: "ModelName", # required
  primary_container: {
    container_hostname: "ContainerHostname",
    image: "ContainerImage",
    image_config: {
      repository_access_mode: "Platform", # required, accepts Platform, Vpc
      repository_auth_config: {
        repository_credentials_provider_arn: "RepositoryCredentialsProviderArn", # required
      },
    },
    mode: "SingleModel", # accepts SingleModel, MultiModel
    model_data_url: "Url",
    model_data_source: {
      s3_data_source: {
        s3_uri: "S3ModelUri", # required
        s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
        compression_type: "None", # required, accepts None, Gzip
        model_access_config: {
          accept_eula: false, # required
        },
        hub_access_config: {
          hub_content_arn: "HubContentArn", # required
        },
      },
    },
    additional_model_data_sources: [
      {
        channel_name: "AdditionalModelChannelName", # required
        s3_data_source: { # required
          s3_uri: "S3ModelUri", # required
          s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
          compression_type: "None", # required, accepts None, Gzip
          model_access_config: {
            accept_eula: false, # required
          },
          hub_access_config: {
            hub_content_arn: "HubContentArn", # required
          },
        },
      },
    ],
    environment: {
      "EnvironmentKey" => "EnvironmentValue",
    },
    model_package_name: "VersionedArnOrName",
    inference_specification_name: "InferenceSpecificationName",
    multi_model_config: {
      model_cache_setting: "Enabled", # accepts Enabled, Disabled
    },
  },
  containers: [
    {
      container_hostname: "ContainerHostname",
      image: "ContainerImage",
      image_config: {
        repository_access_mode: "Platform", # required, accepts Platform, Vpc
        repository_auth_config: {
          repository_credentials_provider_arn: "RepositoryCredentialsProviderArn", # required
        },
      },
      mode: "SingleModel", # accepts SingleModel, MultiModel
      model_data_url: "Url",
      model_data_source: {
        s3_data_source: {
          s3_uri: "S3ModelUri", # required
          s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
          compression_type: "None", # required, accepts None, Gzip
          model_access_config: {
            accept_eula: false, # required
          },
          hub_access_config: {
            hub_content_arn: "HubContentArn", # required
          },
        },
      },
      additional_model_data_sources: [
        {
          channel_name: "AdditionalModelChannelName", # required
          s3_data_source: { # required
            s3_uri: "S3ModelUri", # required
            s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
            compression_type: "None", # required, accepts None, Gzip
            model_access_config: {
              accept_eula: false, # required
            },
            hub_access_config: {
              hub_content_arn: "HubContentArn", # required
            },
          },
        },
      ],
      environment: {
        "EnvironmentKey" => "EnvironmentValue",
      },
      model_package_name: "VersionedArnOrName",
      inference_specification_name: "InferenceSpecificationName",
      multi_model_config: {
        model_cache_setting: "Enabled", # accepts Enabled, Disabled
      },
    },
  ],
  inference_execution_config: {
    mode: "Serial", # required, accepts Serial, Direct
  },
  execution_role_arn: "RoleArn",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  enable_network_isolation: false,
})

Response structure


resp.model_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 5896

def create_model(params = {}, options = {})
  req = build_request(:create_model, params)
  req.send_request(options)
end

#create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse

Creates the definition for a model bias job.

Examples:

Request syntax with placeholder values


resp = client.create_model_bias_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
  model_bias_baseline_config: {
    baselining_job_name: "ProcessingJobName",
    constraints_resource: {
      s3_uri: "S3Uri",
    },
  },
  model_bias_app_specification: { # required
    image_uri: "ImageUri", # required
    config_uri: "S3Uri", # required
    environment: {
      "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
    },
  },
  model_bias_job_input: { # required
    endpoint_input: {
      endpoint_name: "EndpointName", # required
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    batch_transform_input: {
      data_captured_destination_s3_uri: "DestinationS3Uri", # required
      dataset_format: { # required
        csv: {
          header: false,
        },
        json: {
          line: false,
        },
        parquet: {
        },
      },
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    ground_truth_s3_input: { # required
      s3_uri: "MonitoringS3Uri",
    },
  },
  model_bias_job_output_config: { # required
    monitoring_outputs: [ # required
      {
        s3_output: { # required
          s3_uri: "MonitoringS3Uri", # required
          local_path: "ProcessingLocalPath", # required
          s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
        },
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  job_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.job_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 6053

def create_model_bias_job_definition(params = {}, options = {})
  req = build_request(:create_model_bias_job_definition, params)
  req.send_request(options)
end

#create_model_card(params = {}) ⇒ Types::CreateModelCardResponse

Creates an Amazon SageMaker Model Card.

For information about how to use model cards, see [Amazon SageMaker Model Card].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html

Examples:

Request syntax with placeholder values


resp = client.create_model_card({
  model_card_name: "EntityName", # required
  security_config: {
    kms_key_id: "KmsKeyId",
  },
  content: "ModelCardContent", # required
  model_card_status: "Draft", # required, accepts Draft, PendingReview, Approved, Archived
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.model_card_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_name (required, String) —

    The unique name of the model card.

  • :security_config (Types::ModelCardSecurityConfig) —

    An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.

  • :content (required, String) —

    The content of the model card. Content must be in [model card JSON schema] and provided as a string.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html#model-cards-json-schema

  • :model_card_status (required, String) —

    The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.

    • ‘Draft`: The model card is a work in progress.

    • ‘PendingReview`: The model card is pending review.

    • ‘Approved`: The model card is approved.

    • ‘Archived`: The model card is archived. No more updates should be made to the model card, but it can still be exported.

  • :tags (Array<Types::Tag>) —

    Key-value pairs used to manage metadata for model cards.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 6129

def create_model_card(params = {}, options = {})
  req = build_request(:create_model_card, params)
  req.send_request(options)
end

#create_model_card_export_job(params = {}) ⇒ Types::CreateModelCardExportJobResponse

Creates an Amazon SageMaker Model Card export job.

Examples:

Request syntax with placeholder values


resp = client.create_model_card_export_job({
  model_card_name: "ModelCardNameOrArn", # required
  model_card_version: 1,
  model_card_export_job_name: "EntityName", # required
  output_config: { # required
    s3_output_path: "S3Uri", # required
  },
})

Response structure


resp.model_card_export_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_name (required, String) —

    The name or Amazon Resource Name (ARN) of the model card to export.

  • :model_card_version (Integer) —

    The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.

  • :model_card_export_job_name (required, String) —

    The name of the model card export job.

  • :output_config (required, Types::ModelCardExportOutputConfig) —

    The model card output configuration that specifies the Amazon S3 path for exporting.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 6173

def create_model_card_export_job(params = {}, options = {})
  req = build_request(:create_model_card_export_job, params)
  req.send_request(options)
end

#create_model_explainability_job_definition(params = {}) ⇒ Types::CreateModelExplainabilityJobDefinitionResponse

Creates the definition for a model explainability job.

Examples:

Request syntax with placeholder values


resp = client.create_model_explainability_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
  model_explainability_baseline_config: {
    baselining_job_name: "ProcessingJobName",
    constraints_resource: {
      s3_uri: "S3Uri",
    },
  },
  model_explainability_app_specification: { # required
    image_uri: "ImageUri", # required
    config_uri: "S3Uri", # required
    environment: {
      "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
    },
  },
  model_explainability_job_input: { # required
    endpoint_input: {
      endpoint_name: "EndpointName", # required
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    batch_transform_input: {
      data_captured_destination_s3_uri: "DestinationS3Uri", # required
      dataset_format: { # required
        csv: {
          header: false,
        },
        json: {
          line: false,
        },
        parquet: {
        },
      },
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
  },
  model_explainability_job_output_config: { # required
    monitoring_outputs: [ # required
      {
        s3_output: { # required
          s3_uri: "MonitoringS3Uri", # required
          local_path: "ProcessingLocalPath", # required
          s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
        },
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  job_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.job_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 6328

def create_model_explainability_job_definition(params = {}, options = {})
  req = build_request(:create_model_explainability_job_definition, params)
  req.send_request(options)
end

#create_model_package(params = {}) ⇒ Types::CreateModelPackageOutput

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for ‘InferenceSpecification`. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for `SourceAlgorithmSpecification`.

<note markdown=“1”> There are two types of model packages:

* Versioned - a model that is part of a model group in the model
 registry.

  • Unversioned - a model package that is not part of a model group.

</note>

Examples:

Request syntax with placeholder values


resp = client.create_model_package({
  model_package_name: "EntityName",
  model_package_group_name: "ArnOrName",
  model_package_description: "EntityDescription",
  inference_specification: {
    containers: [ # required
      {
        container_hostname: "ContainerHostname",
        image: "ContainerImage", # required
        image_digest: "ImageDigest",
        model_data_url: "Url",
        model_data_source: {
          s3_data_source: {
            s3_uri: "S3ModelUri", # required
            s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
            compression_type: "None", # required, accepts None, Gzip
            model_access_config: {
              accept_eula: false, # required
            },
            hub_access_config: {
              hub_content_arn: "HubContentArn", # required
            },
          },
        },
        product_id: "ProductId",
        environment: {
          "EnvironmentKey" => "EnvironmentValue",
        },
        model_input: {
          data_input_config: "DataInputConfig", # required
        },
        framework: "String",
        framework_version: "ModelPackageFrameworkVersion",
        nearest_model_name: "String",
        additional_s3_data_source: {
          s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
          s3_uri: "S3Uri", # required
          compression_type: "None", # accepts None, Gzip
        },
      },
    ],
    supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
    supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
    supported_content_types: ["ContentType"],
    supported_response_mime_types: ["ResponseMIMEType"],
  },
  validation_specification: {
    validation_role: "RoleArn", # required
    validation_profiles: [ # required
      {
        profile_name: "EntityName", # required
        transform_job_definition: { # required
          max_concurrent_transforms: 1,
          max_payload_in_mb: 1,
          batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord
          environment: {
            "TransformEnvironmentKey" => "TransformEnvironmentValue",
          },
          transform_input: { # required
            data_source: { # required
              s3_data_source: { # required
                s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
                s3_uri: "S3Uri", # required
              },
            },
            content_type: "ContentType",
            compression_type: "None", # accepts None, Gzip
            split_type: "None", # accepts None, Line, RecordIO, TFRecord
          },
          transform_output: { # required
            s3_output_path: "S3Uri", # required
            accept: "Accept",
            assemble_with: "None", # accepts None, Line
            kms_key_id: "KmsKeyId",
          },
          transform_resources: { # required
            instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
            instance_count: 1, # required
            volume_kms_key_id: "KmsKeyId",
          },
        },
      },
    ],
  },
  source_algorithm_specification: {
    source_algorithms: [ # required
      {
        model_data_url: "Url",
        model_data_source: {
          s3_data_source: {
            s3_uri: "S3ModelUri", # required
            s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
            compression_type: "None", # required, accepts None, Gzip
            model_access_config: {
              accept_eula: false, # required
            },
            hub_access_config: {
              hub_content_arn: "HubContentArn", # required
            },
          },
        },
        algorithm_name: "ArnOrName", # required
      },
    ],
  },
  certify_for_marketplace: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  model_approval_status: "Approved", # accepts Approved, Rejected, PendingManualApproval
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  model_metrics: {
    model_quality: {
      statistics: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
    model_data_quality: {
      statistics: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
    bias: {
      report: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      pre_training_report: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      post_training_report: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
    explainability: {
      report: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
  },
  client_token: "ClientToken",
  domain: "String",
  task: "String",
  sample_payload_url: "S3Uri",
  customer_metadata_properties: {
    "CustomerMetadataKey" => "CustomerMetadataValue",
  },
  drift_check_baselines: {
    bias: {
      config_file: {
        content_type: "ContentType",
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      pre_training_constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      post_training_constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
    explainability: {
      constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      config_file: {
        content_type: "ContentType",
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
    model_quality: {
      statistics: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
    model_data_quality: {
      statistics: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
      constraints: {
        content_type: "ContentType", # required
        content_digest: "ContentDigest",
        s3_uri: "S3Uri", # required
      },
    },
  },
  additional_inference_specifications: [
    {
      name: "EntityName", # required
      description: "EntityDescription",
      containers: [ # required
        {
          container_hostname: "ContainerHostname",
          image: "ContainerImage", # required
          image_digest: "ImageDigest",
          model_data_url: "Url",
          model_data_source: {
            s3_data_source: {
              s3_uri: "S3ModelUri", # required
              s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
              compression_type: "None", # required, accepts None, Gzip
              model_access_config: {
                accept_eula: false, # required
              },
              hub_access_config: {
                hub_content_arn: "HubContentArn", # required
              },
            },
          },
          product_id: "ProductId",
          environment: {
            "EnvironmentKey" => "EnvironmentValue",
          },
          model_input: {
            data_input_config: "DataInputConfig", # required
          },
          framework: "String",
          framework_version: "ModelPackageFrameworkVersion",
          nearest_model_name: "String",
          additional_s3_data_source: {
            s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
            s3_uri: "S3Uri", # required
            compression_type: "None", # accepts None, Gzip
          },
        },
      ],
      supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
      supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
      supported_content_types: ["ContentType"],
      supported_response_mime_types: ["ResponseMIMEType"],
    },
  ],
  skip_model_validation: "All", # accepts All, None
  source_uri: "ModelPackageSourceUri",
  security_config: {
    kms_key_id: "KmsKeyId", # required
  },
  model_card: {
    model_card_content: "ModelCardContent",
    model_card_status: "Draft", # accepts Draft, PendingReview, Approved, Archived
  },
})

Response structure


resp.model_package_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_name (String) —

    The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

    This parameter is required for unversioned models. It is not applicable to versioned models.

  • :model_package_group_name (String) —

    The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

    This parameter is required for versioned models, and does not apply to unversioned models.

  • :model_package_description (String) —

    A description of the model package.

  • :inference_specification (Types::InferenceSpecification) —

    Specifies details about inference jobs that you can run with models based on this model package, including the following information:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the model package supports for inference.

  • :validation_specification (Types::ModelPackageValidationSpecification) —

    Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

  • :source_algorithm_specification (Types::SourceAlgorithmSpecification) —

    Details about the algorithm that was used to create the model package.

  • :certify_for_marketplace (Boolean) —

    Whether to certify the model package for listing on Amazon Web Services Marketplace.

    This parameter is optional for unversioned models, and does not apply to versioned models.

  • :tags (Array<Types::Tag>) —

    A list of key value pairs associated with the model. For more information, see [Tagging Amazon Web Services resources] in the *Amazon Web Services General Reference Guide*.

    If you supply ‘ModelPackageGroupName`, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a `tag` argument.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

  • :model_approval_status (String) —

    Whether the model is approved for deployment.

    This parameter is optional for versioned models, and does not apply to unversioned models.

    For versioned models, the value of this parameter must be set to ‘Approved` to deploy the model.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :model_metrics (Types::ModelMetrics) —

    A structure that contains model metrics reports.

  • :client_token (String) —

    A unique token that guarantees that the call to this API is idempotent.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

  • :domain (String) —

    The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

  • :task (String) —

    The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: ‘“IMAGE_CLASSIFICATION”` | `“OBJECT_DETECTION”` | `“TEXT_GENERATION”` |`“IMAGE_SEGMENTATION”` | `“FILL_MASK”` | `“CLASSIFICATION”` | `“REGRESSION”` | `“OTHER”`.

    Specify “OTHER” if none of the tasks listed fit your use case.

  • :sample_payload_url (String) —

    The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the [InvokeEndpoint] call.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html#API_runtime_InvokeEndpoint_RequestSyntax

  • :customer_metadata_properties (Hash<String,String>) —

    The metadata properties associated with the model package versions.

  • :drift_check_baselines (Types::DriftCheckBaselines) —

    Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on [Drift Detection against Previous Baselines in SageMaker Pipelines] in the *Amazon SageMaker Developer Guide*.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/pipelines-quality-clarify-baseline-lifecycle.html#pipelines-quality-clarify-baseline-drift-detection

  • :additional_inference_specifications (Array<Types::AdditionalInferenceSpecificationDefinition>) —

    An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

  • :skip_model_validation (String) —

    Indicates if you want to skip model validation.

  • :source_uri (String) —

    The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

  • :security_config (Types::ModelPackageSecurityConfig) —

    The KMS Key ID (‘KMSKeyId`) used for encryption of model package information.

  • :model_card (Types::ModelPackageModelCard) —

    The model card associated with the model package. Since ‘ModelPackageModelCard` is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of `ModelCard`. The `ModelPackageModelCard` schema does not include `model_package_details`, and `model_overview` is composed of the `model_creator` and `model_artifact` properties. For more information about the model package model card schema, see [Model package model card schema]. For more information about the model card associated with the model package, see [View the Details of a Model Version].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/model-registry-details.html#model-card-schema [2]: docs.aws.amazon.com/sagemaker/latest/dg/model-registry-details.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 6809

def create_model_package(params = {}, options = {})
  req = build_request(:create_model_package, params)
  req.send_request(options)
end

#create_model_package_group(params = {}) ⇒ Types::CreateModelPackageGroupOutput

Creates a model group. A model group contains a group of model versions.

Examples:

Request syntax with placeholder values


resp = client.create_model_package_group({
  model_package_group_name: "EntityName", # required
  model_package_group_description: "EntityDescription",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.model_package_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_group_name (required, String) —

    The name of the model group.

  • :model_package_group_description (String) —

    A description for the model group.

  • :tags (Array<Types::Tag>) —

    A list of key value pairs associated with the model group. For more information, see [Tagging Amazon Web Services resources] in the *Amazon Web Services General Reference Guide*.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 6857

def create_model_package_group(params = {}, options = {})
  req = build_request(:create_model_package_group, params)
  req.send_request(options)
end

#create_model_quality_job_definition(params = {}) ⇒ Types::CreateModelQualityJobDefinitionResponse

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see [Amazon SageMaker Model Monitor].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html

Examples:

Request syntax with placeholder values


resp = client.create_model_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
  model_quality_baseline_config: {
    baselining_job_name: "ProcessingJobName",
    constraints_resource: {
      s3_uri: "S3Uri",
    },
  },
  model_quality_app_specification: { # required
    image_uri: "ImageUri", # required
    container_entrypoint: ["ContainerEntrypointString"],
    container_arguments: ["ContainerArgument"],
    record_preprocessor_source_uri: "S3Uri",
    post_analytics_processor_source_uri: "S3Uri",
    problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression
    environment: {
      "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
    },
  },
  model_quality_job_input: { # required
    endpoint_input: {
      endpoint_name: "EndpointName", # required
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    batch_transform_input: {
      data_captured_destination_s3_uri: "DestinationS3Uri", # required
      dataset_format: { # required
        csv: {
          header: false,
        },
        json: {
          line: false,
        },
        parquet: {
        },
      },
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    ground_truth_s3_input: { # required
      s3_uri: "MonitoringS3Uri",
    },
  },
  model_quality_job_output_config: { # required
    monitoring_outputs: [ # required
      {
        s3_output: { # required
          s3_uri: "MonitoringS3Uri", # required
          local_path: "ProcessingLocalPath", # required
          s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
        },
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  job_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.job_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7023

def create_model_quality_job_definition(params = {}, options = {})
  req = build_request(:create_model_quality_job_definition, params)
  req.send_request(options)
end

#create_monitoring_schedule(params = {}) ⇒ Types::CreateMonitoringScheduleResponse

Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.

Examples:

Request syntax with placeholder values


resp = client.create_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
  monitoring_schedule_config: { # required
    schedule_config: {
      schedule_expression: "ScheduleExpression", # required
      data_analysis_start_time: "String",
      data_analysis_end_time: "String",
    },
    monitoring_job_definition: {
      baseline_config: {
        baselining_job_name: "ProcessingJobName",
        constraints_resource: {
          s3_uri: "S3Uri",
        },
        statistics_resource: {
          s3_uri: "S3Uri",
        },
      },
      monitoring_inputs: [ # required
        {
          endpoint_input: {
            endpoint_name: "EndpointName", # required
            local_path: "ProcessingLocalPath", # required
            s3_input_mode: "Pipe", # accepts Pipe, File
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
            features_attribute: "String",
            inference_attribute: "String",
            probability_attribute: "String",
            probability_threshold_attribute: 1.0,
            start_time_offset: "MonitoringTimeOffsetString",
            end_time_offset: "MonitoringTimeOffsetString",
            exclude_features_attribute: "ExcludeFeaturesAttribute",
          },
          batch_transform_input: {
            data_captured_destination_s3_uri: "DestinationS3Uri", # required
            dataset_format: { # required
              csv: {
                header: false,
              },
              json: {
                line: false,
              },
              parquet: {
              },
            },
            local_path: "ProcessingLocalPath", # required
            s3_input_mode: "Pipe", # accepts Pipe, File
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
            features_attribute: "String",
            inference_attribute: "String",
            probability_attribute: "String",
            probability_threshold_attribute: 1.0,
            start_time_offset: "MonitoringTimeOffsetString",
            end_time_offset: "MonitoringTimeOffsetString",
            exclude_features_attribute: "ExcludeFeaturesAttribute",
          },
        },
      ],
      monitoring_output_config: { # required
        monitoring_outputs: [ # required
          {
            s3_output: { # required
              s3_uri: "MonitoringS3Uri", # required
              local_path: "ProcessingLocalPath", # required
              s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
            },
          },
        ],
        kms_key_id: "KmsKeyId",
      },
      monitoring_resources: { # required
        cluster_config: { # required
          instance_count: 1, # required
          instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
          volume_size_in_gb: 1, # required
          volume_kms_key_id: "KmsKeyId",
        },
      },
      monitoring_app_specification: { # required
        image_uri: "ImageUri", # required
        container_entrypoint: ["ContainerEntrypointString"],
        container_arguments: ["ContainerArgument"],
        record_preprocessor_source_uri: "S3Uri",
        post_analytics_processor_source_uri: "S3Uri",
      },
      stopping_condition: {
        max_runtime_in_seconds: 1, # required
      },
      environment: {
        "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
      },
      network_config: {
        enable_inter_container_traffic_encryption: false,
        enable_network_isolation: false,
        vpc_config: {
          security_group_ids: ["SecurityGroupId"], # required
          subnets: ["SubnetId"], # required
        },
      },
      role_arn: "RoleArn", # required
    },
    monitoring_job_definition_name: "MonitoringJobDefinitionName",
    monitoring_type: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.monitoring_schedule_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7171

def create_monitoring_schedule(params = {}, options = {})
  req = build_request(:create_monitoring_schedule, params)
  req.send_request(options)
end

#create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput

Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a ‘CreateNotebookInstance` request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, SageMaker does the following:

  1. Creates a network interface in the SageMaker VPC.

  2. (Option) If you specified ‘SubnetId`, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified ‘SubnetId` of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can’t change the name of a notebook instance after you create it.

After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.

For more information, see [How It Works].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html

Examples:

Request syntax with placeholder values


resp = client.create_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
  instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
  subnet_id: "SubnetId",
  security_group_ids: ["SecurityGroupId"],
  role_arn: "RoleArn", # required
  kms_key_id: "KmsKeyId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  lifecycle_config_name: "NotebookInstanceLifecycleConfigName",
  direct_internet_access: "Enabled", # accepts Enabled, Disabled
  volume_size_in_gb: 1,
  accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
  default_code_repository: "CodeRepositoryNameOrUrl",
  additional_code_repositories: ["CodeRepositoryNameOrUrl"],
  root_access: "Enabled", # accepts Enabled, Disabled
  platform_identifier: "PlatformIdentifier",
  instance_metadata_service_configuration: {
    minimum_instance_metadata_service_version: "MinimumInstanceMetadataServiceVersion", # required
  },
})

Response structure


resp.notebook_instance_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the new notebook instance.

  • :instance_type (required, String) —

    The type of ML compute instance to launch for the notebook instance.

  • :subnet_id (String) —

    The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

  • :security_group_ids (Array<String>) —

    The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

  • :role_arn (required, String) —

    When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker can perform these tasks. The policy must allow the SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see [SageMaker Roles].

    <note markdown=“1”> To be able to pass this role to SageMaker, the caller of this API must have the ‘iam:PassRole` permission.

    </note>

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

  • :kms_key_id (String) —

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see [Enabling and Disabling Keys] in the *Amazon Web Services Key Management Service Developer Guide*.

    [1]: docs.aws.amazon.com/kms/latest/developerguide/enabling-keys.html

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

  • :lifecycle_config_name (String) —

    The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html

  • :direct_internet_access (String) —

    Sets whether SageMaker provides internet access to the notebook instance. If you set this to ‘Disabled` this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.

    For more information, see [Notebook Instances Are Internet-Enabled by Default]. You can set the value of this parameter to ‘Disabled` only if you set a value for the `SubnetId` parameter.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/appendix-additional-considerations.html#appendix-notebook-and-internet-access

  • :volume_size_in_gb (Integer) —

    The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

  • :accelerator_types (Array<String>) —

    A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see [Using Elastic Inference in Amazon SageMaker].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • :default_code_repository (String) —

    A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in [Amazon Web Services CodeCommit] or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see [Associating Git Repositories with SageMaker Notebook Instances].

    [1]: docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html

  • :additional_code_repositories (Array<String>) —

    An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in

    Amazon Web Services CodeCommit][1

    or in any other Git repository.

    These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see [Associating Git Repositories with SageMaker Notebook Instances].

    [1]: docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html

  • :root_access (String) —

    Whether root access is enabled or disabled for users of the notebook instance. The default value is ‘Enabled`.

    <note markdown=“1”> Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

    </note>
  • :platform_identifier (String) —

    The platform identifier of the notebook instance runtime environment.

  • :instance_metadata_service_configuration (Types::InstanceMetadataServiceConfiguration) —

    Information on the IMDS configuration of the notebook instance

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7397

def create_notebook_instance(params = {}, options = {})
  req = build_request(:create_notebook_instance, params)
  req.send_request(options)
end

#create_notebook_instance_lifecycle_config(params = {}) ⇒ Types::CreateNotebookInstanceLifecycleConfigOutput

Creates a lifecycle configuration that you can associate with a notebook instance. A *lifecycle configuration* is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the ‘$PATH` environment variable that is available to both scripts is `/sbin:bin:/usr/sbin:/usr/bin`.

View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group ‘/aws/sagemaker/NotebookInstances` in log stream `[notebook-instance-name]/`.

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html

Examples:

Request syntax with placeholder values


resp = client.create_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
  on_create: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
  on_start: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
})

Response structure


resp.notebook_instance_lifecycle_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_lifecycle_config_name (required, String) —

    The name of the lifecycle configuration.

  • :on_create (Array<Types::NotebookInstanceLifecycleHook>) —

    A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

  • :on_start (Array<Types::NotebookInstanceLifecycleHook>) —

    A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7466

def create_notebook_instance_lifecycle_config(params = {}, options = {})
  req = build_request(:create_notebook_instance_lifecycle_config, params)
  req.send_request(options)
end

#create_optimization_job(params = {}) ⇒ Types::CreateOptimizationJobResponse

Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.

For more information about how to use this action, and about the supported optimization techniques, see [Optimize model inference with Amazon SageMaker].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/model-optimize.html

Examples:

Request syntax with placeholder values


resp = client.create_optimization_job({
  optimization_job_name: "EntityName", # required
  role_arn: "RoleArn", # required
  model_source: { # required
    s3: {
      s3_uri: "S3Uri",
      model_access_config: {
        accept_eula: false, # required
      },
    },
  },
  deployment_instance_type: "ml.p4d.24xlarge", # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge
  optimization_environment: {
    "NonEmptyString256" => "String256",
  },
  optimization_configs: [ # required
    {
      model_quantization_config: {
        image: "OptimizationContainerImage",
        override_environment: {
          "NonEmptyString256" => "String256",
        },
      },
      model_compilation_config: {
        image: "OptimizationContainerImage",
        override_environment: {
          "NonEmptyString256" => "String256",
        },
      },
    },
  ],
  output_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_location: "S3Uri", # required
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
    max_pending_time_in_seconds: 1,
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  vpc_config: {
    security_group_ids: ["OptimizationVpcSecurityGroupId"], # required
    subnets: ["OptimizationVpcSubnetId"], # required
  },
})

Response structure


resp.optimization_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :optimization_job_name (required, String) —

    A custom name for the new optimization job.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

    During model optimization, Amazon SageMaker needs your permission to:

    • Read input data from an S3 bucket

    • Write model artifacts to an S3 bucket

    • Write logs to Amazon CloudWatch Logs

    • Publish metrics to Amazon CloudWatch

    You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the ‘iam:PassRole` permission. For more information, see [Amazon SageMaker Roles.]

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

  • :model_source (required, Types::OptimizationJobModelSource) —

    The location of the source model to optimize with an optimization job.

  • :deployment_instance_type (required, String) —

    The type of instance that hosts the optimized model that you create with the optimization job.

  • :optimization_environment (Hash<String,String>) —

    The environment variables to set in the model container.

  • :optimization_configs (required, Array<Types::OptimizationConfig>) —

    Settings for each of the optimization techniques that the job applies.

  • :output_config (required, Types::OptimizationJobOutputConfig) —

    Details for where to store the optimized model that you create with the optimization job.

  • :stopping_condition (required, Types::StoppingCondition) —

    Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.

    To stop a training job, SageMaker sends the algorithm the ‘SIGTERM` signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

    The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with ‘CreateModel`.

    <note markdown=“1”> The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

    </note>
  • :tags (Array<Types::Tag>) —

    A list of key-value pairs associated with the optimization job. For more information, see [Tagging Amazon Web Services resources] in the *Amazon Web Services General Reference Guide*.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

  • :vpc_config (Types::OptimizationVpcConfig) —

    A VPC in Amazon VPC that your optimized model has access to.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7629

def create_optimization_job(params = {}, options = {})
  req = build_request(:create_optimization_job, params)
  req.send_request(options)
end

#create_pipeline(params = {}) ⇒ Types::CreatePipelineResponse

Creates a pipeline using a JSON pipeline definition.

Examples:

Request syntax with placeholder values


resp = client.create_pipeline({
  pipeline_name: "PipelineName", # required
  pipeline_display_name: "PipelineName",
  pipeline_definition: "PipelineDefinition",
  pipeline_definition_s3_location: {
    bucket: "BucketName", # required
    object_key: "Key", # required
    version_id: "VersionId",
  },
  pipeline_description: "PipelineDescription",
  client_request_token: "IdempotencyToken", # required
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  parallelism_configuration: {
    max_parallel_execution_steps: 1, # required
  },
})

Response structure


resp.pipeline_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name (required, String) —

    The name of the pipeline.

  • :pipeline_display_name (String) —

    The display name of the pipeline.

  • :pipeline_definition (String) —

    The [JSON pipeline definition] of the pipeline.

    [1]: aws-sagemaker-mlops.github.io/sagemaker-model-building-pipeline-definition-JSON-schema/

  • :pipeline_definition_s3_location (Types::PipelineDefinitionS3Location) —

    The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

  • :pipeline_description (String) —

    A description of the pipeline.

  • :client_request_token (required, String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the created pipeline.

  • :parallelism_configuration (Types::ParallelismConfiguration) —

    This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7714

def create_pipeline(params = {}, options = {})
  req = build_request(:create_pipeline, params)
  req.send_request(options)
end

#create_presigned_domain_url(params = {}) ⇒ Types::CreatePresignedDomainUrlResponse

Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain’s Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.

The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.

You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see [Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint] .

<note markdown=“1”> The URL that you get from a call to ‘CreatePresignedDomainUrl` has a default timeout of 5 minutes. You can configure this value using `ExpiresInSeconds`. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/dg/studio-interface-endpoint.html

Examples:

Request syntax with placeholder values


resp = client.create_presigned_domain_url({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  session_expiration_duration_in_seconds: 1,
  expires_in_seconds: 1,
  space_name: "SpaceName",
  landing_uri: "LandingUri",
})

Response structure


resp.authorized_url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The name of the UserProfile to sign-in as.

  • :session_expiration_duration_in_seconds (Integer) —

    The session expiration duration in seconds. This value defaults to 43200.

  • :expires_in_seconds (Integer) —

    The number of seconds until the pre-signed URL expires. This value defaults to 300.

  • :space_name (String) —

    The name of the space.

  • :landing_uri (String) —

    The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:

    • ‘studio::relative/path`: Directs users to the relative path in Studio.

    • ‘app:JupyterServer:relative/path`: Directs users to the relative path in the Studio Classic application.

    • ‘app:JupyterLab:relative/path`: Directs users to the relative path in the JupyterLab application.

    • ‘app:RStudioServerPro:relative/path`: Directs users to the relative path in the RStudio application.

    • ‘app:CodeEditor:relative/path`: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application.

    • ‘app:Canvas:relative/path`: Directs users to the relative path in the Canvas application.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7813

def create_presigned_domain_url(params = {}, options = {})
  req = build_request(:create_presigned_domain_url, params)
  req.send_request(options)
end

#create_presigned_mlflow_tracking_server_url(params = {}) ⇒ Types::CreatePresignedMlflowTrackingServerUrlResponse

Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see [Launch the MLflow UI using a presigned URL].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/mlflow-launch-ui.html

Examples:

Request syntax with placeholder values


resp = client.create_presigned_mlflow_tracking_server_url({
  tracking_server_name: "TrackingServerName", # required
  expires_in_seconds: 1,
  session_expiration_duration_in_seconds: 1,
})

Response structure


resp.authorized_url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    The name of the tracking server to connect to your MLflow UI.

  • :expires_in_seconds (Integer) —

    The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.

  • :session_expiration_duration_in_seconds (Integer) —

    The duration in seconds that your MLflow UI session is valid.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7856

def create_presigned_mlflow_tracking_server_url(params = {}, options = {})
  req = build_request(:create_presigned_mlflow_tracking_server_url, params)
  req.send_request(options)
end

#create_presigned_notebook_instance_url(params = {}) ⇒ Types::CreatePresignedNotebookInstanceUrlOutput

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose ‘Open` next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.

You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the ‘NotIpAddress` condition operator and the `aws:SourceIP` condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see [Limit Access to a Notebook Instance by IP Address].

<note markdown=“1”> The URL that you get from a call to

CreatePresignedNotebookInstanceUrl][2

is valid only for 5 minutes.

If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/dg/security_iam_id-based-policy-examples.html#nbi-ip-filter [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreatePresignedNotebookInstanceUrl.html

Examples:

Request syntax with placeholder values


resp = client.create_presigned_notebook_instance_url({
  notebook_instance_name: "NotebookInstanceName", # required
  session_expiration_duration_in_seconds: 1,
})

Response structure


resp.authorized_url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the notebook instance.

  • :session_expiration_duration_in_seconds (Integer) —

    The duration of the session, in seconds. The default is 12 hours.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 7918

def create_presigned_notebook_instance_url(params = {}, options = {})
  req = build_request(:create_presigned_notebook_instance_url, params)
  req.send_request(options)
end

#create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse

Creates a processing job.

Examples:

Request syntax with placeholder values


resp = client.create_processing_job({
  processing_inputs: [
    {
      input_name: "String", # required
      app_managed: false,
      s3_input: {
        s3_uri: "S3Uri", # required
        local_path: "ProcessingLocalPath",
        s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
        s3_input_mode: "Pipe", # accepts Pipe, File
        s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
        s3_compression_type: "None", # accepts None, Gzip
      },
      dataset_definition: {
        athena_dataset_definition: {
          catalog: "AthenaCatalog", # required
          database: "AthenaDatabase", # required
          query_string: "AthenaQueryString", # required
          work_group: "AthenaWorkGroup",
          output_s3_uri: "S3Uri", # required
          kms_key_id: "KmsKeyId",
          output_format: "PARQUET", # required, accepts PARQUET, ORC, AVRO, JSON, TEXTFILE
          output_compression: "GZIP", # accepts GZIP, SNAPPY, ZLIB
        },
        redshift_dataset_definition: {
          cluster_id: "RedshiftClusterId", # required
          database: "RedshiftDatabase", # required
          db_user: "RedshiftUserName", # required
          query_string: "RedshiftQueryString", # required
          cluster_role_arn: "RoleArn", # required
          output_s3_uri: "S3Uri", # required
          kms_key_id: "KmsKeyId",
          output_format: "PARQUET", # required, accepts PARQUET, CSV
          output_compression: "None", # accepts None, GZIP, BZIP2, ZSTD, SNAPPY
        },
        local_path: "ProcessingLocalPath",
        data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
        input_mode: "Pipe", # accepts Pipe, File
      },
    },
  ],
  processing_output_config: {
    outputs: [ # required
      {
        output_name: "String", # required
        s3_output: {
          s3_uri: "S3Uri", # required
          local_path: "ProcessingLocalPath",
          s3_upload_mode: "Continuous", # required, accepts Continuous, EndOfJob
        },
        feature_store_output: {
          feature_group_name: "FeatureGroupName", # required
        },
        app_managed: false,
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  processing_job_name: "ProcessingJobName", # required
  processing_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  app_specification: { # required
    image_uri: "ImageUri", # required
    container_entrypoint: ["ContainerEntrypointString"],
    container_arguments: ["ContainerArgument"],
  },
  environment: {
    "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
    run_name: "ExperimentEntityName",
  },
})

Response structure


resp.processing_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 8102

def create_processing_job(params = {}, options = {})
  req = build_request(:create_processing_job, params)
  req.send_request(options)
end

#create_project(params = {}) ⇒ Types::CreateProjectOutput

Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.

Examples:

Request syntax with placeholder values


resp = client.create_project({
  project_name: "ProjectEntityName", # required
  project_description: "EntityDescription",
  service_catalog_provisioning_details: { # required
    product_id: "ServiceCatalogEntityId", # required
    provisioning_artifact_id: "ServiceCatalogEntityId",
    path_id: "ServiceCatalogEntityId",
    provisioning_parameters: [
      {
        key: "ProvisioningParameterKey",
        value: "ProvisioningParameterValue",
      },
    ],
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.project_arn #=> String
resp.project_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_name (required, String) —

    The name of the project.

  • :project_description (String) —

    A description for the project.

  • :service_catalog_provisioning_details (required, Types::ServiceCatalogProvisioningDetails) —

    The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don’t provide the provisioning artifact ID. For more information, see [What is Amazon Web Services Service Catalog].

    [1]: docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see

    Tagging Amazon Web Services resources][1

    in the *Amazon Web Services

    General Reference Guide*.

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 8176

def create_project(params = {}, options = {})
  req = build_request(:create_project, params)
  req.send_request(options)
end

#create_space(params = {}) ⇒ Types::CreateSpaceResponse

Creates a private space or a space used for real time collaboration in a domain.

Examples:

Request syntax with placeholder values


resp = client.create_space({
  domain_id: "DomainId", # required
  space_name: "SpaceName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  space_settings: {
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      app_lifecycle_management: {
        idle_settings: {
          idle_timeout_in_minutes: 1,
        },
      },
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          idle_timeout_in_minutes: 1,
        },
      },
    },
    app_type: "JupyterServer", # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
    space_storage_settings: {
      ebs_storage_settings: {
        ebs_volume_size_in_gb: 1, # required
      },
    },
    custom_file_systems: [
      {
        efs_file_system: {
          file_system_id: "FileSystemId", # required
        },
      },
    ],
  },
  ownership_settings: {
    owner_user_profile_name: "UserProfileName", # required
  },
  space_sharing_settings: {
    sharing_type: "Private", # required, accepts Private, Shared
  },
  space_display_name: "NonEmptyString64",
})

Response structure


resp.space_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the associated domain.

  • :space_name (required, String) —

    The name of the space.

  • :tags (Array<Types::Tag>) —

    Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the ‘Search` API.

  • :space_settings (Types::SpaceSettings) —

    A collection of space settings.

  • :ownership_settings (Types::OwnershipSettings) —

    A collection of ownership settings.

  • :space_sharing_settings (Types::SpaceSharingSettings) —

    A collection of space sharing settings.

  • :space_display_name (String) —

    The name of the space that appears in the SageMaker Studio UI.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 8319

def create_space(params = {}, options = {})
  req = build_request(:create_space, params)
  req.send_request(options)
end

#create_studio_lifecycle_config(params = {}) ⇒ Types::CreateStudioLifecycleConfigResponse

Creates a new Amazon SageMaker Studio Lifecycle Configuration.

Examples:

Request syntax with placeholder values


resp = client.create_studio_lifecycle_config({
  studio_lifecycle_config_name: "StudioLifecycleConfigName", # required
  studio_lifecycle_config_content: "StudioLifecycleConfigContent", # required
  studio_lifecycle_config_app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, CodeEditor, JupyterLab
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.studio_lifecycle_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :studio_lifecycle_config_name (required, String) —

    The name of the Amazon SageMaker Studio Lifecycle Configuration to create.

  • :studio_lifecycle_config_content (required, String) —

    The content of your Amazon SageMaker Studio Lifecycle Configuration script. This content must be base64 encoded.

  • :studio_lifecycle_config_app_type (required, String) —

    The App type that the Lifecycle Configuration is attached to.

  • :tags (Array<Types::Tag>) —

    Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 8368

def create_studio_lifecycle_config(params = {}, options = {})
  req = build_request(:create_studio_lifecycle_config, params)
  req.send_request(options)
end

#create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.

In the request body, you provide the following:

  • ‘AlgorithmSpecification` - Identifies the training algorithm to use.

  • ‘HyperParameters` - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see [Algorithms].

    Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

  • ‘InputDataConfig` - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.

  • ‘OutputDataConfig` - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.

  • ‘ResourceConfig` - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

  • ‘EnableManagedSpotTraining` - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see [Managed Spot Training].

  • ‘RoleArn` - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.

  • ‘StoppingCondition` - To help cap training costs, use `MaxRuntimeInSeconds` to set a time limit for training. Use `MaxWaitTimeInSeconds` to specify how long a managed spot training job has to complete.

  • ‘Environment` - The environment variables to set in the Docker container.

  • ‘RetryStrategy` - The number of times to retry the job when the job fails due to an `InternalServerError`.

For more information about SageMaker, see [How It Works].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/algos.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html [3]: docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html

Examples:

Request syntax with placeholder values


resp = client.create_training_job({
  training_job_name: "TrainingJobName", # required
  hyper_parameters: {
    "HyperParameterKey" => "HyperParameterValue",
  },
  algorithm_specification: { # required
    training_image: "AlgorithmImage",
    algorithm_name: "ArnOrName",
    training_input_mode: "Pipe", # required, accepts Pipe, File, FastFile
    metric_definitions: [
      {
        name: "MetricName", # required
        regex: "MetricRegex", # required
      },
    ],
    enable_sage_maker_metrics_time_series: false,
    container_entrypoint: ["TrainingContainerEntrypointString"],
    container_arguments: ["TrainingContainerArgument"],
    training_image_config: {
      training_repository_access_mode: "Platform", # required, accepts Platform, Vpc
      training_repository_auth_config: {
        training_repository_credentials_provider_arn: "TrainingRepositoryCredentialsProviderArn", # required
      },
    },
  },
  role_arn: "RoleArn", # required
  input_data_config: [
    {
      channel_name: "ChannelName", # required
      data_source: { # required
        s3_data_source: {
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
          s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
          attribute_names: ["AttributeName"],
          instance_group_names: ["InstanceGroupName"],
        },
        file_system_data_source: {
          file_system_id: "FileSystemId", # required
          file_system_access_mode: "rw", # required, accepts rw, ro
          file_system_type: "EFS", # required, accepts EFS, FSxLustre
          directory_path: "DirectoryPath", # required
        },
      },
      content_type: "ContentType",
      compression_type: "None", # accepts None, Gzip
      record_wrapper_type: "None", # accepts None, RecordIO
      input_mode: "Pipe", # accepts Pipe, File, FastFile
      shuffle_config: {
        seed: 1, # required
      },
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
    compression_type: "GZIP", # accepts GZIP, NONE
  },
  resource_config: { # required
    instance_type: "ml.m4.xlarge", # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
    instance_count: 1,
    volume_size_in_gb: 1, # required
    volume_kms_key_id: "KmsKeyId",
    keep_alive_period_in_seconds: 1,
    instance_groups: [
      {
        instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
        instance_count: 1, # required
        instance_group_name: "InstanceGroupName", # required
      },
    ],
  },
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
    max_pending_time_in_seconds: 1,
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  enable_network_isolation: false,
  enable_inter_container_traffic_encryption: false,
  enable_managed_spot_training: false,
  checkpoint_config: {
    s3_uri: "S3Uri", # required
    local_path: "DirectoryPath",
  },
  debug_hook_config: {
    local_path: "DirectoryPath",
    s3_output_path: "S3Uri", # required
    hook_parameters: {
      "ConfigKey" => "ConfigValue",
    },
    collection_configurations: [
      {
        collection_name: "CollectionName",
        collection_parameters: {
          "ConfigKey" => "ConfigValue",
        },
      },
    ],
  },
  debug_rule_configurations: [
    {
      rule_configuration_name: "RuleConfigurationName", # required
      local_path: "DirectoryPath",
      s3_output_path: "S3Uri",
      rule_evaluator_image: "AlgorithmImage", # required
      instance_type: "ml.t3.medium", # accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1,
      rule_parameters: {
        "ConfigKey" => "ConfigValue",
      },
    },
  ],
  tensor_board_output_config: {
    local_path: "DirectoryPath",
    s3_output_path: "S3Uri", # required
  },
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
    run_name: "ExperimentEntityName",
  },
  profiler_config: {
    s3_output_path: "S3Uri",
    profiling_interval_in_milliseconds: 1,
    profiling_parameters: {
      "ConfigKey" => "ConfigValue",
    },
    disable_profiler: false,
  },
  profiler_rule_configurations: [
    {
      rule_configuration_name: "RuleConfigurationName", # required
      local_path: "DirectoryPath",
      s3_output_path: "S3Uri",
      rule_evaluator_image: "AlgorithmImage", # required
      instance_type: "ml.t3.medium", # accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1,
      rule_parameters: {
        "ConfigKey" => "ConfigValue",
      },
    },
  ],
  environment: {
    "TrainingEnvironmentKey" => "TrainingEnvironmentValue",
  },
  retry_strategy: {
    maximum_retry_attempts: 1, # required
  },
  remote_debug_config: {
    enable_remote_debug: false,
  },
  infra_check_config: {
    enable_infra_check: false,
  },
  session_chaining_config: {
    enable_session_tag_chaining: false,
  },
})

Response structure


resp.training_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :training_job_name (required, String) —

    The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • :hyper_parameters (Hash<String,String>) —

    Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see [Algorithms].

    You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the ‘Length Constraint`.

    Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/algos.html

  • :algorithm_specification (required, Types::AlgorithmSpecification) —

    The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see [Algorithms]. For information about providing your own algorithms, see [Using Your Own Algorithms with Amazon SageMaker].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/algos.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.

    During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see [SageMaker Roles].

    <note markdown=“1”> To be able to pass this role to SageMaker, the caller of this API must have the ‘iam:PassRole` permission.

    </note>

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

  • :input_data_config (Array<Types::Channel>) —

    An array of ‘Channel` objects. Each channel is a named input source. `InputDataConfig` describes the input data and its location.

    Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, ‘training_data` and `validation_data`. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

    Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.

    Your input must be in the same Amazon Web Services region as your training job.

  • :output_data_config (required, Types::OutputDataConfig) —

    Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

  • :resource_config (required, Types::ResourceConfig) —

    The resources, including the ML compute instances and ML storage volumes, to use for model training.

    ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose ‘File` as the `TrainingInputMode` in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

  • :vpc_config (Types::VpcConfig) —

    A [VpcConfig] object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see [Protect Training Jobs by Using an Amazon Virtual Private Cloud].

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html

  • :stopping_condition (required, Types::StoppingCondition) —

    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

    To stop a job, SageMaker sends the algorithm the ‘SIGTERM` signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources].

    [1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

  • :enable_network_isolation (Boolean) —

    Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

  • :enable_inter_container_traffic_encryption (Boolean) —

    To encrypt all communications between ML compute instances in distributed training, choose ‘True`. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see [Protect Communications Between ML Compute Instances in a Distributed Training Job].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html

  • :enable_managed_spot_training (Boolean) —

    To train models using managed spot training, choose ‘True`. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.

    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.

  • :checkpoint_config (Types::CheckpointConfig) —

    Contains information about the output location for managed spot training checkpoint data.

  • :debug_hook_config (Types::DebugHookConfig) —

    Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the ‘DebugHookConfig` parameter, see [Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html

  • :debug_rule_configurations (Array<Types::DebugRuleConfiguration>) —

    Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

  • :tensor_board_output_config (Types::TensorBoardOutputConfig) —

    Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

  • :experiment_config (Types::ExperimentConfig) —

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob][1
    • CreateTrainingJob][2
    • CreateTransformJob][3

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html

  • :profiler_config (Types::ProfilerConfig) —

    Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

  • :profiler_rule_configurations (Array<Types::ProfilerRuleConfiguration>) —

    Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

  • :environment (Hash<String,String>) —

    The environment variables to set in the Docker container.

  • :retry_strategy (Types::RetryStrategy) —

    The number of times to retry the job when the job fails due to an ‘InternalServerError`.

  • :remote_debug_config (Types::RemoteDebugConfig) —

    Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see [Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/train-remote-debugging.html

  • :infra_check_config (Types::InfraCheckConfig) —

    Contains information about the infrastructure health check configuration for the training job.

  • :session_chaining_config (Types::SessionChainingConfig) —

    Contains information about attribute-based access control (ABAC) for the training job.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 8849

def create_training_job(params = {}, options = {})
  req = build_request(:create_training_job, params)
  req.send_request(options)
end

#create_transform_job(params = {}) ⇒ Types::CreateTransformJobResponse

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

  • ‘TransformJobName` - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • ‘ModelName` - Identifies the model to use. `ModelName` must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see [CreateModel].

  • ‘TransformInput` - Describes the dataset to be transformed and the Amazon S3 location where it is stored.

  • ‘TransformOutput` - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

  • ‘TransformResources` - Identifies the ML compute instances for the transform job.

For more information about how batch transformation works, see [Batch Transform].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html

Examples:

Request syntax with placeholder values


resp = client.create_transform_job({
  transform_job_name: "TransformJobName", # required
  model_name: "ModelName", # required
  max_concurrent_transforms: 1,
  model_client_config: {
    invocations_timeout_in_seconds: 1,
    invocations_max_retries: 1,
  },
  max_payload_in_mb: 1,
  batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord
  environment: {
    "TransformEnvironmentKey" => "TransformEnvironmentValue",
  },
  transform_input: { # required
    data_source: { # required
      s3_data_source: { # required
        s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
        s3_uri: "S3Uri", # required
      },
    },
    content_type: "ContentType",
    compression_type: "None", # accepts None, Gzip
    split_type: "None", # accepts None, Line, RecordIO, TFRecord
  },
  transform_output: { # required
    s3_output_path: "S3Uri", # required
    accept: "Accept",
    assemble_with: "None", # accepts None, Line
    kms_key_id: "KmsKeyId",
  },
  data_capture_config: {
    destination_s3_uri: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    generate_inference_id: false,
  },
  transform_resources: { # required
    instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
    instance_count: 1, # required
    volume_kms_key_id: "KmsKeyId",
  },
  data_processing: {
    input_filter: "JsonPath",
    output_filter: "JsonPath",
    join_source: "Input", # accepts Input, None
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
    run_name: "ExperimentEntityName",
  },
})

Response structure


resp.transform_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :transform_job_name (required, String) —

    The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • :model_name (required, String) —

    The name of the model that you want to use for the transform job. ‘ModelName` must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.

  • :max_concurrent_transforms (Integer) —

    The maximum number of parallel requests that can be sent to each instance in a transform job. If ‘MaxConcurrentTransforms` is set to `0` or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is `1`. For more information on execution-parameters, see [How Containers Serve Requests]. For built-in algorithms, you don’t need to set a value for ‘MaxConcurrentTransforms`.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests

  • :model_client_config (Types::ModelClientConfig) —

    Configures the timeout and maximum number of retries for processing a transform job invocation.

  • :max_payload_in_mb (Integer) —

    The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in ‘MaxPayloadInMB` must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is `6` MB.

    The value of ‘MaxPayloadInMB` cannot be greater than 100 MB. If you specify the `MaxConcurrentTransforms` parameter, the value of `(MaxConcurrentTransforms * MaxPayloadInMB)` also cannot exceed 100 MB.

    For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to ‘0`. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

  • :batch_strategy (String) —

    Specifies the number of records to include in a mini-batch for an HTTP inference request. A record ** is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

    To enable the batch strategy, you must set the ‘SplitType` property to `Line`, `RecordIO`, or `TFRecord`.

    To use only one record when making an HTTP invocation request to a container, set ‘BatchStrategy` to `SingleRecord` and `SplitType` to `Line`.

    To fit as many records in a mini-batch as can fit within the ‘MaxPayloadInMB` limit, set `BatchStrategy` to `MultiRecord` and `SplitType` to `Line`.

  • :environment (Hash<String,String>) —

    The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.

  • :transform_input (required, Types::TransformInput) —

    Describes the input source and the way the transform job consumes it.

  • :transform_output (required, Types::TransformOutput) —

    Describes the results of the transform job.

  • :data_capture_config (Types::BatchDataCaptureConfig) —

    Configuration to control how SageMaker captures inference data.

  • :transform_resources (required, Types::TransformResources) —

    Describes the resources, including ML instance types and ML instance count, to use for the transform job.

  • :data_processing (Types::DataProcessing) —

    The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see [Associate Prediction Results with their Corresponding Input Records].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html

  • :tags (Array<Types::Tag>) — default: Optional —

    An array of key-value pairs. For more information, see

    Using Cost Allocation Tags][1

    in the *Amazon Web Services Billing

    and Cost Management User Guide*.

    [1]: docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what

  • :experiment_config (Types::ExperimentConfig) —

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob][1
    • CreateTrainingJob][2
    • CreateTransformJob][3

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9083

def create_transform_job(params = {}, options = {})
  req = build_request(:create_transform_job, params)
  req.send_request(options)
end

#create_trial(params = {}) ⇒ Types::CreateTrialResponse

Creates an SageMaker trial. A trial is a set of steps called *trial components* that produce a machine learning model. A trial is part of a single SageMaker experiment.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to a trial and then use the [Search] API to search for the tags.

To get a list of all your trials, call the [ListTrials] API. To view a trial’s properties, call the [DescribeTrial] API. To create a trial component, call the [CreateTrialComponent] API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListTrials.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrial.html [4]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrialComponent.html

Examples:

Request syntax with placeholder values


resp = client.create_trial({
  trial_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  experiment_name: "ExperimentEntityName", # required
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_name (required, String) —

    The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • :display_name (String) —

    The name of the trial as displayed. The name doesn’t need to be unique. If ‘DisplayName` isn’t specified, ‘TrialName` is displayed.

  • :experiment_name (required, String) —

    The name of the experiment to associate the trial with.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :tags (Array<Types::Tag>) —

    A list of tags to associate with the trial. You can use [Search] API to search on the tags.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9165

def create_trial(params = {}, options = {})
  req = build_request(:create_trial, params)
  req.send_request(options)
end

#create_trial_component(params = {}) ⇒ Types::CreateTrialComponentResponse

Creates a *trial component*, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.

Trial components include pre-processing jobs, training jobs, and batch transform jobs.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to a trial component and then use the [Search] API to search for the tags.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html

Examples:

Request syntax with placeholder values


resp = client.create_trial_component({
  trial_component_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  status: {
    primary_status: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
    message: "TrialComponentStatusMessage",
  },
  start_time: Time.now,
  end_time: Time.now,
  parameters: {
    "TrialComponentKey320" => {
      string_value: "StringParameterValue",
      number_value: 1.0,
    },
  },
  input_artifacts: {
    "TrialComponentKey128" => {
      media_type: "MediaType",
      value: "TrialComponentArtifactValue", # required
    },
  },
  output_artifacts: {
    "TrialComponentKey128" => {
      media_type: "MediaType",
      value: "TrialComponentArtifactValue", # required
    },
  },
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.trial_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • :display_name (String) —

    The name of the component as displayed. The name doesn’t need to be unique. If ‘DisplayName` isn’t specified, ‘TrialComponentName` is displayed.

  • :status (Types::TrialComponentStatus) —

    The status of the component. States include:

    • InProgress

    • Completed

    • Failed

  • :start_time (Time, DateTime, Date, Integer, String) —

    When the component started.

  • :end_time (Time, DateTime, Date, Integer, String) —

    When the component ended.

  • :parameters (Hash<String,Types::TrialComponentParameterValue>) —

    The hyperparameters for the component.

  • :input_artifacts (Hash<String,Types::TrialComponentArtifact>) —

    The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.

  • :output_artifacts (Hash<String,Types::TrialComponentArtifact>) —

    The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :tags (Array<Types::Tag>) —

    A list of tags to associate with the component. You can use

    Search][1

    API to search on the tags.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9291

def create_trial_component(params = {}, options = {})
  req = build_request(:create_trial_component, params)
  req.send_request(options)
end

#create_user_profile(params = {}) ⇒ Types::CreateUserProfileResponse

Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a “person” for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user’s private Amazon Elastic File System home directory.

Examples:

Request syntax with placeholder values


resp = client.({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  single_sign_on_user_identifier: "SingleSignOnUserIdentifier",
  single_sign_on_user_value: "String256",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  user_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    sharing_settings: {
      notebook_output_option: "Allowed", # accepts Allowed, Disabled
      s3_output_path: "S3Uri",
      s3_kms_key_id: "KmsKeyId",
    },
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    r_studio_server_pro_app_settings: {
      access_status: "ENABLED", # accepts ENABLED, DISABLED
      user_group: "R_STUDIO_ADMIN", # accepts R_STUDIO_ADMIN, R_STUDIO_USER
    },
    r_session_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
    },
    canvas_app_settings: {
      time_series_forecasting_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        amazon_forecast_role_arn: "RoleArn",
      },
      model_register_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        cross_account_model_register_role_arn: "RoleArn",
      },
      workspace_settings: {
        s3_artifact_path: "S3Uri",
        s3_kms_key_id: "KmsKeyId",
      },
      identity_provider_o_auth_settings: [
        {
          data_source_name: "SalesforceGenie", # accepts SalesforceGenie, Snowflake
          status: "ENABLED", # accepts ENABLED, DISABLED
          secret_arn: "SecretArn",
        },
      ],
      direct_deploy_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      kendra_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      generative_ai_settings: {
        amazon_bedrock_role_arn: "RoleArn",
      },
      emr_serverless_settings: {
        execution_role_arn: "RoleArn",
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    default_landing_uri: "LandingUri",
    studio_web_portal: "ENABLED", # accepts ENABLED, DISABLED
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
      },
    ],
    studio_web_portal_settings: {
      hidden_ml_tools: ["DataWrangler"], # accepts DataWrangler, FeatureStore, EmrClusters, AutoMl, Experiments, Training, ModelEvaluation, Pipelines, Models, JumpStart, InferenceRecommender, Endpoints, Projects, InferenceOptimization
      hidden_app_types: ["JupyterServer"], # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
    },
    auto_mount_home_efs: "Enabled", # accepts Enabled, Disabled, DefaultAsDomain
  },
})

Response structure


resp. #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the associated Domain.

  • :user_profile_name (required, String) —

    A name for the UserProfile. This value is not case sensitive.

  • :single_sign_on_user_identifier (String) —

    A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is “UserName”. If the Domain’s AuthMode is IAM Identity Center, this field is required. If the Domain’s AuthMode is not IAM Identity Center, this field cannot be specified.

  • :single_sign_on_user_value (String) —

    The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain’s AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain’s AuthMode is not IAM Identity Center, this field cannot be specified.

  • :tags (Array<Types::Tag>) —

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

    Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.

  • :user_settings (Types::UserSettings) —

    A collection of settings.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9551

def (params = {}, options = {})
  req = build_request(:create_user_profile, params)
  req.send_request(options)
end

#create_workforce(params = {}) ⇒ Types::CreateWorkforceResponse

Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the [DeleteWorkforce] API operation to delete the existing workforce and then use ‘CreateWorkforce` to create a new workforce.

To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in ‘CognitoConfig`. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see [ Create a Private Workforce (Amazon Cognito)].

To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in ‘OidcConfig`. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see [ Create a Private Workforce (OIDC IdP)].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteWorkforce.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private.html [3]: docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private-oidc.html

Examples:

Request syntax with placeholder values


resp = client.create_workforce({
  cognito_config: {
    user_pool: "CognitoUserPool", # required
    client_id: "ClientId", # required
  },
  oidc_config: {
    client_id: "ClientId", # required
    client_secret: "ClientSecret", # required
    issuer: "OidcEndpoint", # required
    authorization_endpoint: "OidcEndpoint", # required
    token_endpoint: "OidcEndpoint", # required
    user_info_endpoint: "OidcEndpoint", # required
    logout_endpoint: "OidcEndpoint", # required
    jwks_uri: "OidcEndpoint", # required
    scope: "Scope",
    authentication_request_extra_params: {
      "AuthenticationRequestExtraParamsKey" => "AuthenticationRequestExtraParamsValue",
    },
  },
  source_ip_config: {
    cidrs: ["Cidr"], # required
  },
  workforce_name: "WorkforceName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  workforce_vpc_config: {
    vpc_id: "WorkforceVpcId",
    security_group_ids: ["WorkforceSecurityGroupId"],
    subnets: ["WorkforceSubnetId"],
  },
})

Response structure


resp.workforce_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cognito_config (Types::CognitoConfig) —

    Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single [ Amazon Cognito user pool].

    Do not use ‘OidcConfig` if you specify values for `CognitoConfig`.

    [1]: docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html

  • :oidc_config (Types::OidcConfig) —

    Use this parameter to configure a private workforce using your own OIDC Identity Provider.

    Do not use ‘CognitoConfig` if you specify values for `OidcConfig`.

  • :source_ip_config (Types::SourceIpConfig) —

    A list of IP address ranges ([CIDRs]). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn’t restricted to specific IP addresses.

    [1]: docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html

  • :workforce_name (required, String) —

    The name of the private workforce.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.

  • :workforce_vpc_config (Types::WorkforceVpcConfigRequest) —

    Use this parameter to configure a workforce using VPC.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9671

def create_workforce(params = {}, options = {})
  req = build_request(:create_workforce, params)
  req.send_request(options)
end

#create_workteam(params = {}) ⇒ Types::CreateWorkteamResponse

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.

You cannot create more than 25 work teams in an account and region.

Examples:

Request syntax with placeholder values


resp = client.create_workteam({
  workteam_name: "WorkteamName", # required
  workforce_name: "WorkforceName",
  member_definitions: [ # required
    {
      cognito_member_definition: {
        user_pool: "CognitoUserPool", # required
        user_group: "CognitoUserGroup", # required
        client_id: "ClientId", # required
      },
      oidc_member_definition: {
        groups: ["Group"],
      },
    },
  ],
  description: "String200", # required
  notification_configuration: {
    notification_topic_arn: "NotificationTopicArn",
  },
  worker_access_configuration: {
    s3_presign: {
      iam_policy_constraints: {
        source_ip: "Enabled", # accepts Enabled, Disabled
        vpc_source_ip: "Enabled", # accepts Enabled, Disabled
      },
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.workteam_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_name (required, String) —

    The name of the work team. Use this name to identify the work team.

  • :workforce_name (String) —

    The name of the workforce.

  • :member_definitions (required, Array<Types::MemberDefinition>) —

    A list of ‘MemberDefinition` objects that contains objects that identify the workers that make up the work team.

    Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use ‘CognitoMemberDefinition`. For workforces created using your own OIDC identity provider (IdP) use `OidcMemberDefinition`. Do not provide input for both of these parameters in a single request.

    For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito *user groups* within the user pool used to create a workforce. All of the ‘CognitoMemberDefinition` objects that make up the member definition must have the same `ClientId` and `UserPool` values. To add a Amazon Cognito user group to an existing worker pool, see [Adding groups to a User Pool](). For more information about user pools, see [Amazon Cognito User Pools].

    For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in ‘OidcMemberDefinition` by listing those groups in `Groups`.

    [1]: docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html

  • :description (required, String) —

    A description of the work team.

  • :notification_configuration (Types::NotificationConfiguration) —

    Configures notification of workers regarding available or expiring work items.

  • :worker_access_configuration (Types::WorkerAccessConfiguration) —

    Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs.

    For more information, see [Resource Tag] and [Using Cost Allocation Tags] in the Amazon Web Services Billing and Cost Management User Guide.

    [1]: docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-resource-tags.html [2]: docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9788

def create_workteam(params = {}, options = {})
  req = build_request(:create_workteam, params)
  req.send_request(options)
end

#delete_action(params = {}) ⇒ Types::DeleteActionResponse

Deletes an action.

Examples:

Request syntax with placeholder values


resp = client.delete_action({
  action_name: "ExperimentEntityName", # required
})

Response structure


resp.action_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :action_name (required, String) —

    The name of the action to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9816

def delete_action(params = {}, options = {})
  req = build_request(:delete_action, params)
  req.send_request(options)
end

#delete_algorithm(params = {}) ⇒ Struct

Removes the specified algorithm from your account.

Examples:

Request syntax with placeholder values


resp = client.delete_algorithm({
  algorithm_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :algorithm_name (required, String) —

    The name of the algorithm to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9838

def delete_algorithm(params = {}, options = {})
  req = build_request(:delete_algorithm, params)
  req.send_request(options)
end

#delete_app(params = {}) ⇒ Struct

Used to stop and delete an app.

Examples:

Request syntax with placeholder values


resp = client.delete_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName",
  space_name: "SpaceName",
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
  app_name: "AppName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (String) —

    The user profile name. If this value is not set, then ‘SpaceName` must be set.

  • :space_name (String) —

    The name of the space. If this value is not set, then ‘UserProfileName` must be set.

  • :app_type (required, String) —

    The type of app.

  • :app_name (required, String) —

    The name of the app.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9878

def delete_app(params = {}, options = {})
  req = build_request(:delete_app, params)
  req.send_request(options)
end

#delete_app_image_config(params = {}) ⇒ Struct

Deletes an AppImageConfig.

Examples:

Request syntax with placeholder values


resp = client.delete_app_image_config({
  app_image_config_name: "AppImageConfigName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :app_image_config_name (required, String) —

    The name of the AppImageConfig to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9900

def delete_app_image_config(params = {}, options = {})
  req = build_request(:delete_app_image_config, params)
  req.send_request(options)
end

#delete_artifact(params = {}) ⇒ Types::DeleteArtifactResponse

Deletes an artifact. Either ‘ArtifactArn` or `Source` must be specified.

Examples:

Request syntax with placeholder values


resp = client.delete_artifact({
  artifact_arn: "ArtifactArn",
  source: {
    source_uri: "SourceUri", # required
    source_types: [
      {
        source_id_type: "MD5Hash", # required, accepts MD5Hash, S3ETag, S3Version, Custom
        value: "String256", # required
      },
    ],
  },
})

Response structure


resp.artifact_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :artifact_arn (String) —

    The Amazon Resource Name (ARN) of the artifact to delete.

  • :source (Types::ArtifactSource) —

    The URI of the source.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9941

def delete_artifact(params = {}, options = {})
  req = build_request(:delete_artifact, params)
  req.send_request(options)
end

#delete_association(params = {}) ⇒ Types::DeleteAssociationResponse

Deletes an association.

Examples:

Request syntax with placeholder values


resp = client.delete_association({
  source_arn: "AssociationEntityArn", # required
  destination_arn: "AssociationEntityArn", # required
})

Response structure


resp.source_arn #=> String
resp.destination_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_arn (required, String) —

    The ARN of the source.

  • :destination_arn (required, String) —

    The Amazon Resource Name (ARN) of the destination.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 9975

def delete_association(params = {}, options = {})
  req = build_request(:delete_association, params)
  req.send_request(options)
end

#delete_cluster(params = {}) ⇒ Types::DeleteClusterResponse

Delete a SageMaker HyperPod cluster.

Examples:

Request syntax with placeholder values


resp = client.delete_cluster({
  cluster_name: "ClusterNameOrArn", # required
})

Response structure


resp.cluster_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10004

def delete_cluster(params = {}, options = {})
  req = build_request(:delete_cluster, params)
  req.send_request(options)
end

#delete_code_repository(params = {}) ⇒ Struct

Deletes the specified Git repository from your account.

Examples:

Request syntax with placeholder values


resp = client.delete_code_repository({
  code_repository_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :code_repository_name (required, String) —

    The name of the Git repository to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10026

def delete_code_repository(params = {}, options = {})
  req = build_request(:delete_code_repository, params)
  req.send_request(options)
end

#delete_compilation_job(params = {}) ⇒ Struct

Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn’t delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.

You can delete a compilation job only if its current status is ‘COMPLETED`, `FAILED`, or `STOPPED`. If the job status is `STARTING` or `INPROGRESS`, stop the job, and then delete it after its status becomes `STOPPED`.

Examples:

Request syntax with placeholder values


resp = client.delete_compilation_job({
  compilation_job_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :compilation_job_name (required, String) —

    The name of the compilation job to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10057

def delete_compilation_job(params = {}, options = {})
  req = build_request(:delete_compilation_job, params)
  req.send_request(options)
end

#delete_context(params = {}) ⇒ Types::DeleteContextResponse

Deletes an context.

Examples:

Request syntax with placeholder values


resp = client.delete_context({
  context_name: "ContextName", # required
})

Response structure


resp.context_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :context_name (required, String) —

    The name of the context to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10085

def delete_context(params = {}, options = {})
  req = build_request(:delete_context, params)
  req.send_request(options)
end

#delete_data_quality_job_definition(params = {}) ⇒ Struct

Deletes a data quality monitoring job definition.

Examples:

Request syntax with placeholder values


resp = client.delete_data_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the data quality monitoring job definition to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10107

def delete_data_quality_job_definition(params = {}, options = {})
  req = build_request(:delete_data_quality_job_definition, params)
  req.send_request(options)
end

#delete_device_fleet(params = {}) ⇒ Struct

Deletes a fleet.

Examples:

Request syntax with placeholder values


resp = client.delete_device_fleet({
  device_fleet_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10129

def delete_device_fleet(params = {}, options = {})
  req = build_request(:delete_device_fleet, params)
  req.send_request(options)
end

#delete_domain(params = {}) ⇒ Struct

Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.

Examples:

Request syntax with placeholder values


resp = client.delete_domain({
  domain_id: "DomainId", # required
  retention_policy: {
    home_efs_file_system: "Retain", # accepts Retain, Delete
  },
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :retention_policy (Types::RetentionPolicy) —

    The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10162

def delete_domain(params = {}, options = {})
  req = build_request(:delete_domain, params)
  req.send_request(options)
end

#delete_edge_deployment_plan(params = {}) ⇒ Struct

Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.

Examples:

Request syntax with placeholder values


resp = client.delete_edge_deployment_plan({
  edge_deployment_plan_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10185

def delete_edge_deployment_plan(params = {}, options = {})
  req = build_request(:delete_edge_deployment_plan, params)
  req.send_request(options)
end

#delete_edge_deployment_stage(params = {}) ⇒ Struct

Delete a stage in an edge deployment plan if (and only if) the stage is inactive.

Examples:

Request syntax with placeholder values


resp = client.delete_edge_deployment_stage({
  edge_deployment_plan_name: "EntityName", # required
  stage_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan from which the stage will be deleted.

  • :stage_name (required, String) —

    The name of the stage.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10213

def delete_edge_deployment_stage(params = {}, options = {})
  req = build_request(:delete_edge_deployment_stage, params)
  req.send_request(options)
end

#delete_endpoint(params = {}) ⇒ Struct

Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.

SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don’t need to use the [RevokeGrant] API call.

When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your ‘ ExecutionRoleArn `, otherwise SageMaker cannot delete these resources.

[1]: docs.aws.amazon.com/kms/latest/APIReference/API_RevokeGrant.html

Examples:

Request syntax with placeholder values


resp = client.delete_endpoint({
  endpoint_name: "EndpointName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (required, String) —

    The name of the endpoint that you want to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10250

def delete_endpoint(params = {}, options = {})
  req = build_request(:delete_endpoint, params)
  req.send_request(options)
end

#delete_endpoint_config(params = {}) ⇒ Struct

Deletes an endpoint configuration. The ‘DeleteEndpointConfig` API deletes only the specified configuration. It does not delete endpoints created using the configuration.

You must not delete an ‘EndpointConfig` in use by an endpoint that is live or while the `UpdateEndpoint` or `CreateEndpoint` operations are being performed on the endpoint. If you delete the `EndpointConfig` of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Examples:

Request syntax with placeholder values


resp = client.delete_endpoint_config({
  endpoint_config_name: "EndpointConfigName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_config_name (required, String) —

    The name of the endpoint configuration that you want to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10281

def delete_endpoint_config(params = {}, options = {})
  req = build_request(:delete_endpoint_config, params)
  req.send_request(options)
end

#delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse

Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the [ListTrials] API to get a list of the trials associated with the experiment.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListTrials.html

Examples:

Request syntax with placeholder values


resp = client.delete_experiment({
  experiment_name: "ExperimentEntityName", # required
})

Response structure


resp.experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10315

def delete_experiment(params = {}, options = {})
  req = build_request(:delete_experiment, params)
  req.send_request(options)
end

#delete_feature_group(params = {}) ⇒ Struct

Delete the ‘FeatureGroup` and any data that was written to the `OnlineStore` of the `FeatureGroup`. Data cannot be accessed from the `OnlineStore` immediately after `DeleteFeatureGroup` is called.

Data written into the ‘OfflineStore` will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your `OfflineStore` are not deleted.

Note that it can take approximately 10-15 minutes to delete an ‘OnlineStore` `FeatureGroup` with the `InMemory` `StorageType`.

Examples:

Request syntax with placeholder values


resp = client.delete_feature_group({
  feature_group_name: "FeatureGroupName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :feature_group_name (required, String) —

    The name of the ‘FeatureGroup` you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10348

def delete_feature_group(params = {}, options = {})
  req = build_request(:delete_feature_group, params)
  req.send_request(options)
end

#delete_flow_definition(params = {}) ⇒ Struct

Deletes the specified flow definition.

Examples:

Request syntax with placeholder values


resp = client.delete_flow_definition({
  flow_definition_name: "FlowDefinitionName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :flow_definition_name (required, String) —

    The name of the flow definition you are deleting.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10370

def delete_flow_definition(params = {}, options = {})
  req = build_request(:delete_flow_definition, params)
  req.send_request(options)
end

#delete_hub(params = {}) ⇒ Struct

Delete a hub.

Examples:

Request syntax with placeholder values


resp = client.delete_hub({
  hub_name: "HubNameOrArn", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10392

def delete_hub(params = {}, options = {})
  req = build_request(:delete_hub, params)
  req.send_request(options)
end

#delete_hub_content(params = {}) ⇒ Struct

Delete the contents of a hub.

Examples:

Request syntax with placeholder values


resp = client.delete_hub_content({
  hub_name: "HubNameOrArn", # required
  hub_content_type: "Model", # required, accepts Model, Notebook, ModelReference
  hub_content_name: "HubContentName", # required
  hub_content_version: "HubContentVersion", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub that you want to delete content in.

  • :hub_content_type (required, String) —

    The type of content that you want to delete from a hub.

  • :hub_content_name (required, String) —

    The name of the content that you want to delete from a hub.

  • :hub_content_version (required, String) —

    The version of the content that you want to delete from a hub.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10426

def delete_hub_content(params = {}, options = {})
  req = build_request(:delete_hub_content, params)
  req.send_request(options)
end

#delete_hub_content_reference(params = {}) ⇒ Struct

Delete a hub content reference in order to remove a model from a private hub.

Examples:

Request syntax with placeholder values


resp = client.delete_hub_content_reference({
  hub_name: "HubNameOrArn", # required
  hub_content_type: "Model", # required, accepts Model, Notebook, ModelReference
  hub_content_name: "HubContentName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to delete the hub content reference from.

  • :hub_content_type (required, String) —

    The type of hub content reference to delete. The only supported type of hub content reference to delete is ‘ModelReference`.

  • :hub_content_name (required, String) —

    The name of the hub content to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10458

def delete_hub_content_reference(params = {}, options = {})
  req = build_request(:delete_hub_content_reference, params)
  req.send_request(options)
end

#delete_human_task_ui(params = {}) ⇒ Struct

Use this operation to delete a human task user interface (worker task template).

To see a list of human task user interfaces (work task templates) in your account, use [ListHumanTaskUis]. When you delete a worker task template, it no longer appears when you call ‘ListHumanTaskUis`.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListHumanTaskUis.html

Examples:

Request syntax with placeholder values


resp = client.delete_human_task_ui({
  human_task_ui_name: "HumanTaskUiName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :human_task_ui_name (required, String) —

    The name of the human task user interface (work task template) you want to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10490

def delete_human_task_ui(params = {}, options = {})
  req = build_request(:delete_human_task_ui, params)
  req.send_request(options)
end

#delete_hyper_parameter_tuning_job(params = {}) ⇒ Struct

Deletes a hyperparameter tuning job. The ‘DeleteHyperParameterTuningJob` API deletes only the tuning job entry that was created in SageMaker when you called the `CreateHyperParameterTuningJob` API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.

Examples:

Request syntax with placeholder values


resp = client.delete_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hyper_parameter_tuning_job_name (required, String) —

    The name of the hyperparameter tuning job that you want to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10516

def delete_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:delete_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#delete_image(params = {}) ⇒ Struct

Deletes a SageMaker image and all versions of the image. The container images aren’t deleted.

Examples:

Request syntax with placeholder values


resp = client.delete_image({
  image_name: "ImageName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image_name (required, String) —

    The name of the image to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10539

def delete_image(params = {}, options = {})
  req = build_request(:delete_image, params)
  req.send_request(options)
end

#delete_image_version(params = {}) ⇒ Struct

Deletes a version of a SageMaker image. The container image the version represents isn’t deleted.

Examples:

Request syntax with placeholder values


resp = client.delete_image_version({
  image_name: "ImageName", # required
  version: 1,
  alias: "SageMakerImageVersionAlias",
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image_name (required, String) —

    The name of the image to delete.

  • :version (Integer) —

    The version to delete.

  • :alias (String) —

    The alias of the image to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10570

def delete_image_version(params = {}, options = {})
  req = build_request(:delete_image_version, params)
  req.send_request(options)
end

#delete_inference_component(params = {}) ⇒ Struct

Deletes an inference component.

Examples:

Request syntax with placeholder values


resp = client.delete_inference_component({
  inference_component_name: "InferenceComponentName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :inference_component_name (required, String) —

    The name of the inference component to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10592

def delete_inference_component(params = {}, options = {})
  req = build_request(:delete_inference_component, params)
  req.send_request(options)
end

#delete_inference_experiment(params = {}) ⇒ Types::DeleteInferenceExperimentResponse

Deletes an inference experiment.

<note markdown=“1”> This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.

</note>

Examples:

Request syntax with placeholder values


resp = client.delete_inference_experiment({
  name: "InferenceExperimentName", # required
})

Response structure


resp.inference_experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the inference experiment you want to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10626

def delete_inference_experiment(params = {}, options = {})
  req = build_request(:delete_inference_experiment, params)
  req.send_request(options)
end

#delete_mlflow_tracking_server(params = {}) ⇒ Types::DeleteMlflowTrackingServerResponse

Deletes an MLflow Tracking Server. For more information, see [Clean up MLflow resources].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/mlflow-cleanup.html.html

Examples:

Request syntax with placeholder values


resp = client.delete_mlflow_tracking_server({
  tracking_server_name: "TrackingServerName", # required
})

Response structure


resp.tracking_server_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    The name of the the tracking server to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10659

def delete_mlflow_tracking_server(params = {}, options = {})
  req = build_request(:delete_mlflow_tracking_server, params)
  req.send_request(options)
end

#delete_model(params = {}) ⇒ Struct

Deletes a model. The ‘DeleteModel` API deletes only the model entry that was created in SageMaker when you called the `CreateModel` API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

Examples:

Request syntax with placeholder values


resp = client.delete_model({
  model_name: "ModelName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_name (required, String) —

    The name of the model to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10684

def delete_model(params = {}, options = {})
  req = build_request(:delete_model, params)
  req.send_request(options)
end

#delete_model_bias_job_definition(params = {}) ⇒ Struct

Deletes an Amazon SageMaker model bias job definition.

Examples:

Request syntax with placeholder values


resp = client.delete_model_bias_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the model bias job definition to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10706

def delete_model_bias_job_definition(params = {}, options = {})
  req = build_request(:delete_model_bias_job_definition, params)
  req.send_request(options)
end

#delete_model_card(params = {}) ⇒ Struct

Deletes an Amazon SageMaker Model Card.

Examples:

Request syntax with placeholder values


resp = client.delete_model_card({
  model_card_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_name (required, String) —

    The name of the model card to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10728

def delete_model_card(params = {}, options = {})
  req = build_request(:delete_model_card, params)
  req.send_request(options)
end

#delete_model_explainability_job_definition(params = {}) ⇒ Struct

Deletes an Amazon SageMaker model explainability job definition.

Examples:

Request syntax with placeholder values


resp = client.delete_model_explainability_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the model explainability job definition to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10750

def delete_model_explainability_job_definition(params = {}, options = {})
  req = build_request(:delete_model_explainability_job_definition, params)
  req.send_request(options)
end

#delete_model_package(params = {}) ⇒ Struct

Deletes a model package.

A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

Examples:

Request syntax with placeholder values


resp = client.delete_model_package({
  model_package_name: "VersionedArnOrName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_name (required, String) —

    The name or Amazon Resource Name (ARN) of the model package to delete.

    When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10780

def delete_model_package(params = {}, options = {})
  req = build_request(:delete_model_package, params)
  req.send_request(options)
end

#delete_model_package_group(params = {}) ⇒ Struct

Deletes the specified model group.

Examples:

Request syntax with placeholder values


resp = client.delete_model_package_group({
  model_package_group_name: "ArnOrName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_group_name (required, String) —

    The name of the model group to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10802

def delete_model_package_group(params = {}, options = {})
  req = build_request(:delete_model_package_group, params)
  req.send_request(options)
end

#delete_model_package_group_policy(params = {}) ⇒ Struct

Deletes a model group resource policy.

Examples:

Request syntax with placeholder values


resp = client.delete_model_package_group_policy({
  model_package_group_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_group_name (required, String) —

    The name of the model group for which to delete the policy.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10824

def delete_model_package_group_policy(params = {}, options = {})
  req = build_request(:delete_model_package_group_policy, params)
  req.send_request(options)
end

#delete_model_quality_job_definition(params = {}) ⇒ Struct

Deletes the secified model quality monitoring job definition.

Examples:

Request syntax with placeholder values


resp = client.delete_model_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the model quality monitoring job definition to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10846

def delete_model_quality_job_definition(params = {}, options = {})
  req = build_request(:delete_model_quality_job_definition, params)
  req.send_request(options)
end

#delete_monitoring_schedule(params = {}) ⇒ Struct

Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.

Examples:

Request syntax with placeholder values


resp = client.delete_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of the monitoring schedule to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10870

def delete_monitoring_schedule(params = {}, options = {})
  req = build_request(:delete_monitoring_schedule, params)
  req.send_request(options)
end

#delete_notebook_instance(params = {}) ⇒ Struct

Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the ‘StopNotebookInstance` API.

When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Examples:

Request syntax with placeholder values


resp = client.delete_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the SageMaker notebook instance to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10898

def delete_notebook_instance(params = {}, options = {})
  req = build_request(:delete_notebook_instance, params)
  req.send_request(options)
end

#delete_notebook_instance_lifecycle_config(params = {}) ⇒ Struct

Deletes a notebook instance lifecycle configuration.

Examples:

Request syntax with placeholder values


resp = client.delete_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_lifecycle_config_name (required, String) —

    The name of the lifecycle configuration to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10920

def delete_notebook_instance_lifecycle_config(params = {}, options = {})
  req = build_request(:delete_notebook_instance_lifecycle_config, params)
  req.send_request(options)
end

#delete_optimization_job(params = {}) ⇒ Struct

Deletes an optimization job.

Examples:

Request syntax with placeholder values


resp = client.delete_optimization_job({
  optimization_job_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :optimization_job_name (required, String) —

    The name that you assigned to the optimization job.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10942

def delete_optimization_job(params = {}, options = {})
  req = build_request(:delete_optimization_job, params)
  req.send_request(options)
end

#delete_pipeline(params = {}) ⇒ Types::DeletePipelineResponse

Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the ‘StopPipelineExecution` API. When you delete a pipeline, all instances of the pipeline are deleted.

Examples:

Request syntax with placeholder values


resp = client.delete_pipeline({
  pipeline_name: "PipelineName", # required
  client_request_token: "IdempotencyToken", # required
})

Response structure


resp.pipeline_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name (required, String) —

    The name of the pipeline to delete.

  • :client_request_token (required, String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 10982

def delete_pipeline(params = {}, options = {})
  req = build_request(:delete_pipeline, params)
  req.send_request(options)
end

#delete_project(params = {}) ⇒ Struct

Delete the specified project.

Examples:

Request syntax with placeholder values


resp = client.delete_project({
  project_name: "ProjectEntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_name (required, String) —

    The name of the project to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11004

def delete_project(params = {}, options = {})
  req = build_request(:delete_project, params)
  req.send_request(options)
end

#delete_space(params = {}) ⇒ Struct

Used to delete a space.

Examples:

Request syntax with placeholder values


resp = client.delete_space({
  domain_id: "DomainId", # required
  space_name: "SpaceName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the associated domain.

  • :space_name (required, String) —

    The name of the space.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11030

def delete_space(params = {}, options = {})
  req = build_request(:delete_space, params)
  req.send_request(options)
end

#delete_studio_lifecycle_config(params = {}) ⇒ Struct

Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.

Examples:

Request syntax with placeholder values


resp = client.delete_studio_lifecycle_config({
  studio_lifecycle_config_name: "StudioLifecycleConfigName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :studio_lifecycle_config_name (required, String) —

    The name of the Amazon SageMaker Studio Lifecycle Configuration to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11056

def delete_studio_lifecycle_config(params = {}, options = {})
  req = build_request(:delete_studio_lifecycle_config, params)
  req.send_request(options)
end

#delete_tags(params = {}) ⇒ Struct

Deletes the specified tags from an SageMaker resource.

To list a resource’s tags, use the ‘ListTags` API.

<note markdown=“1”> When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

</note>

<note markdown=“1”> When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.

</note>

Examples:

Request syntax with placeholder values


resp = client.delete_tags({
  resource_arn: "ResourceArn", # required
  tag_keys: ["TagKey"], # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :resource_arn (required, String) —

    The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

  • :tag_keys (required, Array<String>) —

    An array or one or more tag keys to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11097

def delete_tags(params = {}, options = {})
  req = build_request(:delete_tags, params)
  req.send_request(options)
end

#delete_trial(params = {}) ⇒ Types::DeleteTrialResponse

Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the [DescribeTrialComponent] API to get the list of trial components.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrialComponent.html

Examples:

Request syntax with placeholder values


resp = client.delete_trial({
  trial_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_name (required, String) —

    The name of the trial to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11131

def delete_trial(params = {}, options = {})
  req = build_request(:delete_trial, params)
  req.send_request(options)
end

#delete_trial_component(params = {}) ⇒ Types::DeleteTrialComponentResponse

Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the

DisassociateTrialComponent][1

API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DisassociateTrialComponent.html

Examples:

Request syntax with placeholder values


resp = client.delete_trial_component({
  trial_component_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11166

def delete_trial_component(params = {}, options = {})
  req = build_request(:delete_trial_component, params)
  req.send_request(options)
end

#delete_user_profile(params = {}) ⇒ Struct

Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.

Examples:

Request syntax with placeholder values


resp = client.({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11194

def (params = {}, options = {})
  req = build_request(:delete_user_profile, params)
  req.send_request(options)
end

#delete_workforce(params = {}) ⇒ Struct

Use this operation to delete a workforce.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use [CreateWorkforce] to create a new workforce.

If a private workforce contains one or more work teams, you must use the [DeleteWorkteam] operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ‘ResourceInUse` error.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateWorkforce.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteWorkteam.html

Examples:

Request syntax with placeholder values


resp = client.delete_workforce({
  workforce_name: "WorkforceName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workforce_name (required, String) —

    The name of the workforce.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11231

def delete_workforce(params = {}, options = {})
  req = build_request(:delete_workforce, params)
  req.send_request(options)
end

#delete_workteam(params = {}) ⇒ Types::DeleteWorkteamResponse

Deletes an existing work team. This operation can’t be undone.

Examples:

Request syntax with placeholder values


resp = client.delete_workteam({
  workteam_name: "WorkteamName", # required
})

Response structure


resp.success #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_name (required, String) —

    The name of the work team to delete.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11259

def delete_workteam(params = {}, options = {})
  req = build_request(:delete_workteam, params)
  req.send_request(options)
end

#deregister_devices(params = {}) ⇒ Struct

Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.

Examples:

Request syntax with placeholder values


resp = client.deregister_devices({
  device_fleet_name: "EntityName", # required
  device_names: ["DeviceName"], # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet the devices belong to.

  • :device_names (required, Array<String>) —

    The unique IDs of the devices.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11286

def deregister_devices(params = {}, options = {})
  req = build_request(:deregister_devices, params)
  req.send_request(options)
end

#describe_action(params = {}) ⇒ Types::DescribeActionResponse

Describes an action.

Examples:

Request syntax with placeholder values


resp = client.describe_action({
  action_name: "ExperimentEntityNameOrArn", # required
})

Response structure


resp.action_name #=> String
resp.action_arn #=> String
resp.source.source_uri #=> String
resp.source.source_type #=> String
resp.source.source_id #=> String
resp.action_type #=> String
resp.description #=> String
resp.status #=> String, one of "Unknown", "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.properties #=> Hash
resp.properties["StringParameterValue"] #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.metadata_properties.commit_id #=> String
resp.metadata_properties.repository #=> String
resp.metadata_properties.generated_by #=> String
resp.metadata_properties.project_id #=> String
resp.lineage_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :action_name (required, String) —

    The name of the action to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11354

def describe_action(params = {}, options = {})
  req = build_request(:describe_action, params)
  req.send_request(options)
end

#describe_algorithm(params = {}) ⇒ Types::DescribeAlgorithmOutput

Returns a description of the specified algorithm that is in your account.

Examples:

Request syntax with placeholder values


resp = client.describe_algorithm({
  algorithm_name: "ArnOrName", # required
})

Response structure


resp.algorithm_name #=> String
resp.algorithm_arn #=> String
resp.algorithm_description #=> String
resp.creation_time #=> Time
resp.training_specification.training_image #=> String
resp.training_specification.training_image_digest #=> String
resp.training_specification.supported_hyper_parameters #=> Array
resp.training_specification.supported_hyper_parameters[0].name #=> String
resp.training_specification.supported_hyper_parameters[0].description #=> String
resp.training_specification.supported_hyper_parameters[0].type #=> String, one of "Integer", "Continuous", "Categorical", "FreeText"
resp.training_specification.supported_hyper_parameters[0].range.integer_parameter_range_specification.min_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.integer_parameter_range_specification.max_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.continuous_parameter_range_specification.min_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.continuous_parameter_range_specification.max_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.categorical_parameter_range_specification.values #=> Array
resp.training_specification.supported_hyper_parameters[0].range.categorical_parameter_range_specification.values[0] #=> String
resp.training_specification.supported_hyper_parameters[0].is_tunable #=> Boolean
resp.training_specification.supported_hyper_parameters[0].is_required #=> Boolean
resp.training_specification.supported_hyper_parameters[0].default_value #=> String
resp.training_specification.supported_training_instance_types #=> Array
resp.training_specification.supported_training_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_specification.supports_distributed_training #=> Boolean
resp.training_specification.metric_definitions #=> Array
resp.training_specification.metric_definitions[0].name #=> String
resp.training_specification.metric_definitions[0].regex #=> String
resp.training_specification.training_channels #=> Array
resp.training_specification.training_channels[0].name #=> String
resp.training_specification.training_channels[0].description #=> String
resp.training_specification.training_channels[0].is_required #=> Boolean
resp.training_specification.training_channels[0].supported_content_types #=> Array
resp.training_specification.training_channels[0].supported_content_types[0] #=> String
resp.training_specification.training_channels[0].supported_compression_types #=> Array
resp.training_specification.training_channels[0].supported_compression_types[0] #=> String, one of "None", "Gzip"
resp.training_specification.training_channels[0].supported_input_modes #=> Array
resp.training_specification.training_channels[0].supported_input_modes[0] #=> String, one of "Pipe", "File", "FastFile"
resp.training_specification.supported_tuning_job_objective_metrics #=> Array
resp.training_specification.supported_tuning_job_objective_metrics[0].type #=> String, one of "Maximize", "Minimize"
resp.training_specification.supported_tuning_job_objective_metrics[0].metric_name #=> String
resp.training_specification.additional_s3_data_source.s3_data_type #=> String, one of "S3Object", "S3Prefix"
resp.training_specification.additional_s3_data_source.s3_uri #=> String
resp.training_specification.additional_s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.inference_specification.containers #=> Array
resp.inference_specification.containers[0].container_hostname #=> String
resp.inference_specification.containers[0].image #=> String
resp.inference_specification.containers[0].image_digest #=> String
resp.inference_specification.containers[0].model_data_url #=> String
resp.inference_specification.containers[0].model_data_source.s3_data_source.s3_uri #=> String
resp.inference_specification.containers[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.inference_specification.containers[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.inference_specification.containers[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.inference_specification.containers[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.inference_specification.containers[0].product_id #=> String
resp.inference_specification.containers[0].environment #=> Hash
resp.inference_specification.containers[0].environment["EnvironmentKey"] #=> String
resp.inference_specification.containers[0].model_input.data_input_config #=> String
resp.inference_specification.containers[0].framework #=> String
resp.inference_specification.containers[0].framework_version #=> String
resp.inference_specification.containers[0].nearest_model_name #=> String
resp.inference_specification.containers[0].additional_s3_data_source.s3_data_type #=> String, one of "S3Object", "S3Prefix"
resp.inference_specification.containers[0].additional_s3_data_source.s3_uri #=> String
resp.inference_specification.containers[0].additional_s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.inference_specification.supported_transform_instance_types #=> Array
resp.inference_specification.supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.inference_specification.supported_realtime_inference_instance_types #=> Array
resp.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.inference_specification.supported_content_types #=> Array
resp.inference_specification.supported_content_types[0] #=> String
resp.inference_specification.supported_response_mime_types #=> Array
resp.inference_specification.supported_response_mime_types[0] #=> String
resp.validation_specification.validation_role #=> String
resp.validation_specification.validation_profiles #=> Array
resp.validation_specification.validation_profiles[0].profile_name #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.training_input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.validation_specification.validation_profiles[0].training_job_definition.hyper_parameters #=> Hash
resp.validation_specification.validation_profiles[0].training_job_definition.hyper_parameters["HyperParameterKey"] #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config #=> Array
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].channel_name #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.instance_group_names #=> Array
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.instance_group_names[0] #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].content_type #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].shuffle_config.seed #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.kms_key_id #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.s3_output_path #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.compression_type #=> String, one of "GZIP", "NONE"
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_count #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.volume_size_in_gb #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.volume_kms_key_id #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.keep_alive_period_in_seconds #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_groups #=> Array
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_groups[0].instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_groups[0].instance_count #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_groups[0].instance_group_name #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_pending_time_in_seconds #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash
resp.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String
resp.algorithm_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.algorithm_status_details.validation_statuses #=> Array
resp.algorithm_status_details.validation_statuses[0].name #=> String
resp.algorithm_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.algorithm_status_details.validation_statuses[0].failure_reason #=> String
resp.algorithm_status_details.image_scan_statuses #=> Array
resp.algorithm_status_details.image_scan_statuses[0].name #=> String
resp.algorithm_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.algorithm_status_details.image_scan_statuses[0].failure_reason #=> String
resp.product_id #=> String
resp.certify_for_marketplace #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :algorithm_name (required, String) —

    The name of the algorithm to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11528

def describe_algorithm(params = {}, options = {})
  req = build_request(:describe_algorithm, params)
  req.send_request(options)
end

#describe_app(params = {}) ⇒ Types::DescribeAppResponse

Describes the app.

Examples:

Request syntax with placeholder values


resp = client.describe_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName",
  space_name: "SpaceName",
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
  app_name: "AppName", # required
})

Response structure


resp.app_arn #=> String
resp.app_type #=> String, one of "JupyterServer", "KernelGateway", "DetailedProfiler", "TensorBoard", "CodeEditor", "JupyterLab", "RStudioServerPro", "RSessionGateway", "Canvas"
resp.app_name #=> String
resp.domain_id #=> String
resp. #=> String
resp.space_name #=> String
resp.status #=> String, one of "Deleted", "Deleting", "Failed", "InService", "Pending"
resp.last_health_check_timestamp #=> Time
resp.last_user_activity_timestamp #=> Time
resp.creation_time #=> Time
resp.failure_reason #=> String
resp.resource_spec.sage_maker_image_arn #=> String
resp.resource_spec.sage_maker_image_version_arn #=> String
resp.resource_spec.sage_maker_image_version_alias #=> String
resp.resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.resource_spec.lifecycle_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (String) —

    The user profile name. If this value is not set, then ‘SpaceName` must be set.

  • :space_name (String) —

    The name of the space.

  • :app_type (required, String) —

    The type of app.

  • :app_name (required, String) —

    The name of the app.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11599

def describe_app(params = {}, options = {})
  req = build_request(:describe_app, params)
  req.send_request(options)
end

#describe_app_image_config(params = {}) ⇒ Types::DescribeAppImageConfigResponse

Describes an AppImageConfig.

Examples:

Request syntax with placeholder values


resp = client.describe_app_image_config({
  app_image_config_name: "AppImageConfigName", # required
})

Response structure


resp.app_image_config_arn #=> String
resp.app_image_config_name #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.kernel_gateway_image_config.kernel_specs #=> Array
resp.kernel_gateway_image_config.kernel_specs[0].name #=> String
resp.kernel_gateway_image_config.kernel_specs[0].display_name #=> String
resp.kernel_gateway_image_config.file_system_config.mount_path #=> String
resp.kernel_gateway_image_config.file_system_config.default_uid #=> Integer
resp.kernel_gateway_image_config.file_system_config.default_gid #=> Integer
resp.jupyter_lab_app_image_config.file_system_config.mount_path #=> String
resp.jupyter_lab_app_image_config.file_system_config.default_uid #=> Integer
resp.jupyter_lab_app_image_config.file_system_config.default_gid #=> Integer
resp.jupyter_lab_app_image_config.container_config.container_arguments #=> Array
resp.jupyter_lab_app_image_config.container_config.container_arguments[0] #=> String
resp.jupyter_lab_app_image_config.container_config.container_entrypoint #=> Array
resp.jupyter_lab_app_image_config.container_config.container_entrypoint[0] #=> String
resp.jupyter_lab_app_image_config.container_config.container_environment_variables #=> Hash
resp.jupyter_lab_app_image_config.container_config.container_environment_variables["NonEmptyString256"] #=> String
resp.code_editor_app_image_config.file_system_config.mount_path #=> String
resp.code_editor_app_image_config.file_system_config.default_uid #=> Integer
resp.code_editor_app_image_config.file_system_config.default_gid #=> Integer
resp.code_editor_app_image_config.container_config.container_arguments #=> Array
resp.code_editor_app_image_config.container_config.container_arguments[0] #=> String
resp.code_editor_app_image_config.container_config.container_entrypoint #=> Array
resp.code_editor_app_image_config.container_config.container_entrypoint[0] #=> String
resp.code_editor_app_image_config.container_config.container_environment_variables #=> Hash
resp.code_editor_app_image_config.container_config.container_environment_variables["NonEmptyString256"] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :app_image_config_name (required, String) —

    The name of the AppImageConfig to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11660

def describe_app_image_config(params = {}, options = {})
  req = build_request(:describe_app_image_config, params)
  req.send_request(options)
end

#describe_artifact(params = {}) ⇒ Types::DescribeArtifactResponse

Describes an artifact.

Examples:

Request syntax with placeholder values


resp = client.describe_artifact({
  artifact_arn: "ArtifactArn", # required
})

Response structure


resp.artifact_name #=> String
resp.artifact_arn #=> String
resp.source.source_uri #=> String
resp.source.source_types #=> Array
resp.source.source_types[0].source_id_type #=> String, one of "MD5Hash", "S3ETag", "S3Version", "Custom"
resp.source.source_types[0].value #=> String
resp.artifact_type #=> String
resp.properties #=> Hash
resp.properties["StringParameterValue"] #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.metadata_properties.commit_id #=> String
resp.metadata_properties.repository #=> String
resp.metadata_properties.generated_by #=> String
resp.metadata_properties.project_id #=> String
resp.lineage_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :artifact_arn (required, String) —

    The Amazon Resource Name (ARN) of the artifact to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11725

def describe_artifact(params = {}, options = {})
  req = build_request(:describe_artifact, params)
  req.send_request(options)
end

#describe_auto_ml_job(params = {}) ⇒ Types::DescribeAutoMLJobResponse

Returns information about an AutoML job created by calling [CreateAutoMLJob].

<note markdown=“1”> AutoML jobs created by calling [CreateAutoMLJobV2] cannot be described by ‘DescribeAutoMLJob`.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html

Examples:

Request syntax with placeholder values


resp = client.describe_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
})

Response structure


resp.auto_ml_job_name #=> String
resp.auto_ml_job_arn #=> String
resp.input_data_config #=> Array
resp.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.input_data_config[0].target_attribute_name #=> String
resp.input_data_config[0].content_type #=> String
resp.input_data_config[0].channel_type #=> String, one of "training", "validation"
resp.input_data_config[0].sample_weight_attribute_name #=> String
resp.output_data_config.kms_key_id #=> String
resp.output_data_config.s3_output_path #=> String
resp.role_arn #=> String
resp.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.auto_ml_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_job_config.security_config.volume_kms_key_id #=> String
resp.auto_ml_job_config.security_config.enable_inter_container_traffic_encryption #=> Boolean
resp.auto_ml_job_config.security_config.vpc_config.security_group_ids #=> Array
resp.auto_ml_job_config.security_config.vpc_config.security_group_ids[0] #=> String
resp.auto_ml_job_config.security_config.vpc_config.subnets #=> Array
resp.auto_ml_job_config.security_config.vpc_config.subnets[0] #=> String
resp.auto_ml_job_config.candidate_generation_config.feature_specification_s3_uri #=> String
resp.auto_ml_job_config.candidate_generation_config.algorithms_config #=> Array
resp.auto_ml_job_config.candidate_generation_config.algorithms_config[0].auto_ml_algorithms #=> Array
resp.auto_ml_job_config.candidate_generation_config.algorithms_config[0].auto_ml_algorithms[0] #=> String, one of "xgboost", "linear-learner", "mlp", "lightgbm", "catboost", "randomforest", "extra-trees", "nn-torch", "fastai", "cnn-qr", "deepar", "prophet", "npts", "arima", "ets"
resp.auto_ml_job_config.data_split_config.validation_fraction #=> Float
resp.auto_ml_job_config.mode #=> String, one of "AUTO", "ENSEMBLING", "HYPERPARAMETER_TUNING"
resp.creation_time #=> Time
resp.end_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.partial_failure_reasons #=> Array
resp.partial_failure_reasons[0].partial_failure_message #=> String
resp.best_candidate.candidate_name #=> String
resp.best_candidate.final_auto_ml_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.best_candidate.final_auto_ml_job_objective_metric.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.best_candidate.final_auto_ml_job_objective_metric.value #=> Float
resp.best_candidate.final_auto_ml_job_objective_metric.standard_metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.best_candidate.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.best_candidate.candidate_steps #=> Array
resp.best_candidate.candidate_steps[0].candidate_step_type #=> String, one of "AWS::SageMaker::TrainingJob", "AWS::SageMaker::TransformJob", "AWS::SageMaker::ProcessingJob"
resp.best_candidate.candidate_steps[0].candidate_step_arn #=> String
resp.best_candidate.candidate_steps[0].candidate_step_name #=> String
resp.best_candidate.candidate_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.best_candidate.inference_containers #=> Array
resp.best_candidate.inference_containers[0].image #=> String
resp.best_candidate.inference_containers[0].model_data_url #=> String
resp.best_candidate.inference_containers[0].environment #=> Hash
resp.best_candidate.inference_containers[0].environment["EnvironmentKey"] #=> String
resp.best_candidate.creation_time #=> Time
resp.best_candidate.end_time #=> Time
resp.best_candidate.last_modified_time #=> Time
resp.best_candidate.failure_reason #=> String
resp.best_candidate.candidate_properties.candidate_artifact_locations.explainability #=> String
resp.best_candidate.candidate_properties.candidate_artifact_locations.model_insights #=> String
resp.best_candidate.candidate_properties.candidate_artifact_locations.backtest_results #=> String
resp.best_candidate.candidate_properties.candidate_metrics #=> Array
resp.best_candidate.candidate_properties.candidate_metrics[0].metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.best_candidate.candidate_properties.candidate_metrics[0].value #=> Float
resp.best_candidate.candidate_properties.candidate_metrics[0].set #=> String, one of "Train", "Validation", "Test"
resp.best_candidate.candidate_properties.candidate_metrics[0].standard_metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "MAE", "R2", "BalancedAccuracy", "Precision", "PrecisionMacro", "Recall", "RecallMacro", "LogLoss", "InferenceLatency", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss", "Rouge1", "Rouge2", "RougeL", "RougeLSum", "Perplexity", "ValidationLoss", "TrainingLoss"
resp.best_candidate.inference_container_definitions #=> Hash
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"] #=> Array
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].image #=> String
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].model_data_url #=> String
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].environment #=> Hash
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].environment["EnvironmentKey"] #=> String
resp.auto_ml_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.auto_ml_job_secondary_status #=> String, one of "Starting", "MaxCandidatesReached", "Failed", "Stopped", "MaxAutoMLJobRuntimeReached", "Stopping", "CandidateDefinitionsGenerated", "Completed", "ExplainabilityError", "DeployingModel", "ModelDeploymentError", "GeneratingModelInsightsReport", "ModelInsightsError", "AnalyzingData", "FeatureEngineering", "ModelTuning", "GeneratingExplainabilityReport", "TrainingModels", "PreTraining"
resp.generate_candidate_definitions_only #=> Boolean
resp.auto_ml_job_artifacts.candidate_definition_notebook_location #=> String
resp.auto_ml_job_artifacts.data_exploration_notebook_location #=> String
resp.resolved_attributes.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.resolved_attributes.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.resolved_attributes.completion_criteria.max_candidates #=> Integer
resp.resolved_attributes.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.resolved_attributes.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.model_deploy_config.auto_generate_endpoint_name #=> Boolean
resp.model_deploy_config.endpoint_name #=> String
resp.model_deploy_result.endpoint_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    Requests information about an AutoML job using its unique name.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 11866

def describe_auto_ml_job(params = {}, options = {})
  req = build_request(:describe_auto_ml_job, params)
  req.send_request(options)
end

#describe_auto_ml_job_v2(params = {}) ⇒ Types::DescribeAutoMLJobV2Response

Returns information about an AutoML job created by calling

CreateAutoMLJobV2][1

or [CreateAutoMLJob].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html

Examples:

Request syntax with placeholder values


resp = client.describe_auto_ml_job_v2({
  auto_ml_job_name: "AutoMLJobName", # required
})

Response structure


resp.auto_ml_job_name #=> String
resp.auto_ml_job_arn #=> String
resp.auto_ml_job_input_data_config #=> Array
resp.auto_ml_job_input_data_config[0].channel_type #=> String, one of "training", "validation"
resp.auto_ml_job_input_data_config[0].content_type #=> String
resp.auto_ml_job_input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.auto_ml_job_input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.auto_ml_job_input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.output_data_config.kms_key_id #=> String
resp.output_data_config.s3_output_path #=> String
resp.role_arn #=> String
resp.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.auto_ml_problem_type_config.image_classification_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_problem_type_config.image_classification_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_problem_type_config.image_classification_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_problem_type_config.text_classification_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_problem_type_config.text_classification_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_problem_type_config.text_classification_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_problem_type_config.text_classification_job_config.content_column #=> String
resp.auto_ml_problem_type_config.text_classification_job_config.target_label_column #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.feature_specification_s3_uri #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.forecast_frequency #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.forecast_horizon #=> Integer
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.forecast_quantiles #=> Array
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.forecast_quantiles[0] #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.transformations.filling #=> Hash
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.transformations.filling["TransformationAttributeName"] #=> Hash
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.transformations.filling["TransformationAttributeName"]["FillingType"] #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.transformations.aggregation #=> Hash
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.transformations.aggregation["TransformationAttributeName"] #=> String, one of "sum", "avg", "first", "min", "max"
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.time_series_config.target_attribute_name #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.time_series_config.timestamp_attribute_name #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.time_series_config.item_identifier_attribute_name #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.time_series_config.grouping_attribute_names #=> Array
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.time_series_config.grouping_attribute_names[0] #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.holiday_config #=> Array
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.holiday_config[0].country_code #=> String
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.candidate_generation_config.algorithms_config #=> Array
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.candidate_generation_config.algorithms_config[0].auto_ml_algorithms #=> Array
resp.auto_ml_problem_type_config.time_series_forecasting_job_config.candidate_generation_config.algorithms_config[0].auto_ml_algorithms[0] #=> String, one of "xgboost", "linear-learner", "mlp", "lightgbm", "catboost", "randomforest", "extra-trees", "nn-torch", "fastai", "cnn-qr", "deepar", "prophet", "npts", "arima", "ets"
resp.auto_ml_problem_type_config.tabular_job_config.candidate_generation_config.algorithms_config #=> Array
resp.auto_ml_problem_type_config.tabular_job_config.candidate_generation_config.algorithms_config[0].auto_ml_algorithms #=> Array
resp.auto_ml_problem_type_config.tabular_job_config.candidate_generation_config.algorithms_config[0].auto_ml_algorithms[0] #=> String, one of "xgboost", "linear-learner", "mlp", "lightgbm", "catboost", "randomforest", "extra-trees", "nn-torch", "fastai", "cnn-qr", "deepar", "prophet", "npts", "arima", "ets"
resp.auto_ml_problem_type_config.tabular_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_problem_type_config.tabular_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_problem_type_config.tabular_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_problem_type_config.tabular_job_config.feature_specification_s3_uri #=> String
resp.auto_ml_problem_type_config.tabular_job_config.mode #=> String, one of "AUTO", "ENSEMBLING", "HYPERPARAMETER_TUNING"
resp.auto_ml_problem_type_config.tabular_job_config.generate_candidate_definitions_only #=> Boolean
resp.auto_ml_problem_type_config.tabular_job_config.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.auto_ml_problem_type_config.tabular_job_config.target_attribute_name #=> String
resp.auto_ml_problem_type_config.tabular_job_config.sample_weight_attribute_name #=> String
resp.auto_ml_problem_type_config.text_generation_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_problem_type_config.text_generation_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_problem_type_config.text_generation_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_problem_type_config.text_generation_job_config.base_model_name #=> String
resp.auto_ml_problem_type_config.text_generation_job_config.text_generation_hyper_parameters #=> Hash
resp.auto_ml_problem_type_config.text_generation_job_config.text_generation_hyper_parameters["TextGenerationHyperParameterKey"] #=> String
resp.auto_ml_problem_type_config.text_generation_job_config.model_access_config.accept_eula #=> Boolean
resp.auto_ml_problem_type_config_name #=> String, one of "ImageClassification", "TextClassification", "TimeSeriesForecasting", "Tabular", "TextGeneration"
resp.creation_time #=> Time
resp.end_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.partial_failure_reasons #=> Array
resp.partial_failure_reasons[0].partial_failure_message #=> String
resp.best_candidate.candidate_name #=> String
resp.best_candidate.final_auto_ml_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.best_candidate.final_auto_ml_job_objective_metric.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.best_candidate.final_auto_ml_job_objective_metric.value #=> Float
resp.best_candidate.final_auto_ml_job_objective_metric.standard_metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.best_candidate.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.best_candidate.candidate_steps #=> Array
resp.best_candidate.candidate_steps[0].candidate_step_type #=> String, one of "AWS::SageMaker::TrainingJob", "AWS::SageMaker::TransformJob", "AWS::SageMaker::ProcessingJob"
resp.best_candidate.candidate_steps[0].candidate_step_arn #=> String
resp.best_candidate.candidate_steps[0].candidate_step_name #=> String
resp.best_candidate.candidate_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.best_candidate.inference_containers #=> Array
resp.best_candidate.inference_containers[0].image #=> String
resp.best_candidate.inference_containers[0].model_data_url #=> String
resp.best_candidate.inference_containers[0].environment #=> Hash
resp.best_candidate.inference_containers[0].environment["EnvironmentKey"] #=> String
resp.best_candidate.creation_time #=> Time
resp.best_candidate.end_time #=> Time
resp.best_candidate.last_modified_time #=> Time
resp.best_candidate.failure_reason #=> String
resp.best_candidate.candidate_properties.candidate_artifact_locations.explainability #=> String
resp.best_candidate.candidate_properties.candidate_artifact_locations.model_insights #=> String
resp.best_candidate.candidate_properties.candidate_artifact_locations.backtest_results #=> String
resp.best_candidate.candidate_properties.candidate_metrics #=> Array
resp.best_candidate.candidate_properties.candidate_metrics[0].metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.best_candidate.candidate_properties.candidate_metrics[0].value #=> Float
resp.best_candidate.candidate_properties.candidate_metrics[0].set #=> String, one of "Train", "Validation", "Test"
resp.best_candidate.candidate_properties.candidate_metrics[0].standard_metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "MAE", "R2", "BalancedAccuracy", "Precision", "PrecisionMacro", "Recall", "RecallMacro", "LogLoss", "InferenceLatency", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss", "Rouge1", "Rouge2", "RougeL", "RougeLSum", "Perplexity", "ValidationLoss", "TrainingLoss"
resp.best_candidate.inference_container_definitions #=> Hash
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"] #=> Array
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].image #=> String
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].model_data_url #=> String
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].environment #=> Hash
resp.best_candidate.inference_container_definitions["AutoMLProcessingUnit"][0].environment["EnvironmentKey"] #=> String
resp.auto_ml_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.auto_ml_job_secondary_status #=> String, one of "Starting", "MaxCandidatesReached", "Failed", "Stopped", "MaxAutoMLJobRuntimeReached", "Stopping", "CandidateDefinitionsGenerated", "Completed", "ExplainabilityError", "DeployingModel", "ModelDeploymentError", "GeneratingModelInsightsReport", "ModelInsightsError", "AnalyzingData", "FeatureEngineering", "ModelTuning", "GeneratingExplainabilityReport", "TrainingModels", "PreTraining"
resp.auto_ml_job_artifacts.candidate_definition_notebook_location #=> String
resp.auto_ml_job_artifacts.data_exploration_notebook_location #=> String
resp.resolved_attributes.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.resolved_attributes.completion_criteria.max_candidates #=> Integer
resp.resolved_attributes.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.resolved_attributes.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.resolved_attributes.auto_ml_problem_type_resolved_attributes.tabular_resolved_attributes.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.resolved_attributes.auto_ml_problem_type_resolved_attributes.text_generation_resolved_attributes.base_model_name #=> String
resp.model_deploy_config.auto_generate_endpoint_name #=> Boolean
resp.model_deploy_config.endpoint_name #=> String
resp.model_deploy_result.endpoint_name #=> String
resp.data_split_config.validation_fraction #=> Float
resp.security_config.volume_kms_key_id #=> String
resp.security_config.enable_inter_container_traffic_encryption #=> Boolean
resp.security_config.vpc_config.security_group_ids #=> Array
resp.security_config.vpc_config.security_group_ids[0] #=> String
resp.security_config.vpc_config.subnets #=> Array
resp.security_config.vpc_config.subnets[0] #=> String
resp.auto_ml_compute_config.emr_serverless_compute_config.execution_role_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    Requests information about an AutoML job V2 using its unique name.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12045

def describe_auto_ml_job_v2(params = {}, options = {})
  req = build_request(:describe_auto_ml_job_v2, params)
  req.send_request(options)
end

#describe_cluster(params = {}) ⇒ Types::DescribeClusterResponse

Retrieves information of a SageMaker HyperPod cluster.

Examples:

Request syntax with placeholder values


resp = client.describe_cluster({
  cluster_name: "ClusterNameOrArn", # required
})

Response structure


resp.cluster_arn #=> String
resp.cluster_name #=> String
resp.cluster_status #=> String, one of "Creating", "Deleting", "Failed", "InService", "RollingBack", "SystemUpdating", "Updating"
resp.creation_time #=> Time
resp.failure_message #=> String
resp.instance_groups #=> Array
resp.instance_groups[0].current_count #=> Integer
resp.instance_groups[0].target_count #=> Integer
resp.instance_groups[0].instance_group_name #=> String
resp.instance_groups[0].instance_type #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.c5n.large", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge"
resp.instance_groups[0].life_cycle_config.source_s3_uri #=> String
resp.instance_groups[0].life_cycle_config.on_create #=> String
resp.instance_groups[0].execution_role #=> String
resp.instance_groups[0].threads_per_core #=> Integer
resp.instance_groups[0].instance_storage_configs #=> Array
resp.instance_groups[0].instance_storage_configs[0].ebs_volume_config.volume_size_in_gb #=> Integer
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12099

def describe_cluster(params = {}, options = {})
  req = build_request(:describe_cluster, params)
  req.send_request(options)
end

#describe_cluster_node(params = {}) ⇒ Types::DescribeClusterNodeResponse

Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.

Examples:

Request syntax with placeholder values


resp = client.describe_cluster_node({
  cluster_name: "ClusterNameOrArn", # required
  node_id: "ClusterNodeId", # required
})

Response structure


resp.node_details.instance_group_name #=> String
resp.node_details.instance_id #=> String
resp.node_details.instance_status.status #=> String, one of "Running", "Failure", "Pending", "ShuttingDown", "SystemUpdating"
resp.node_details.instance_status.message #=> String
resp.node_details.instance_type #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.c5n.large", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge"
resp.node_details.launch_time #=> Time
resp.node_details.life_cycle_config.source_s3_uri #=> String
resp.node_details.life_cycle_config.on_create #=> String
resp.node_details.threads_per_core #=> Integer
resp.node_details.instance_storage_configs #=> Array
resp.node_details.instance_storage_configs[0].ebs_volume_config.volume_size_in_gb #=> Integer
resp.node_details.private_primary_ip #=> String
resp.node_details.private_dns_hostname #=> String
resp.node_details.placement.availability_zone #=> String
resp.node_details.placement.availability_zone_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the node is.

  • :node_id (required, String) —

    The ID of the SageMaker HyperPod cluster node.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12147

def describe_cluster_node(params = {}, options = {})
  req = build_request(:describe_cluster_node, params)
  req.send_request(options)
end

#describe_code_repository(params = {}) ⇒ Types::DescribeCodeRepositoryOutput

Gets details about the specified Git repository.

Examples:

Request syntax with placeholder values


resp = client.describe_code_repository({
  code_repository_name: "EntityName", # required
})

Response structure


resp.code_repository_name #=> String
resp.code_repository_arn #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.git_config.repository_url #=> String
resp.git_config.branch #=> String
resp.git_config.secret_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :code_repository_name (required, String) —

    The name of the Git repository to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12185

def describe_code_repository(params = {}, options = {})
  req = build_request(:describe_code_repository, params)
  req.send_request(options)
end

#describe_compilation_job(params = {}) ⇒ Types::DescribeCompilationJobResponse

Returns information about a model compilation job.

To create a model compilation job, use [CreateCompilationJob]. To get information about multiple model compilation jobs, use [ListCompilationJobs].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListCompilationJobs.html

Examples:

Request syntax with placeholder values


resp = client.describe_compilation_job({
  compilation_job_name: "EntityName", # required
})

Response structure


resp.compilation_job_name #=> String
resp.compilation_job_arn #=> String
resp.compilation_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED"
resp.compilation_start_time #=> Time
resp.compilation_end_time #=> Time
resp.stopping_condition.max_runtime_in_seconds #=> Integer
resp.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.stopping_condition.max_pending_time_in_seconds #=> Integer
resp.inference_image #=> String
resp.model_package_version_arn #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.model_artifacts.s3_model_artifacts #=> String
resp.model_digests.artifact_digest #=> String
resp.role_arn #=> String
resp.input_config.s3_uri #=> String
resp.input_config.data_input_config #=> String
resp.input_config.framework #=> String, one of "TENSORFLOW", "KERAS", "MXNET", "ONNX", "PYTORCH", "XGBOOST", "TFLITE", "DARKNET", "SKLEARN"
resp.input_config.framework_version #=> String
resp.output_config.s3_output_location #=> String
resp.output_config.target_device #=> String, one of "lambda", "ml_m4", "ml_m5", "ml_m6g", "ml_c4", "ml_c5", "ml_c6g", "ml_p2", "ml_p3", "ml_g4dn", "ml_inf1", "ml_inf2", "ml_trn1", "ml_eia2", "jetson_tx1", "jetson_tx2", "jetson_nano", "jetson_xavier", "rasp3b", "rasp4b", "imx8qm", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603", "sitara_am57x", "amba_cv2", "amba_cv22", "amba_cv25", "x86_win32", "x86_win64", "coreml", "jacinto_tda4vm", "imx8mplus"
resp.output_config.target_platform.os #=> String, one of "ANDROID", "LINUX"
resp.output_config.target_platform.arch #=> String, one of "X86_64", "X86", "ARM64", "ARM_EABI", "ARM_EABIHF"
resp.output_config.target_platform.accelerator #=> String, one of "INTEL_GRAPHICS", "MALI", "NVIDIA", "NNA"
resp.output_config.compiler_options #=> String
resp.output_config.kms_key_id #=> String
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String
resp.derived_information.derived_data_input_config #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :compilation_job_name (required, String) —

    The name of the model compilation job that you want information about.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12270

def describe_compilation_job(params = {}, options = {})
  req = build_request(:describe_compilation_job, params)
  req.send_request(options)
end

#describe_context(params = {}) ⇒ Types::DescribeContextResponse

Describes a context.

Examples:

Request syntax with placeholder values


resp = client.describe_context({
  context_name: "ContextNameOrArn", # required
})

Response structure


resp.context_name #=> String
resp.context_arn #=> String
resp.source.source_uri #=> String
resp.source.source_type #=> String
resp.source.source_id #=> String
resp.context_type #=> String
resp.description #=> String
resp.properties #=> Hash
resp.properties["StringParameterValue"] #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.lineage_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :context_name (required, String) —

    The name of the context to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12331

def describe_context(params = {}, options = {})
  req = build_request(:describe_context, params)
  req.send_request(options)
end

#describe_data_quality_job_definition(params = {}) ⇒ Types::DescribeDataQualityJobDefinitionResponse

Gets the details of a data quality monitoring job definition.

Examples:

Request syntax with placeholder values


resp = client.describe_data_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Response structure


resp.job_definition_arn #=> String
resp.job_definition_name #=> String
resp.creation_time #=> Time
resp.data_quality_baseline_config.baselining_job_name #=> String
resp.data_quality_baseline_config.constraints_resource.s3_uri #=> String
resp.data_quality_baseline_config.statistics_resource.s3_uri #=> String
resp.data_quality_app_specification.image_uri #=> String
resp.data_quality_app_specification.container_entrypoint #=> Array
resp.data_quality_app_specification.container_entrypoint[0] #=> String
resp.data_quality_app_specification.container_arguments #=> Array
resp.data_quality_app_specification.container_arguments[0] #=> String
resp.data_quality_app_specification.record_preprocessor_source_uri #=> String
resp.data_quality_app_specification.post_analytics_processor_source_uri #=> String
resp.data_quality_app_specification.environment #=> Hash
resp.data_quality_app_specification.environment["ProcessingEnvironmentKey"] #=> String
resp.data_quality_job_input.endpoint_input.endpoint_name #=> String
resp.data_quality_job_input.endpoint_input.local_path #=> String
resp.data_quality_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.data_quality_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.data_quality_job_input.endpoint_input.features_attribute #=> String
resp.data_quality_job_input.endpoint_input.inference_attribute #=> String
resp.data_quality_job_input.endpoint_input.probability_attribute #=> String
resp.data_quality_job_input.endpoint_input.probability_threshold_attribute #=> Float
resp.data_quality_job_input.endpoint_input.start_time_offset #=> String
resp.data_quality_job_input.endpoint_input.end_time_offset #=> String
resp.data_quality_job_input.endpoint_input.exclude_features_attribute #=> String
resp.data_quality_job_input.batch_transform_input.data_captured_destination_s3_uri #=> String
resp.data_quality_job_input.batch_transform_input.dataset_format.csv.header #=> Boolean
resp.data_quality_job_input.batch_transform_input.dataset_format.json.line #=> Boolean
resp.data_quality_job_input.batch_transform_input.local_path #=> String
resp.data_quality_job_input.batch_transform_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.data_quality_job_input.batch_transform_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.data_quality_job_input.batch_transform_input.features_attribute #=> String
resp.data_quality_job_input.batch_transform_input.inference_attribute #=> String
resp.data_quality_job_input.batch_transform_input.probability_attribute #=> String
resp.data_quality_job_input.batch_transform_input.probability_threshold_attribute #=> Float
resp.data_quality_job_input.batch_transform_input.start_time_offset #=> String
resp.data_quality_job_input.batch_transform_input.end_time_offset #=> String
resp.data_quality_job_input.batch_transform_input.exclude_features_attribute #=> String
resp.data_quality_job_output_config.monitoring_outputs #=> Array
resp.data_quality_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String
resp.data_quality_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String
resp.data_quality_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob"
resp.data_quality_job_output_config.kms_key_id #=> String
resp.job_resources.cluster_config.instance_count #=> Integer
resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.job_resources.cluster_config.volume_size_in_gb #=> Integer
resp.job_resources.cluster_config.volume_kms_key_id #=> String
resp.network_config.enable_inter_container_traffic_encryption #=> Boolean
resp.network_config.enable_network_isolation #=> Boolean
resp.network_config.vpc_config.security_group_ids #=> Array
resp.network_config.vpc_config.security_group_ids[0] #=> String
resp.network_config.vpc_config.subnets #=> Array
resp.network_config.vpc_config.subnets[0] #=> String
resp.role_arn #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the data quality monitoring job definition to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12424

def describe_data_quality_job_definition(params = {}, options = {})
  req = build_request(:describe_data_quality_job_definition, params)
  req.send_request(options)
end

#describe_device(params = {}) ⇒ Types::DescribeDeviceResponse

Describes the device.

Examples:

Request syntax with placeholder values


resp = client.describe_device({
  next_token: "NextToken",
  device_name: "EntityName", # required
  device_fleet_name: "EntityName", # required
})

Response structure


resp.device_arn #=> String
resp.device_name #=> String
resp.description #=> String
resp.device_fleet_name #=> String
resp.iot_thing_name #=> String
resp.registration_time #=> Time
resp.latest_heartbeat #=> Time
resp.models #=> Array
resp.models[0].model_name #=> String
resp.models[0].model_version #=> String
resp.models[0].latest_sample_time #=> Time
resp.models[0].latest_inference #=> Time
resp.max_models #=> Integer
resp.next_token #=> String
resp.agent_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    Next token of device description.

  • :device_name (required, String) —

    The unique ID of the device.

  • :device_fleet_name (required, String) —

    The name of the fleet the devices belong to.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12484

def describe_device(params = {}, options = {})
  req = build_request(:describe_device, params)
  req.send_request(options)
end

#describe_device_fleet(params = {}) ⇒ Types::DescribeDeviceFleetResponse

A description of the fleet the device belongs to.

Examples:

Request syntax with placeholder values


resp = client.describe_device_fleet({
  device_fleet_name: "EntityName", # required
})

Response structure


resp.device_fleet_name #=> String
resp.device_fleet_arn #=> String
resp.output_config.s3_output_location #=> String
resp.output_config.kms_key_id #=> String
resp.output_config.preset_deployment_type #=> String, one of "GreengrassV2Component"
resp.output_config.preset_deployment_config #=> String
resp.description #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.role_arn #=> String
resp.iot_role_alias #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12529

def describe_device_fleet(params = {}, options = {})
  req = build_request(:describe_device_fleet, params)
  req.send_request(options)
end

#describe_domain(params = {}) ⇒ Types::DescribeDomainResponse

The description of the domain.

Examples:

Request syntax with placeholder values


resp = client.describe_domain({
  domain_id: "DomainId", # required
})

Response structure


resp.domain_arn #=> String
resp.domain_id #=> String
resp.domain_name #=> String
resp.home_efs_file_system_id #=> String
resp.single_sign_on_managed_application_instance_id #=> String
resp.single_sign_on_application_arn #=> String
resp.status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed"
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.security_group_id_for_domain_boundary #=> String
resp.auth_mode #=> String, one of "SSO", "IAM"
resp..execution_role #=> String
resp..security_groups #=> Array
resp..security_groups[0] #=> String
resp..sharing_settings.notebook_output_option #=> String, one of "Allowed", "Disabled"
resp..sharing_settings.s3_output_path #=> String
resp..sharing_settings.s3_kms_key_id #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..jupyter_server_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..jupyter_server_app_settings.lifecycle_config_arns #=> Array
resp..jupyter_server_app_settings.lifecycle_config_arns[0] #=> String
resp..jupyter_server_app_settings.code_repositories #=> Array
resp..jupyter_server_app_settings.code_repositories[0].repository_url #=> String
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..kernel_gateway_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..kernel_gateway_app_settings.custom_images #=> Array
resp..kernel_gateway_app_settings.custom_images[0].image_name #=> String
resp..kernel_gateway_app_settings.custom_images[0].image_version_number #=> Integer
resp..kernel_gateway_app_settings.custom_images[0].app_image_config_name #=> String
resp..kernel_gateway_app_settings.lifecycle_config_arns #=> Array
resp..kernel_gateway_app_settings.lifecycle_config_arns[0] #=> String
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..tensor_board_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..tensor_board_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..r_studio_server_pro_app_settings.access_status #=> String, one of "ENABLED", "DISABLED"
resp..r_studio_server_pro_app_settings.user_group #=> String, one of "R_STUDIO_ADMIN", "R_STUDIO_USER"
resp..r_session_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..r_session_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..r_session_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..r_session_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..r_session_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..r_session_app_settings.custom_images #=> Array
resp..r_session_app_settings.custom_images[0].image_name #=> String
resp..r_session_app_settings.custom_images[0].image_version_number #=> Integer
resp..r_session_app_settings.custom_images[0].app_image_config_name #=> String
resp..canvas_app_settings.time_series_forecasting_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.time_series_forecasting_settings.amazon_forecast_role_arn #=> String
resp..canvas_app_settings.model_register_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.model_register_settings. #=> String
resp..canvas_app_settings.workspace_settings.s3_artifact_path #=> String
resp..canvas_app_settings.workspace_settings.s3_kms_key_id #=> String
resp..canvas_app_settings.identity_provider_o_auth_settings #=> Array
resp..canvas_app_settings.identity_provider_o_auth_settings[0].data_source_name #=> String, one of "SalesforceGenie", "Snowflake"
resp..canvas_app_settings.identity_provider_o_auth_settings[0].status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.identity_provider_o_auth_settings[0].secret_arn #=> String
resp..canvas_app_settings.direct_deploy_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.kendra_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.generative_ai_settings.amazon_bedrock_role_arn #=> String
resp..canvas_app_settings.emr_serverless_settings.execution_role_arn #=> String
resp..canvas_app_settings.emr_serverless_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..code_editor_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..code_editor_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..code_editor_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..code_editor_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..code_editor_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..code_editor_app_settings.custom_images #=> Array
resp..code_editor_app_settings.custom_images[0].image_name #=> String
resp..code_editor_app_settings.custom_images[0].image_version_number #=> Integer
resp..code_editor_app_settings.custom_images[0].app_image_config_name #=> String
resp..code_editor_app_settings.lifecycle_config_arns #=> Array
resp..code_editor_app_settings.lifecycle_config_arns[0] #=> String
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.lifecycle_management #=> String, one of "ENABLED", "DISABLED"
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.min_idle_timeout_in_minutes #=> Integer
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.max_idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..jupyter_lab_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..jupyter_lab_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..jupyter_lab_app_settings.custom_images #=> Array
resp..jupyter_lab_app_settings.custom_images[0].image_name #=> String
resp..jupyter_lab_app_settings.custom_images[0].image_version_number #=> Integer
resp..jupyter_lab_app_settings.custom_images[0].app_image_config_name #=> String
resp..jupyter_lab_app_settings.lifecycle_config_arns #=> Array
resp..jupyter_lab_app_settings.lifecycle_config_arns[0] #=> String
resp..jupyter_lab_app_settings.code_repositories #=> Array
resp..jupyter_lab_app_settings.code_repositories[0].repository_url #=> String
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.lifecycle_management #=> String, one of "ENABLED", "DISABLED"
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.min_idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.max_idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.emr_settings.assumable_role_arns #=> Array
resp..jupyter_lab_app_settings.emr_settings.assumable_role_arns[0] #=> String
resp..jupyter_lab_app_settings.emr_settings.execution_role_arns #=> Array
resp..jupyter_lab_app_settings.emr_settings.execution_role_arns[0] #=> String
resp..space_storage_settings.default_ebs_storage_settings.default_ebs_volume_size_in_gb #=> Integer
resp..space_storage_settings.default_ebs_storage_settings.maximum_ebs_volume_size_in_gb #=> Integer
resp..default_landing_uri #=> String
resp..studio_web_portal #=> String, one of "ENABLED", "DISABLED"
resp..custom_posix_user_config.uid #=> Integer
resp..custom_posix_user_config.gid #=> Integer
resp..custom_file_system_configs #=> Array
resp..custom_file_system_configs[0].efs_file_system_config.file_system_id #=> String
resp..custom_file_system_configs[0].efs_file_system_config.file_system_path #=> String
resp..studio_web_portal_settings.hidden_ml_tools #=> Array
resp..studio_web_portal_settings.hidden_ml_tools[0] #=> String, one of "DataWrangler", "FeatureStore", "EmrClusters", "AutoMl", "Experiments", "Training", "ModelEvaluation", "Pipelines", "Models", "JumpStart", "InferenceRecommender", "Endpoints", "Projects", "InferenceOptimization"
resp..studio_web_portal_settings.hidden_app_types #=> Array
resp..studio_web_portal_settings.hidden_app_types[0] #=> String, one of "JupyterServer", "KernelGateway", "DetailedProfiler", "TensorBoard", "CodeEditor", "JupyterLab", "RStudioServerPro", "RSessionGateway", "Canvas"
resp..auto_mount_home_efs #=> String, one of "Enabled", "Disabled", "DefaultAsDomain"
resp.domain_settings.security_group_ids #=> Array
resp.domain_settings.security_group_ids[0] #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.domain_execution_role_arn #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.r_studio_connect_url #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.r_studio_package_manager_url #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.domain_settings.r_studio_server_pro_domain_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.domain_settings.r_studio_server_pro_domain_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.domain_settings.execution_role_identity_config #=> String, one of "USER_PROFILE_NAME", "DISABLED"
resp.domain_settings.docker_settings.enable_docker_access #=> String, one of "ENABLED", "DISABLED"
resp.domain_settings.docker_settings.vpc_only_trusted_accounts #=> Array
resp.domain_settings.docker_settings.vpc_only_trusted_accounts[0] #=> String
resp.domain_settings.amazon_q_settings.status #=> String, one of "ENABLED", "DISABLED"
resp.domain_settings.amazon_q_settings.q_profile_arn #=> String
resp.app_network_access_type #=> String, one of "PublicInternetOnly", "VpcOnly"
resp.home_efs_file_system_kms_key_id #=> String
resp.subnet_ids #=> Array
resp.subnet_ids[0] #=> String
resp.url #=> String
resp.vpc_id #=> String
resp.kms_key_id #=> String
resp.app_security_group_management #=> String, one of "Service", "Customer"
resp.default_space_settings.execution_role #=> String
resp.default_space_settings.security_groups #=> Array
resp.default_space_settings.security_groups[0] #=> String
resp.default_space_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.default_space_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.default_space_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.default_space_settings.jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.default_space_settings.jupyter_server_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.default_space_settings.jupyter_server_app_settings.lifecycle_config_arns #=> Array
resp.default_space_settings.jupyter_server_app_settings.lifecycle_config_arns[0] #=> String
resp.default_space_settings.jupyter_server_app_settings.code_repositories #=> Array
resp.default_space_settings.jupyter_server_app_settings.code_repositories[0].repository_url #=> String
resp.default_space_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.default_space_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.default_space_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.default_space_settings.kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.default_space_settings.kernel_gateway_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.default_space_settings.kernel_gateway_app_settings.custom_images #=> Array
resp.default_space_settings.kernel_gateway_app_settings.custom_images[0].image_name #=> String
resp.default_space_settings.kernel_gateway_app_settings.custom_images[0].image_version_number #=> Integer
resp.default_space_settings.kernel_gateway_app_settings.custom_images[0].app_image_config_name #=> String
resp.default_space_settings.kernel_gateway_app_settings.lifecycle_config_arns #=> Array
resp.default_space_settings.kernel_gateway_app_settings.lifecycle_config_arns[0] #=> String
resp.default_space_settings.jupyter_lab_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.default_space_settings.jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.default_space_settings.jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.default_space_settings.jupyter_lab_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.default_space_settings.jupyter_lab_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.default_space_settings.jupyter_lab_app_settings.custom_images #=> Array
resp.default_space_settings.jupyter_lab_app_settings.custom_images[0].image_name #=> String
resp.default_space_settings.jupyter_lab_app_settings.custom_images[0].image_version_number #=> Integer
resp.default_space_settings.jupyter_lab_app_settings.custom_images[0].app_image_config_name #=> String
resp.default_space_settings.jupyter_lab_app_settings.lifecycle_config_arns #=> Array
resp.default_space_settings.jupyter_lab_app_settings.lifecycle_config_arns[0] #=> String
resp.default_space_settings.jupyter_lab_app_settings.code_repositories #=> Array
resp.default_space_settings.jupyter_lab_app_settings.code_repositories[0].repository_url #=> String
resp.default_space_settings.jupyter_lab_app_settings.app_lifecycle_management.idle_settings.lifecycle_management #=> String, one of "ENABLED", "DISABLED"
resp.default_space_settings.jupyter_lab_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp.default_space_settings.jupyter_lab_app_settings.app_lifecycle_management.idle_settings.min_idle_timeout_in_minutes #=> Integer
resp.default_space_settings.jupyter_lab_app_settings.app_lifecycle_management.idle_settings.max_idle_timeout_in_minutes #=> Integer
resp.default_space_settings.jupyter_lab_app_settings.emr_settings.assumable_role_arns #=> Array
resp.default_space_settings.jupyter_lab_app_settings.emr_settings.assumable_role_arns[0] #=> String
resp.default_space_settings.jupyter_lab_app_settings.emr_settings.execution_role_arns #=> Array
resp.default_space_settings.jupyter_lab_app_settings.emr_settings.execution_role_arns[0] #=> String
resp.default_space_settings.space_storage_settings.default_ebs_storage_settings.default_ebs_volume_size_in_gb #=> Integer
resp.default_space_settings.space_storage_settings.default_ebs_storage_settings.maximum_ebs_volume_size_in_gb #=> Integer
resp.default_space_settings.custom_posix_user_config.uid #=> Integer
resp.default_space_settings.custom_posix_user_config.gid #=> Integer
resp.default_space_settings.custom_file_system_configs #=> Array
resp.default_space_settings.custom_file_system_configs[0].efs_file_system_config.file_system_id #=> String
resp.default_space_settings.custom_file_system_configs[0].efs_file_system_config.file_system_path #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12771

def describe_domain(params = {}, options = {})
  req = build_request(:describe_domain, params)
  req.send_request(options)
end

#describe_edge_deployment_plan(params = {}) ⇒ Types::DescribeEdgeDeploymentPlanResponse

Describes an edge deployment plan with deployment status per stage.

Examples:

Request syntax with placeholder values


resp = client.describe_edge_deployment_plan({
  edge_deployment_plan_name: "EntityName", # required
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.edge_deployment_plan_arn #=> String
resp.edge_deployment_plan_name #=> String
resp.model_configs #=> Array
resp.model_configs[0].model_handle #=> String
resp.model_configs[0].edge_packaging_job_name #=> String
resp.device_fleet_name #=> String
resp.edge_deployment_success #=> Integer
resp.edge_deployment_pending #=> Integer
resp.edge_deployment_failed #=> Integer
resp.stages #=> Array
resp.stages[0].stage_name #=> String
resp.stages[0].device_selection_config.device_subset_type #=> String, one of "PERCENTAGE", "SELECTION", "NAMECONTAINS"
resp.stages[0].device_selection_config.percentage #=> Integer
resp.stages[0].device_selection_config.device_names #=> Array
resp.stages[0].device_selection_config.device_names[0] #=> String
resp.stages[0].device_selection_config.device_name_contains #=> String
resp.stages[0].deployment_config.failure_handling_policy #=> String, one of "ROLLBACK_ON_FAILURE", "DO_NOTHING"
resp.stages[0].deployment_status.stage_status #=> String, one of "CREATING", "READYTODEPLOY", "STARTING", "INPROGRESS", "DEPLOYED", "FAILED", "STOPPING", "STOPPED"
resp.stages[0].deployment_status.edge_deployment_success_in_stage #=> Integer
resp.stages[0].deployment_status.edge_deployment_pending_in_stage #=> Integer
resp.stages[0].deployment_status.edge_deployment_failed_in_stage #=> Integer
resp.stages[0].deployment_status.edge_deployment_status_message #=> String
resp.stages[0].deployment_status.edge_deployment_stage_start_time #=> Time
resp.next_token #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the deployment plan to describe.

  • :next_token (String) —

    If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.

  • :max_results (Integer) —

    The maximum number of results to select (50 by default).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12843

def describe_edge_deployment_plan(params = {}, options = {})
  req = build_request(:describe_edge_deployment_plan, params)
  req.send_request(options)
end

#describe_edge_packaging_job(params = {}) ⇒ Types::DescribeEdgePackagingJobResponse

A description of edge packaging jobs.

Examples:

Request syntax with placeholder values


resp = client.describe_edge_packaging_job({
  edge_packaging_job_name: "EntityName", # required
})

Response structure


resp.edge_packaging_job_arn #=> String
resp.edge_packaging_job_name #=> String
resp.compilation_job_name #=> String
resp.model_name #=> String
resp.model_version #=> String
resp.role_arn #=> String
resp.output_config.s3_output_location #=> String
resp.output_config.kms_key_id #=> String
resp.output_config.preset_deployment_type #=> String, one of "GreengrassV2Component"
resp.output_config.preset_deployment_config #=> String
resp.resource_key #=> String
resp.edge_packaging_job_status #=> String, one of "STARTING", "INPROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED"
resp.edge_packaging_job_status_message #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.model_artifact #=> String
resp.model_signature #=> String
resp.preset_deployment_output.type #=> String, one of "GreengrassV2Component"
resp.preset_deployment_output.artifact #=> String
resp.preset_deployment_output.status #=> String, one of "COMPLETED", "FAILED"
resp.preset_deployment_output.status_message #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_packaging_job_name (required, String) —

    The name of the edge packaging job.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 12905

def describe_edge_packaging_job(params = {}, options = {})
  req = build_request(:describe_edge_packaging_job, params)
  req.send_request(options)
end

#describe_endpoint(params = {}) ⇒ Types::DescribeEndpointOutput

Returns the description of an endpoint.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* endpoint_deleted
* endpoint_in_service

Examples:

Request syntax with placeholder values


resp = client.describe_endpoint({
  endpoint_name: "EndpointName", # required
})

Response structure


resp.endpoint_name #=> String
resp.endpoint_arn #=> String
resp.endpoint_config_name #=> String
resp.production_variants #=> Array
resp.production_variants[0].variant_name #=> String
resp.production_variants[0].deployed_images #=> Array
resp.production_variants[0].deployed_images[0].specified_image #=> String
resp.production_variants[0].deployed_images[0].resolved_image #=> String
resp.production_variants[0].deployed_images[0].resolution_time #=> Time
resp.production_variants[0].current_weight #=> Float
resp.production_variants[0].desired_weight #=> Float
resp.production_variants[0].current_instance_count #=> Integer
resp.production_variants[0].desired_instance_count #=> Integer
resp.production_variants[0].variant_status #=> Array
resp.production_variants[0].variant_status[0].status #=> String, one of "Creating", "Updating", "Deleting", "ActivatingTraffic", "Baking"
resp.production_variants[0].variant_status[0].status_message #=> String
resp.production_variants[0].variant_status[0].start_time #=> Time
resp.production_variants[0].current_serverless_config.memory_size_in_mb #=> Integer
resp.production_variants[0].current_serverless_config.max_concurrency #=> Integer
resp.production_variants[0].current_serverless_config.provisioned_concurrency #=> Integer
resp.production_variants[0].desired_serverless_config.memory_size_in_mb #=> Integer
resp.production_variants[0].desired_serverless_config.max_concurrency #=> Integer
resp.production_variants[0].desired_serverless_config.provisioned_concurrency #=> Integer
resp.production_variants[0].managed_instance_scaling.status #=> String, one of "ENABLED", "DISABLED"
resp.production_variants[0].managed_instance_scaling.min_instance_count #=> Integer
resp.production_variants[0].managed_instance_scaling.max_instance_count #=> Integer
resp.production_variants[0].routing_config.routing_strategy #=> String, one of "LEAST_OUTSTANDING_REQUESTS", "RANDOM"
resp.data_capture_config.enable_capture #=> Boolean
resp.data_capture_config.capture_status #=> String, one of "Started", "Stopped"
resp.data_capture_config.current_sampling_percentage #=> Integer
resp.data_capture_config.destination_s3_uri #=> String
resp.data_capture_config.kms_key_id #=> String
resp.endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed", "UpdateRollbackFailed"
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.type #=> String, one of "ALL_AT_ONCE", "CANARY", "LINEAR"
resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.wait_interval_in_seconds #=> Integer
resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.canary_size.type #=> String, one of "INSTANCE_COUNT", "CAPACITY_PERCENT"
resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.canary_size.value #=> Integer
resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.linear_step_size.type #=> String, one of "INSTANCE_COUNT", "CAPACITY_PERCENT"
resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.linear_step_size.value #=> Integer
resp.last_deployment_config.blue_green_update_policy.termination_wait_in_seconds #=> Integer
resp.last_deployment_config.blue_green_update_policy.maximum_execution_timeout_in_seconds #=> Integer
resp.last_deployment_config.rolling_update_policy.maximum_batch_size.type #=> String, one of "INSTANCE_COUNT", "CAPACITY_PERCENT"
resp.last_deployment_config.rolling_update_policy.maximum_batch_size.value #=> Integer
resp.last_deployment_config.rolling_update_policy.wait_interval_in_seconds #=> Integer
resp.last_deployment_config.rolling_update_policy.maximum_execution_timeout_in_seconds #=> Integer
resp.last_deployment_config.rolling_update_policy.rollback_maximum_batch_size.type #=> String, one of "INSTANCE_COUNT", "CAPACITY_PERCENT"
resp.last_deployment_config.rolling_update_policy.rollback_maximum_batch_size.value #=> Integer
resp.last_deployment_config.auto_rollback_configuration.alarms #=> Array
resp.last_deployment_config.auto_rollback_configuration.alarms[0].alarm_name #=> String
resp.async_inference_config.client_config.max_concurrent_invocations_per_instance #=> Integer
resp.async_inference_config.output_config.kms_key_id #=> String
resp.async_inference_config.output_config.s3_output_path #=> String
resp.async_inference_config.output_config.notification_config.success_topic #=> String
resp.async_inference_config.output_config.notification_config.error_topic #=> String
resp.async_inference_config.output_config.notification_config.include_inference_response_in #=> Array
resp.async_inference_config.output_config.notification_config.include_inference_response_in[0] #=> String, one of "SUCCESS_NOTIFICATION_TOPIC", "ERROR_NOTIFICATION_TOPIC"
resp.async_inference_config.output_config.s3_failure_path #=> String
resp.pending_deployment_summary.endpoint_config_name #=> String
resp.pending_deployment_summary.production_variants #=> Array
resp.pending_deployment_summary.production_variants[0].variant_name #=> String
resp.pending_deployment_summary.production_variants[0].deployed_images #=> Array
resp.pending_deployment_summary.production_variants[0].deployed_images[0].specified_image #=> String
resp.pending_deployment_summary.production_variants[0].deployed_images[0].resolved_image #=> String
resp.pending_deployment_summary.production_variants[0].deployed_images[0].resolution_time #=> Time
resp.pending_deployment_summary.production_variants[0].current_weight #=> Float
resp.pending_deployment_summary.production_variants[0].desired_weight #=> Float
resp.pending_deployment_summary.production_variants[0].current_instance_count #=> Integer
resp.pending_deployment_summary.production_variants[0].desired_instance_count #=> Integer
resp.pending_deployment_summary.production_variants[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.pending_deployment_summary.production_variants[0].accelerator_type #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.pending_deployment_summary.production_variants[0].variant_status #=> Array
resp.pending_deployment_summary.production_variants[0].variant_status[0].status #=> String, one of "Creating", "Updating", "Deleting", "ActivatingTraffic", "Baking"
resp.pending_deployment_summary.production_variants[0].variant_status[0].status_message #=> String
resp.pending_deployment_summary.production_variants[0].variant_status[0].start_time #=> Time
resp.pending_deployment_summary.production_variants[0].current_serverless_config.memory_size_in_mb #=> Integer
resp.pending_deployment_summary.production_variants[0].current_serverless_config.max_concurrency #=> Integer
resp.pending_deployment_summary.production_variants[0].current_serverless_config.provisioned_concurrency #=> Integer
resp.pending_deployment_summary.production_variants[0].desired_serverless_config.memory_size_in_mb #=> Integer
resp.pending_deployment_summary.production_variants[0].desired_serverless_config.max_concurrency #=> Integer
resp.pending_deployment_summary.production_variants[0].desired_serverless_config.provisioned_concurrency #=> Integer
resp.pending_deployment_summary.production_variants[0].managed_instance_scaling.status #=> String, one of "ENABLED", "DISABLED"
resp.pending_deployment_summary.production_variants[0].managed_instance_scaling.min_instance_count #=> Integer
resp.pending_deployment_summary.production_variants[0].managed_instance_scaling.max_instance_count #=> Integer
resp.pending_deployment_summary.production_variants[0].routing_config.routing_strategy #=> String, one of "LEAST_OUTSTANDING_REQUESTS", "RANDOM"
resp.pending_deployment_summary.start_time #=> Time
resp.pending_deployment_summary.shadow_production_variants #=> Array
resp.pending_deployment_summary.shadow_production_variants[0].variant_name #=> String
resp.pending_deployment_summary.shadow_production_variants[0].deployed_images #=> Array
resp.pending_deployment_summary.shadow_production_variants[0].deployed_images[0].specified_image #=> String
resp.pending_deployment_summary.shadow_production_variants[0].deployed_images[0].resolved_image #=> String
resp.pending_deployment_summary.shadow_production_variants[0].deployed_images[0].resolution_time #=> Time
resp.pending_deployment_summary.shadow_production_variants[0].current_weight #=> Float
resp.pending_deployment_summary.shadow_production_variants[0].desired_weight #=> Float
resp.pending_deployment_summary.shadow_production_variants[0].current_instance_count #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].desired_instance_count #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.pending_deployment_summary.shadow_production_variants[0].accelerator_type #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.pending_deployment_summary.shadow_production_variants[0].variant_status #=> Array
resp.pending_deployment_summary.shadow_production_variants[0].variant_status[0].status #=> String, one of "Creating", "Updating", "Deleting", "ActivatingTraffic", "Baking"
resp.pending_deployment_summary.shadow_production_variants[0].variant_status[0].status_message #=> String
resp.pending_deployment_summary.shadow_production_variants[0].variant_status[0].start_time #=> Time
resp.pending_deployment_summary.shadow_production_variants[0].current_serverless_config.memory_size_in_mb #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].current_serverless_config.max_concurrency #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].current_serverless_config.provisioned_concurrency #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].desired_serverless_config.memory_size_in_mb #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].desired_serverless_config.max_concurrency #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].desired_serverless_config.provisioned_concurrency #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].managed_instance_scaling.status #=> String, one of "ENABLED", "DISABLED"
resp.pending_deployment_summary.shadow_production_variants[0].managed_instance_scaling.min_instance_count #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].managed_instance_scaling.max_instance_count #=> Integer
resp.pending_deployment_summary.shadow_production_variants[0].routing_config.routing_strategy #=> String, one of "LEAST_OUTSTANDING_REQUESTS", "RANDOM"
resp.explainer_config.clarify_explainer_config.enable_explanations #=> String
resp.explainer_config.clarify_explainer_config.inference_config.features_attribute #=> String
resp.explainer_config.clarify_explainer_config.inference_config.content_template #=> String
resp.explainer_config.clarify_explainer_config.inference_config.max_record_count #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.max_payload_in_mb #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.probability_index #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.label_index #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.probability_attribute #=> String
resp.explainer_config.clarify_explainer_config.inference_config.label_attribute #=> String
resp.explainer_config.clarify_explainer_config.inference_config.label_headers #=> Array
resp.explainer_config.clarify_explainer_config.inference_config.label_headers[0] #=> String
resp.explainer_config.clarify_explainer_config.inference_config.feature_headers #=> Array
resp.explainer_config.clarify_explainer_config.inference_config.feature_headers[0] #=> String
resp.explainer_config.clarify_explainer_config.inference_config.feature_types #=> Array
resp.explainer_config.clarify_explainer_config.inference_config.feature_types[0] #=> String, one of "numerical", "categorical", "text"
resp.explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.mime_type #=> String
resp.explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.shap_baseline #=> String
resp.explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.shap_baseline_uri #=> String
resp.explainer_config.clarify_explainer_config.shap_config.number_of_samples #=> Integer
resp.explainer_config.clarify_explainer_config.shap_config.use_logit #=> Boolean
resp.explainer_config.clarify_explainer_config.shap_config.seed #=> Integer
resp.explainer_config.clarify_explainer_config.shap_config.text_config.language #=> String, one of "af", "sq", "ar", "hy", "eu", "bn", "bg", "ca", "zh", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "de", "el", "gu", "he", "hi", "hu", "is", "id", "ga", "it", "kn", "ky", "lv", "lt", "lb", "mk", "ml", "mr", "ne", "nb", "fa", "pl", "pt", "ro", "ru", "sa", "sr", "tn", "si", "sk", "sl", "es", "sv", "tl", "ta", "tt", "te", "tr", "uk", "ur", "yo", "lij", "xx"
resp.explainer_config.clarify_explainer_config.shap_config.text_config.granularity #=> String, one of "token", "sentence", "paragraph"
resp.shadow_production_variants #=> Array
resp.shadow_production_variants[0].variant_name #=> String
resp.shadow_production_variants[0].deployed_images #=> Array
resp.shadow_production_variants[0].deployed_images[0].specified_image #=> String
resp.shadow_production_variants[0].deployed_images[0].resolved_image #=> String
resp.shadow_production_variants[0].deployed_images[0].resolution_time #=> Time
resp.shadow_production_variants[0].current_weight #=> Float
resp.shadow_production_variants[0].desired_weight #=> Float
resp.shadow_production_variants[0].current_instance_count #=> Integer
resp.shadow_production_variants[0].desired_instance_count #=> Integer
resp.shadow_production_variants[0].variant_status #=> Array
resp.shadow_production_variants[0].variant_status[0].status #=> String, one of "Creating", "Updating", "Deleting", "ActivatingTraffic", "Baking"
resp.shadow_production_variants[0].variant_status[0].status_message #=> String
resp.shadow_production_variants[0].variant_status[0].start_time #=> Time
resp.shadow_production_variants[0].current_serverless_config.memory_size_in_mb #=> Integer
resp.shadow_production_variants[0].current_serverless_config.max_concurrency #=> Integer
resp.shadow_production_variants[0].current_serverless_config.provisioned_concurrency #=> Integer
resp.shadow_production_variants[0].desired_serverless_config.memory_size_in_mb #=> Integer
resp.shadow_production_variants[0].desired_serverless_config.max_concurrency #=> Integer
resp.shadow_production_variants[0].desired_serverless_config.provisioned_concurrency #=> Integer
resp.shadow_production_variants[0].managed_instance_scaling.status #=> String, one of "ENABLED", "DISABLED"
resp.shadow_production_variants[0].managed_instance_scaling.min_instance_count #=> Integer
resp.shadow_production_variants[0].managed_instance_scaling.max_instance_count #=> Integer
resp.shadow_production_variants[0].routing_config.routing_strategy #=> String, one of "LEAST_OUTSTANDING_REQUESTS", "RANDOM"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (required, String) —

    The name of the endpoint.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13112

def describe_endpoint(params = {}, options = {})
  req = build_request(:describe_endpoint, params)
  req.send_request(options)
end

#describe_endpoint_config(params = {}) ⇒ Types::DescribeEndpointConfigOutput

Returns the description of an endpoint configuration created using the ‘CreateEndpointConfig` API.

Examples:

Request syntax with placeholder values


resp = client.describe_endpoint_config({
  endpoint_config_name: "EndpointConfigName", # required
})

Response structure


resp.endpoint_config_name #=> String
resp.endpoint_config_arn #=> String
resp.production_variants #=> Array
resp.production_variants[0].variant_name #=> String
resp.production_variants[0].model_name #=> String
resp.production_variants[0].initial_instance_count #=> Integer
resp.production_variants[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.production_variants[0].initial_variant_weight #=> Float
resp.production_variants[0].accelerator_type #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.production_variants[0].core_dump_config.destination_s3_uri #=> String
resp.production_variants[0].core_dump_config.kms_key_id #=> String
resp.production_variants[0].serverless_config.memory_size_in_mb #=> Integer
resp.production_variants[0].serverless_config.max_concurrency #=> Integer
resp.production_variants[0].serverless_config.provisioned_concurrency #=> Integer
resp.production_variants[0].volume_size_in_gb #=> Integer
resp.production_variants[0].model_data_download_timeout_in_seconds #=> Integer
resp.production_variants[0].container_startup_health_check_timeout_in_seconds #=> Integer
resp.production_variants[0].enable_ssm_access #=> Boolean
resp.production_variants[0].managed_instance_scaling.status #=> String, one of "ENABLED", "DISABLED"
resp.production_variants[0].managed_instance_scaling.min_instance_count #=> Integer
resp.production_variants[0].managed_instance_scaling.max_instance_count #=> Integer
resp.production_variants[0].routing_config.routing_strategy #=> String, one of "LEAST_OUTSTANDING_REQUESTS", "RANDOM"
resp.production_variants[0].inference_ami_version #=> String, one of "al2-ami-sagemaker-inference-gpu-2"
resp.data_capture_config.enable_capture #=> Boolean
resp.data_capture_config.initial_sampling_percentage #=> Integer
resp.data_capture_config.destination_s3_uri #=> String
resp.data_capture_config.kms_key_id #=> String
resp.data_capture_config.capture_options #=> Array
resp.data_capture_config.capture_options[0].capture_mode #=> String, one of "Input", "Output", "InputAndOutput"
resp.data_capture_config.capture_content_type_header.csv_content_types #=> Array
resp.data_capture_config.capture_content_type_header.csv_content_types[0] #=> String
resp.data_capture_config.capture_content_type_header.json_content_types #=> Array
resp.data_capture_config.capture_content_type_header.json_content_types[0] #=> String
resp.kms_key_id #=> String
resp.creation_time #=> Time
resp.async_inference_config.client_config.max_concurrent_invocations_per_instance #=> Integer
resp.async_inference_config.output_config.kms_key_id #=> String
resp.async_inference_config.output_config.s3_output_path #=> String
resp.async_inference_config.output_config.notification_config.success_topic #=> String
resp.async_inference_config.output_config.notification_config.error_topic #=> String
resp.async_inference_config.output_config.notification_config.include_inference_response_in #=> Array
resp.async_inference_config.output_config.notification_config.include_inference_response_in[0] #=> String, one of "SUCCESS_NOTIFICATION_TOPIC", "ERROR_NOTIFICATION_TOPIC"
resp.async_inference_config.output_config.s3_failure_path #=> String
resp.explainer_config.clarify_explainer_config.enable_explanations #=> String
resp.explainer_config.clarify_explainer_config.inference_config.features_attribute #=> String
resp.explainer_config.clarify_explainer_config.inference_config.content_template #=> String
resp.explainer_config.clarify_explainer_config.inference_config.max_record_count #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.max_payload_in_mb #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.probability_index #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.label_index #=> Integer
resp.explainer_config.clarify_explainer_config.inference_config.probability_attribute #=> String
resp.explainer_config.clarify_explainer_config.inference_config.label_attribute #=> String
resp.explainer_config.clarify_explainer_config.inference_config.label_headers #=> Array
resp.explainer_config.clarify_explainer_config.inference_config.label_headers[0] #=> String
resp.explainer_config.clarify_explainer_config.inference_config.feature_headers #=> Array
resp.explainer_config.clarify_explainer_config.inference_config.feature_headers[0] #=> String
resp.explainer_config.clarify_explainer_config.inference_config.feature_types #=> Array
resp.explainer_config.clarify_explainer_config.inference_config.feature_types[0] #=> String, one of "numerical", "categorical", "text"
resp.explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.mime_type #=> String
resp.explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.shap_baseline #=> String
resp.explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.shap_baseline_uri #=> String
resp.explainer_config.clarify_explainer_config.shap_config.number_of_samples #=> Integer
resp.explainer_config.clarify_explainer_config.shap_config.use_logit #=> Boolean
resp.explainer_config.clarify_explainer_config.shap_config.seed #=> Integer
resp.explainer_config.clarify_explainer_config.shap_config.text_config.language #=> String, one of "af", "sq", "ar", "hy", "eu", "bn", "bg", "ca", "zh", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "de", "el", "gu", "he", "hi", "hu", "is", "id", "ga", "it", "kn", "ky", "lv", "lt", "lb", "mk", "ml", "mr", "ne", "nb", "fa", "pl", "pt", "ro", "ru", "sa", "sr", "tn", "si", "sk", "sl", "es", "sv", "tl", "ta", "tt", "te", "tr", "uk", "ur", "yo", "lij", "xx"
resp.explainer_config.clarify_explainer_config.shap_config.text_config.granularity #=> String, one of "token", "sentence", "paragraph"
resp.shadow_production_variants #=> Array
resp.shadow_production_variants[0].variant_name #=> String
resp.shadow_production_variants[0].model_name #=> String
resp.shadow_production_variants[0].initial_instance_count #=> Integer
resp.shadow_production_variants[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.shadow_production_variants[0].initial_variant_weight #=> Float
resp.shadow_production_variants[0].accelerator_type #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.shadow_production_variants[0].core_dump_config.destination_s3_uri #=> String
resp.shadow_production_variants[0].core_dump_config.kms_key_id #=> String
resp.shadow_production_variants[0].serverless_config.memory_size_in_mb #=> Integer
resp.shadow_production_variants[0].serverless_config.max_concurrency #=> Integer
resp.shadow_production_variants[0].serverless_config.provisioned_concurrency #=> Integer
resp.shadow_production_variants[0].volume_size_in_gb #=> Integer
resp.shadow_production_variants[0].model_data_download_timeout_in_seconds #=> Integer
resp.shadow_production_variants[0].container_startup_health_check_timeout_in_seconds #=> Integer
resp.shadow_production_variants[0].enable_ssm_access #=> Boolean
resp.shadow_production_variants[0].managed_instance_scaling.status #=> String, one of "ENABLED", "DISABLED"
resp.shadow_production_variants[0].managed_instance_scaling.min_instance_count #=> Integer
resp.shadow_production_variants[0].managed_instance_scaling.max_instance_count #=> Integer
resp.shadow_production_variants[0].routing_config.routing_strategy #=> String, one of "LEAST_OUTSTANDING_REQUESTS", "RANDOM"
resp.shadow_production_variants[0].inference_ami_version #=> String, one of "al2-ami-sagemaker-inference-gpu-2"
resp.execution_role_arn #=> String
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String
resp.enable_network_isolation #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_config_name (required, String) —

    The name of the endpoint configuration.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13244

def describe_endpoint_config(params = {}, options = {})
  req = build_request(:describe_endpoint_config, params)
  req.send_request(options)
end

#describe_experiment(params = {}) ⇒ Types::DescribeExperimentResponse

Provides a list of an experiment’s properties.

Examples:

Request syntax with placeholder values


resp = client.describe_experiment({
  experiment_name: "ExperimentEntityName", # required
})

Response structure


resp.experiment_name #=> String
resp.experiment_arn #=> String
resp.display_name #=> String
resp.source.source_arn #=> String
resp.source.source_type #=> String
resp.description #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13299

def describe_experiment(params = {}, options = {})
  req = build_request(:describe_experiment, params)
  req.send_request(options)
end

#describe_feature_group(params = {}) ⇒ Types::DescribeFeatureGroupResponse

Use this operation to describe a ‘FeatureGroup`. The response includes information on the creation time, `FeatureGroup` name, the unique identifier for each `FeatureGroup`, and more.

Examples:

Request syntax with placeholder values


resp = client.describe_feature_group({
  feature_group_name: "FeatureGroupNameOrArn", # required
  next_token: "NextToken",
})

Response structure


resp.feature_group_arn #=> String
resp.feature_group_name #=> String
resp.record_identifier_feature_name #=> String
resp.event_time_feature_name #=> String
resp.feature_definitions #=> Array
resp.feature_definitions[0].feature_name #=> String
resp.feature_definitions[0].feature_type #=> String, one of "Integral", "Fractional", "String"
resp.feature_definitions[0].collection_type #=> String, one of "List", "Set", "Vector"
resp.feature_definitions[0].collection_config.vector_config.dimension #=> Integer
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.online_store_config.security_config.kms_key_id #=> String
resp.online_store_config.enable_online_store #=> Boolean
resp.online_store_config.ttl_duration.unit #=> String, one of "Seconds", "Minutes", "Hours", "Days", "Weeks"
resp.online_store_config.ttl_duration.value #=> Integer
resp.online_store_config.storage_type #=> String, one of "Standard", "InMemory"
resp.offline_store_config.s3_storage_config.s3_uri #=> String
resp.offline_store_config.s3_storage_config.kms_key_id #=> String
resp.offline_store_config.s3_storage_config.resolved_output_s3_uri #=> String
resp.offline_store_config.disable_glue_table_creation #=> Boolean
resp.offline_store_config.data_catalog_config.table_name #=> String
resp.offline_store_config.data_catalog_config.catalog #=> String
resp.offline_store_config.data_catalog_config.database #=> String
resp.offline_store_config.table_format #=> String, one of "Default", "Glue", "Iceberg"
resp.throughput_config.throughput_mode #=> String, one of "OnDemand", "Provisioned"
resp.throughput_config.provisioned_read_capacity_units #=> Integer
resp.throughput_config.provisioned_write_capacity_units #=> Integer
resp.role_arn #=> String
resp.feature_group_status #=> String, one of "Creating", "Created", "CreateFailed", "Deleting", "DeleteFailed"
resp.offline_store_status.status #=> String, one of "Active", "Blocked", "Disabled"
resp.offline_store_status.blocked_reason #=> String
resp.last_update_status.status #=> String, one of "Successful", "Failed", "InProgress"
resp.last_update_status.failure_reason #=> String
resp.failure_reason #=> String
resp.description #=> String
resp.next_token #=> String
resp.online_store_total_size_bytes #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :feature_group_name (required, String) —

    The name or Amazon Resource Name (ARN) of the ‘FeatureGroup` you want described.

  • :next_token (String) —

    A token to resume pagination of the list of ‘Features` (`FeatureDefinitions`). 2,500 `Features` are returned by default.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13388

def describe_feature_group(params = {}, options = {})
  req = build_request(:describe_feature_group, params)
  req.send_request(options)
end

#describe_feature_metadata(params = {}) ⇒ Types::DescribeFeatureMetadataResponse

Shows the metadata for a feature within a feature group.

Examples:

Request syntax with placeholder values


resp = client.describe_feature_metadata({
  feature_group_name: "FeatureGroupNameOrArn", # required
  feature_name: "FeatureName", # required
})

Response structure


resp.feature_group_arn #=> String
resp.feature_group_name #=> String
resp.feature_name #=> String
resp.feature_type #=> String, one of "Integral", "Fractional", "String"
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.description #=> String
resp.parameters #=> Array
resp.parameters[0].key #=> String
resp.parameters[0].value #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :feature_group_name (required, String) —

    The name or Amazon Resource Name (ARN) of the feature group containing the feature.

  • :feature_name (required, String) —

    The name of the feature.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13437

def describe_feature_metadata(params = {}, options = {})
  req = build_request(:describe_feature_metadata, params)
  req.send_request(options)
end

#describe_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse

Returns information about the specified flow definition.

Examples:

Request syntax with placeholder values


resp = client.describe_flow_definition({
  flow_definition_name: "FlowDefinitionName", # required
})

Response structure


resp.flow_definition_arn #=> String
resp.flow_definition_name #=> String
resp.flow_definition_status #=> String, one of "Initializing", "Active", "Failed", "Deleting"
resp.creation_time #=> Time
resp.human_loop_request_source.aws_managed_human_loop_request_source #=> String, one of "AWS/Rekognition/DetectModerationLabels/Image/V3", "AWS/Textract/AnalyzeDocument/Forms/V1"
resp.human_loop_activation_config.human_loop_activation_conditions_config.human_loop_activation_conditions #=> String
resp.human_loop_config.workteam_arn #=> String
resp.human_loop_config.human_task_ui_arn #=> String
resp.human_loop_config.task_title #=> String
resp.human_loop_config.task_description #=> String
resp.human_loop_config.task_count #=> Integer
resp.human_loop_config.task_availability_lifetime_in_seconds #=> Integer
resp.human_loop_config.task_time_limit_in_seconds #=> Integer
resp.human_loop_config.task_keywords #=> Array
resp.human_loop_config.task_keywords[0] #=> String
resp.human_loop_config.public_workforce_task_price.amount_in_usd.dollars #=> Integer
resp.human_loop_config.public_workforce_task_price.amount_in_usd.cents #=> Integer
resp.human_loop_config.public_workforce_task_price.amount_in_usd.tenth_fractions_of_a_cent #=> Integer
resp.output_config.s3_output_path #=> String
resp.output_config.kms_key_id #=> String
resp.role_arn #=> String
resp.failure_reason #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :flow_definition_name (required, String) —

    The name of the flow definition.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13495

def describe_flow_definition(params = {}, options = {})
  req = build_request(:describe_flow_definition, params)
  req.send_request(options)
end

#describe_hub(params = {}) ⇒ Types::DescribeHubResponse

Describes a hub.

Examples:

Request syntax with placeholder values


resp = client.describe_hub({
  hub_name: "HubNameOrArn", # required
})

Response structure


resp.hub_name #=> String
resp.hub_arn #=> String
resp.hub_display_name #=> String
resp.hub_description #=> String
resp.hub_search_keywords #=> Array
resp.hub_search_keywords[0] #=> String
resp.s3_storage_config.s3_output_path #=> String
resp.hub_status #=> String, one of "InService", "Creating", "Updating", "Deleting", "CreateFailed", "UpdateFailed", "DeleteFailed"
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13542

def describe_hub(params = {}, options = {})
  req = build_request(:describe_hub, params)
  req.send_request(options)
end

#describe_hub_content(params = {}) ⇒ Types::DescribeHubContentResponse

Describe the content of a hub.

Examples:

Request syntax with placeholder values


resp = client.describe_hub_content({
  hub_name: "HubNameOrArn", # required
  hub_content_type: "Model", # required, accepts Model, Notebook, ModelReference
  hub_content_name: "HubContentName", # required
  hub_content_version: "HubContentVersion",
})

Response structure


resp.hub_content_name #=> String
resp.hub_content_arn #=> String
resp.hub_content_version #=> String
resp.hub_content_type #=> String, one of "Model", "Notebook", "ModelReference"
resp.document_schema_version #=> String
resp.hub_name #=> String
resp.hub_arn #=> String
resp.hub_content_display_name #=> String
resp.hub_content_description #=> String
resp.hub_content_markdown #=> String
resp.hub_content_document #=> String
resp.sage_maker_public_hub_content_arn #=> String
resp.reference_min_version #=> String
resp.support_status #=> String, one of "Supported", "Deprecated"
resp.hub_content_search_keywords #=> Array
resp.hub_content_search_keywords[0] #=> String
resp.hub_content_dependencies #=> Array
resp.hub_content_dependencies[0].dependency_origin_path #=> String
resp.hub_content_dependencies[0].dependency_copy_path #=> String
resp.hub_content_status #=> String, one of "Available", "Importing", "Deleting", "ImportFailed", "DeleteFailed"
resp.failure_reason #=> String
resp.creation_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub that contains the content to describe.

  • :hub_content_type (required, String) —

    The type of content in the hub.

  • :hub_content_name (required, String) —

    The name of the content to describe.

  • :hub_content_version (String) —

    The version of the content to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13621

def describe_hub_content(params = {}, options = {})
  req = build_request(:describe_hub_content, params)
  req.send_request(options)
end

#describe_human_task_ui(params = {}) ⇒ Types::DescribeHumanTaskUiResponse

Returns information about the requested human task user interface (worker task template).

Examples:

Request syntax with placeholder values


resp = client.describe_human_task_ui({
  human_task_ui_name: "HumanTaskUiName", # required
})

Response structure


resp.human_task_ui_arn #=> String
resp.human_task_ui_name #=> String
resp.human_task_ui_status #=> String, one of "Active", "Deleting"
resp.creation_time #=> Time
resp.ui_template.url #=> String
resp.ui_template.content_sha_256 #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :human_task_ui_name (required, String) —

    The name of the human task user interface (worker task template) you want information about.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13660

def describe_human_task_ui(params = {}, options = {})
  req = build_request(:describe_human_task_ui, params)
  req.send_request(options)
end

#describe_hyper_parameter_tuning_job(params = {}) ⇒ Types::DescribeHyperParameterTuningJobResponse

Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.

Examples:

Request syntax with placeholder values


resp = client.describe_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
})

Response structure


resp.hyper_parameter_tuning_job_name #=> String
resp.hyper_parameter_tuning_job_arn #=> String
resp.hyper_parameter_tuning_job_config.strategy #=> String, one of "Bayesian", "Random", "Hyperband", "Grid"
resp.hyper_parameter_tuning_job_config.strategy_config.hyperband_strategy_config.min_resource #=> Integer
resp.hyper_parameter_tuning_job_config.strategy_config.hyperband_strategy_config.max_resource #=> Integer
resp.hyper_parameter_tuning_job_config.hyper_parameter_tuning_job_objective.type #=> String, one of "Maximize", "Minimize"
resp.hyper_parameter_tuning_job_config.hyper_parameter_tuning_job_objective.metric_name #=> String
resp.hyper_parameter_tuning_job_config.resource_limits.max_number_of_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_config.resource_limits.max_parallel_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_config.resource_limits.max_runtime_in_seconds #=> Integer
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].min_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].max_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].min_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].max_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].values #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.auto_parameters #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.auto_parameters[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.auto_parameters[0].value_hint #=> String
resp.hyper_parameter_tuning_job_config.training_job_early_stopping_type #=> String, one of "Off", "Auto"
resp.hyper_parameter_tuning_job_config.tuning_job_completion_criteria.target_objective_metric_value #=> Float
resp.hyper_parameter_tuning_job_config.tuning_job_completion_criteria.best_objective_not_improving.max_number_of_training_jobs_not_improving #=> Integer
resp.hyper_parameter_tuning_job_config.tuning_job_completion_criteria.convergence_detected.complete_on_convergence #=> String, one of "Disabled", "Enabled"
resp.hyper_parameter_tuning_job_config.random_seed #=> Integer
resp.training_job_definition.definition_name #=> String
resp.training_job_definition.tuning_objective.type #=> String, one of "Maximize", "Minimize"
resp.training_job_definition.tuning_objective.metric_name #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges #=> Array
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].min_value #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].max_value #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges #=> Array
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].min_value #=> String
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].max_value #=> String
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges #=> Array
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].values #=> Array
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String
resp.training_job_definition.hyper_parameter_ranges.auto_parameters #=> Array
resp.training_job_definition.hyper_parameter_ranges.auto_parameters[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.auto_parameters[0].value_hint #=> String
resp.training_job_definition.static_hyper_parameters #=> Hash
resp.training_job_definition.static_hyper_parameters["HyperParameterKey"] #=> String
resp.training_job_definition.algorithm_specification.training_image #=> String
resp.training_job_definition.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.training_job_definition.algorithm_specification.algorithm_name #=> String
resp.training_job_definition.algorithm_specification.metric_definitions #=> Array
resp.training_job_definition.algorithm_specification.metric_definitions[0].name #=> String
resp.training_job_definition.algorithm_specification.metric_definitions[0].regex #=> String
resp.training_job_definition.role_arn #=> String
resp.training_job_definition.input_data_config #=> Array
resp.training_job_definition.input_data_config[0].channel_name #=> String
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.instance_group_names #=> Array
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.instance_group_names[0] #=> String
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.training_job_definition.input_data_config[0].content_type #=> String
resp.training_job_definition.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.training_job_definition.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.training_job_definition.input_data_config[0].input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.training_job_definition.input_data_config[0].shuffle_config.seed #=> Integer
resp.training_job_definition.vpc_config.security_group_ids #=> Array
resp.training_job_definition.vpc_config.security_group_ids[0] #=> String
resp.training_job_definition.vpc_config.subnets #=> Array
resp.training_job_definition.vpc_config.subnets[0] #=> String
resp.training_job_definition.output_data_config.kms_key_id #=> String
resp.training_job_definition.output_data_config.s3_output_path #=> String
resp.training_job_definition.output_data_config.compression_type #=> String, one of "GZIP", "NONE"
resp.training_job_definition.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definition.resource_config.instance_count #=> Integer
resp.training_job_definition.resource_config.volume_size_in_gb #=> Integer
resp.training_job_definition.resource_config.volume_kms_key_id #=> String
resp.training_job_definition.resource_config.keep_alive_period_in_seconds #=> Integer
resp.training_job_definition.resource_config.instance_groups #=> Array
resp.training_job_definition.resource_config.instance_groups[0].instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definition.resource_config.instance_groups[0].instance_count #=> Integer
resp.training_job_definition.resource_config.instance_groups[0].instance_group_name #=> String
resp.training_job_definition.hyper_parameter_tuning_resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definition.hyper_parameter_tuning_resource_config.instance_count #=> Integer
resp.training_job_definition.hyper_parameter_tuning_resource_config.volume_size_in_gb #=> Integer
resp.training_job_definition.hyper_parameter_tuning_resource_config.volume_kms_key_id #=> String
resp.training_job_definition.hyper_parameter_tuning_resource_config.allocation_strategy #=> String, one of "Prioritized"
resp.training_job_definition.hyper_parameter_tuning_resource_config.instance_configs #=> Array
resp.training_job_definition.hyper_parameter_tuning_resource_config.instance_configs[0].instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definition.hyper_parameter_tuning_resource_config.instance_configs[0].instance_count #=> Integer
resp.training_job_definition.hyper_parameter_tuning_resource_config.instance_configs[0].volume_size_in_gb #=> Integer
resp.training_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer
resp.training_job_definition.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.training_job_definition.stopping_condition.max_pending_time_in_seconds #=> Integer
resp.training_job_definition.enable_network_isolation #=> Boolean
resp.training_job_definition.enable_inter_container_traffic_encryption #=> Boolean
resp.training_job_definition.enable_managed_spot_training #=> Boolean
resp.training_job_definition.checkpoint_config.s3_uri #=> String
resp.training_job_definition.checkpoint_config.local_path #=> String
resp.training_job_definition.retry_strategy.maximum_retry_attempts #=> Integer
resp.training_job_definition.environment #=> Hash
resp.training_job_definition.environment["HyperParameterTrainingJobEnvironmentKey"] #=> String
resp.training_job_definitions #=> Array
resp.training_job_definitions[0].definition_name #=> String
resp.training_job_definitions[0].tuning_objective.type #=> String, one of "Maximize", "Minimize"
resp.training_job_definitions[0].tuning_objective.metric_name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].min_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].max_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].min_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].max_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].values #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.auto_parameters #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.auto_parameters[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.auto_parameters[0].value_hint #=> String
resp.training_job_definitions[0].static_hyper_parameters #=> Hash
resp.training_job_definitions[0].static_hyper_parameters["HyperParameterKey"] #=> String
resp.training_job_definitions[0].algorithm_specification.training_image #=> String
resp.training_job_definitions[0].algorithm_specification.training_input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.training_job_definitions[0].algorithm_specification.algorithm_name #=> String
resp.training_job_definitions[0].algorithm_specification.metric_definitions #=> Array
resp.training_job_definitions[0].algorithm_specification.metric_definitions[0].name #=> String
resp.training_job_definitions[0].algorithm_specification.metric_definitions[0].regex #=> String
resp.training_job_definitions[0].role_arn #=> String
resp.training_job_definitions[0].input_data_config #=> Array
resp.training_job_definitions[0].input_data_config[0].channel_name #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.instance_group_names #=> Array
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.instance_group_names[0] #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.training_job_definitions[0].input_data_config[0].content_type #=> String
resp.training_job_definitions[0].input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.training_job_definitions[0].input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.training_job_definitions[0].input_data_config[0].input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.training_job_definitions[0].input_data_config[0].shuffle_config.seed #=> Integer
resp.training_job_definitions[0].vpc_config.security_group_ids #=> Array
resp.training_job_definitions[0].vpc_config.security_group_ids[0] #=> String
resp.training_job_definitions[0].vpc_config.subnets #=> Array
resp.training_job_definitions[0].vpc_config.subnets[0] #=> String
resp.training_job_definitions[0].output_data_config.kms_key_id #=> String
resp.training_job_definitions[0].output_data_config.s3_output_path #=> String
resp.training_job_definitions[0].output_data_config.compression_type #=> String, one of "GZIP", "NONE"
resp.training_job_definitions[0].resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definitions[0].resource_config.instance_count #=> Integer
resp.training_job_definitions[0].resource_config.volume_size_in_gb #=> Integer
resp.training_job_definitions[0].resource_config.volume_kms_key_id #=> String
resp.training_job_definitions[0].resource_config.keep_alive_period_in_seconds #=> Integer
resp.training_job_definitions[0].resource_config.instance_groups #=> Array
resp.training_job_definitions[0].resource_config.instance_groups[0].instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definitions[0].resource_config.instance_groups[0].instance_count #=> Integer
resp.training_job_definitions[0].resource_config.instance_groups[0].instance_group_name #=> String
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.instance_count #=> Integer
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.volume_size_in_gb #=> Integer
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.volume_kms_key_id #=> String
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.allocation_strategy #=> String, one of "Prioritized"
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.instance_configs #=> Array
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.instance_configs[0].instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.instance_configs[0].instance_count #=> Integer
resp.training_job_definitions[0].hyper_parameter_tuning_resource_config.instance_configs[0].volume_size_in_gb #=> Integer
resp.training_job_definitions[0].stopping_condition.max_runtime_in_seconds #=> Integer
resp.training_job_definitions[0].stopping_condition.max_wait_time_in_seconds #=> Integer
resp.training_job_definitions[0].stopping_condition.max_pending_time_in_seconds #=> Integer
resp.training_job_definitions[0].enable_network_isolation #=> Boolean
resp.training_job_definitions[0].enable_inter_container_traffic_encryption #=> Boolean
resp.training_job_definitions[0].enable_managed_spot_training #=> Boolean
resp.training_job_definitions[0].checkpoint_config.s3_uri #=> String
resp.training_job_definitions[0].checkpoint_config.local_path #=> String
resp.training_job_definitions[0].retry_strategy.maximum_retry_attempts #=> Integer
resp.training_job_definitions[0].environment #=> Hash
resp.training_job_definitions[0].environment["HyperParameterTrainingJobEnvironmentKey"] #=> String
resp.hyper_parameter_tuning_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping", "Deleting", "DeleteFailed"
resp.creation_time #=> Time
resp.hyper_parameter_tuning_end_time #=> Time
resp.last_modified_time #=> Time
resp.training_job_status_counters.completed #=> Integer
resp.training_job_status_counters.in_progress #=> Integer
resp.training_job_status_counters.retryable_error #=> Integer
resp.training_job_status_counters.non_retryable_error #=> Integer
resp.training_job_status_counters.stopped #=> Integer
resp.objective_status_counters.succeeded #=> Integer
resp.objective_status_counters.pending #=> Integer
resp.objective_status_counters.failed #=> Integer
resp.best_training_job.training_job_definition_name #=> String
resp.best_training_job.training_job_name #=> String
resp.best_training_job.training_job_arn #=> String
resp.best_training_job.tuning_job_name #=> String
resp.best_training_job.creation_time #=> Time
resp.best_training_job.training_start_time #=> Time
resp.best_training_job.training_end_time #=> Time
resp.best_training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.best_training_job.tuned_hyper_parameters #=> Hash
resp.best_training_job.tuned_hyper_parameters["HyperParameterKey"] #=> String
resp.best_training_job.failure_reason #=> String
resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String
resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.value #=> Float
resp.best_training_job.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.overall_best_training_job.training_job_definition_name #=> String
resp.overall_best_training_job.training_job_name #=> String
resp.overall_best_training_job.training_job_arn #=> String
resp.overall_best_training_job.tuning_job_name #=> String
resp.overall_best_training_job.creation_time #=> Time
resp.overall_best_training_job.training_start_time #=> Time
resp.overall_best_training_job.training_end_time #=> Time
resp.overall_best_training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.overall_best_training_job.tuned_hyper_parameters #=> Hash
resp.overall_best_training_job.tuned_hyper_parameters["HyperParameterKey"] #=> String
resp.overall_best_training_job.failure_reason #=> String
resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String
resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.value #=> Float
resp.overall_best_training_job.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.warm_start_config.parent_hyper_parameter_tuning_jobs #=> Array
resp.warm_start_config.parent_hyper_parameter_tuning_jobs[0].hyper_parameter_tuning_job_name #=> String
resp.warm_start_config.warm_start_type #=> String, one of "IdenticalDataAndAlgorithm", "TransferLearning"
resp.autotune.mode #=> String, one of "Enabled"
resp.failure_reason #=> String
resp.tuning_job_completion_details.number_of_training_jobs_objective_not_improving #=> Integer
resp.tuning_job_completion_details.convergence_detected_time #=> Time
resp.consumed_resources.runtime_in_seconds #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hyper_parameter_tuning_job_name (required, String) —

    The name of the tuning job.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 13955

def describe_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:describe_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#describe_image(params = {}) ⇒ Types::DescribeImageResponse

Describes a SageMaker image.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* image_created
* image_deleted
* image_updated

Examples:

Request syntax with placeholder values


resp = client.describe_image({
  image_name: "ImageName", # required
})

Response structure


resp.creation_time #=> Time
resp.description #=> String
resp.display_name #=> String
resp.failure_reason #=> String
resp.image_arn #=> String
resp.image_name #=> String
resp.image_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "UPDATING", "UPDATE_FAILED", "DELETING", "DELETE_FAILED"
resp.last_modified_time #=> Time
resp.role_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image_name (required, String) —

    The name of the image to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14006

def describe_image(params = {}, options = {})
  req = build_request(:describe_image, params)
  req.send_request(options)
end

#describe_image_version(params = {}) ⇒ Types::DescribeImageVersionResponse

Describes a version of a SageMaker image.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* image_version_created
* image_version_deleted

Examples:

Request syntax with placeholder values


resp = client.describe_image_version({
  image_name: "ImageName", # required
  version: 1,
  alias: "SageMakerImageVersionAlias",
})

Response structure


resp.base_image #=> String
resp.container_image #=> String
resp.creation_time #=> Time
resp.failure_reason #=> String
resp.image_arn #=> String
resp.image_version_arn #=> String
resp.image_version_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "DELETING", "DELETE_FAILED"
resp.last_modified_time #=> Time
resp.version #=> Integer
resp.vendor_guidance #=> String, one of "NOT_PROVIDED", "STABLE", "TO_BE_ARCHIVED", "ARCHIVED"
resp.job_type #=> String, one of "TRAINING", "INFERENCE", "NOTEBOOK_KERNEL"
resp.ml_framework #=> String
resp.programming_lang #=> String
resp.processor #=> String, one of "CPU", "GPU"
resp.horovod #=> Boolean
resp.release_notes #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image_name (required, String) —

    The name of the image.

  • :version (Integer) —

    The version of the image. If not specified, the latest version is described.

  • :alias (String) —

    The alias of the image version.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14079

def describe_image_version(params = {}, options = {})
  req = build_request(:describe_image_version, params)
  req.send_request(options)
end

#describe_inference_component(params = {}) ⇒ Types::DescribeInferenceComponentOutput

Returns information about an inference component.

Examples:

Request syntax with placeholder values


resp = client.describe_inference_component({
  inference_component_name: "InferenceComponentName", # required
})

Response structure


resp.inference_component_name #=> String
resp.inference_component_arn #=> String
resp.endpoint_name #=> String
resp.endpoint_arn #=> String
resp.variant_name #=> String
resp.failure_reason #=> String
resp.specification.model_name #=> String
resp.specification.container.deployed_image.specified_image #=> String
resp.specification.container.deployed_image.resolved_image #=> String
resp.specification.container.deployed_image.resolution_time #=> Time
resp.specification.container.artifact_url #=> String
resp.specification.container.environment #=> Hash
resp.specification.container.environment["EnvironmentKey"] #=> String
resp.specification.startup_parameters.model_data_download_timeout_in_seconds #=> Integer
resp.specification.startup_parameters.container_startup_health_check_timeout_in_seconds #=> Integer
resp.specification.compute_resource_requirements.number_of_cpu_cores_required #=> Float
resp.specification.compute_resource_requirements.number_of_accelerator_devices_required #=> Float
resp.specification.compute_resource_requirements.min_memory_required_in_mb #=> Integer
resp.specification.compute_resource_requirements.max_memory_required_in_mb #=> Integer
resp.runtime_config.desired_copy_count #=> Integer
resp.runtime_config.current_copy_count #=> Integer
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.inference_component_status #=> String, one of "InService", "Creating", "Updating", "Failed", "Deleting"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :inference_component_name (required, String) —

    The name of the inference component.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14140

def describe_inference_component(params = {}, options = {})
  req = build_request(:describe_inference_component, params)
  req.send_request(options)
end

#describe_inference_experiment(params = {}) ⇒ Types::DescribeInferenceExperimentResponse

Returns details about an inference experiment.

Examples:

Request syntax with placeholder values


resp = client.describe_inference_experiment({
  name: "InferenceExperimentName", # required
})

Response structure


resp.arn #=> String
resp.name #=> String
resp.type #=> String, one of "ShadowMode"
resp.schedule.start_time #=> Time
resp.schedule.end_time #=> Time
resp.status #=> String, one of "Creating", "Created", "Updating", "Running", "Starting", "Stopping", "Completed", "Cancelled"
resp.status_reason #=> String
resp.description #=> String
resp.creation_time #=> Time
resp.completion_time #=> Time
resp.last_modified_time #=> Time
resp.role_arn #=> String
resp.endpoint_metadata.endpoint_name #=> String
resp.endpoint_metadata.endpoint_config_name #=> String
resp.endpoint_metadata.endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed", "UpdateRollbackFailed"
resp.endpoint_metadata.failure_reason #=> String
resp.model_variants #=> Array
resp.model_variants[0].model_name #=> String
resp.model_variants[0].variant_name #=> String
resp.model_variants[0].infrastructure_config.infrastructure_type #=> String, one of "RealTimeInference"
resp.model_variants[0].infrastructure_config.real_time_inference_config.instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge"
resp.model_variants[0].infrastructure_config.real_time_inference_config.instance_count #=> Integer
resp.model_variants[0].status #=> String, one of "Creating", "Updating", "InService", "Deleting", "Deleted"
resp.data_storage_config.destination #=> String
resp.data_storage_config.kms_key #=> String
resp.data_storage_config.content_type.csv_content_types #=> Array
resp.data_storage_config.content_type.csv_content_types[0] #=> String
resp.data_storage_config.content_type.json_content_types #=> Array
resp.data_storage_config.content_type.json_content_types[0] #=> String
resp.shadow_mode_config.source_model_variant_name #=> String
resp.shadow_mode_config.shadow_model_variants #=> Array
resp.shadow_mode_config.shadow_model_variants[0].shadow_model_variant_name #=> String
resp.shadow_mode_config.shadow_model_variants[0].sampling_percentage #=> Integer
resp.kms_key #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the inference experiment to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14216

def describe_inference_experiment(params = {}, options = {})
  req = build_request(:describe_inference_experiment, params)
  req.send_request(options)
end

#describe_inference_recommendations_job(params = {}) ⇒ Types::DescribeInferenceRecommendationsJobResponse

Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.

Examples:

Request syntax with placeholder values


resp = client.describe_inference_recommendations_job({
  job_name: "RecommendationJobName", # required
})

Response structure


resp.job_name #=> String
resp.job_description #=> String
resp.job_type #=> String, one of "Default", "Advanced"
resp.job_arn #=> String
resp.role_arn #=> String
resp.status #=> String, one of "PENDING", "IN_PROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED", "DELETING", "DELETED"
resp.creation_time #=> Time
resp.completion_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.input_config.model_package_version_arn #=> String
resp.input_config.model_name #=> String
resp.input_config.job_duration_in_seconds #=> Integer
resp.input_config.traffic_pattern.traffic_type #=> String, one of "PHASES", "STAIRS"
resp.input_config.traffic_pattern.phases #=> Array
resp.input_config.traffic_pattern.phases[0].initial_number_of_users #=> Integer
resp.input_config.traffic_pattern.phases[0].spawn_rate #=> Integer
resp.input_config.traffic_pattern.phases[0].duration_in_seconds #=> Integer
resp.input_config.traffic_pattern.stairs.duration_in_seconds #=> Integer
resp.input_config.traffic_pattern.stairs.number_of_steps #=> Integer
resp.input_config.traffic_pattern.stairs.users_per_step #=> Integer
resp.input_config.resource_limit.max_number_of_tests #=> Integer
resp.input_config.resource_limit.max_parallel_of_tests #=> Integer
resp.input_config.endpoint_configurations #=> Array
resp.input_config.endpoint_configurations[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.input_config.endpoint_configurations[0].serverless_config.memory_size_in_mb #=> Integer
resp.input_config.endpoint_configurations[0].serverless_config.max_concurrency #=> Integer
resp.input_config.endpoint_configurations[0].serverless_config.provisioned_concurrency #=> Integer
resp.input_config.endpoint_configurations[0].inference_specification_name #=> String
resp.input_config.endpoint_configurations[0].environment_parameter_ranges.categorical_parameter_ranges #=> Array
resp.input_config.endpoint_configurations[0].environment_parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.input_config.endpoint_configurations[0].environment_parameter_ranges.categorical_parameter_ranges[0].value #=> Array
resp.input_config.endpoint_configurations[0].environment_parameter_ranges.categorical_parameter_ranges[0].value[0] #=> String
resp.input_config.volume_kms_key_id #=> String
resp.input_config.container_config.domain #=> String
resp.input_config.container_config.task #=> String
resp.input_config.container_config.framework #=> String
resp.input_config.container_config.framework_version #=> String
resp.input_config.container_config.payload_config.sample_payload_url #=> String
resp.input_config.container_config.payload_config.supported_content_types #=> Array
resp.input_config.container_config.payload_config.supported_content_types[0] #=> String
resp.input_config.container_config.nearest_model_name #=> String
resp.input_config.container_config.supported_instance_types #=> Array
resp.input_config.container_config.supported_instance_types[0] #=> String
resp.input_config.container_config.supported_endpoint_type #=> String, one of "RealTime", "Serverless"
resp.input_config.container_config.data_input_config #=> String
resp.input_config.container_config.supported_response_mime_types #=> Array
resp.input_config.container_config.supported_response_mime_types[0] #=> String
resp.input_config.endpoints #=> Array
resp.input_config.endpoints[0].endpoint_name #=> String
resp.input_config.vpc_config.security_group_ids #=> Array
resp.input_config.vpc_config.security_group_ids[0] #=> String
resp.input_config.vpc_config.subnets #=> Array
resp.input_config.vpc_config.subnets[0] #=> String
resp.stopping_conditions.max_invocations #=> Integer
resp.stopping_conditions.model_latency_thresholds #=> Array
resp.stopping_conditions.model_latency_thresholds[0].percentile #=> String
resp.stopping_conditions.model_latency_thresholds[0].value_in_milliseconds #=> Integer
resp.stopping_conditions.flat_invocations #=> String, one of "Continue", "Stop"
resp.inference_recommendations #=> Array
resp.inference_recommendations[0].recommendation_id #=> String
resp.inference_recommendations[0].metrics.cost_per_hour #=> Float
resp.inference_recommendations[0].metrics.cost_per_inference #=> Float
resp.inference_recommendations[0].metrics.max_invocations #=> Integer
resp.inference_recommendations[0].metrics.model_latency #=> Integer
resp.inference_recommendations[0].metrics.cpu_utilization #=> Float
resp.inference_recommendations[0].metrics.memory_utilization #=> Float
resp.inference_recommendations[0].metrics.model_setup_time #=> Integer
resp.inference_recommendations[0].endpoint_configuration.endpoint_name #=> String
resp.inference_recommendations[0].endpoint_configuration.variant_name #=> String
resp.inference_recommendations[0].endpoint_configuration.instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.inference_recommendations[0].endpoint_configuration.initial_instance_count #=> Integer
resp.inference_recommendations[0].endpoint_configuration.serverless_config.memory_size_in_mb #=> Integer
resp.inference_recommendations[0].endpoint_configuration.serverless_config.max_concurrency #=> Integer
resp.inference_recommendations[0].endpoint_configuration.serverless_config.provisioned_concurrency #=> Integer
resp.inference_recommendations[0].model_configuration.inference_specification_name #=> String
resp.inference_recommendations[0].model_configuration.environment_parameters #=> Array
resp.inference_recommendations[0].model_configuration.environment_parameters[0].key #=> String
resp.inference_recommendations[0].model_configuration.environment_parameters[0].value_type #=> String
resp.inference_recommendations[0].model_configuration.environment_parameters[0].value #=> String
resp.inference_recommendations[0].model_configuration.compilation_job_name #=> String
resp.inference_recommendations[0].invocation_end_time #=> Time
resp.inference_recommendations[0].invocation_start_time #=> Time
resp.endpoint_performances #=> Array
resp.endpoint_performances[0].metrics.max_invocations #=> Integer
resp.endpoint_performances[0].metrics.model_latency #=> Integer
resp.endpoint_performances[0].endpoint_info.endpoint_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_name (required, String) —

    The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14345

def describe_inference_recommendations_job(params = {}, options = {})
  req = build_request(:describe_inference_recommendations_job, params)
  req.send_request(options)
end

#describe_labeling_job(params = {}) ⇒ Types::DescribeLabelingJobResponse

Gets information about a labeling job.

Examples:

Request syntax with placeholder values


resp = client.describe_labeling_job({
  labeling_job_name: "LabelingJobName", # required
})

Response structure


resp.labeling_job_status #=> String, one of "Initializing", "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.label_counters.total_labeled #=> Integer
resp.label_counters.human_labeled #=> Integer
resp.label_counters.machine_labeled #=> Integer
resp.label_counters.failed_non_retryable_error #=> Integer
resp.label_counters.unlabeled #=> Integer
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.job_reference_code #=> String
resp.labeling_job_name #=> String
resp.labeling_job_arn #=> String
resp.label_attribute_name #=> String
resp.input_config.data_source.s3_data_source.manifest_s3_uri #=> String
resp.input_config.data_source.sns_data_source.sns_topic_arn #=> String
resp.input_config.data_attributes.content_classifiers #=> Array
resp.input_config.data_attributes.content_classifiers[0] #=> String, one of "FreeOfPersonallyIdentifiableInformation", "FreeOfAdultContent"
resp.output_config.s3_output_path #=> String
resp.output_config.kms_key_id #=> String
resp.output_config.sns_topic_arn #=> String
resp.role_arn #=> String
resp.label_category_config_s3_uri #=> String
resp.stopping_conditions.max_human_labeled_object_count #=> Integer
resp.stopping_conditions.max_percentage_of_input_dataset_labeled #=> Integer
resp.labeling_job_algorithms_config.labeling_job_algorithm_specification_arn #=> String
resp.labeling_job_algorithms_config.initial_active_learning_model_arn #=> String
resp.labeling_job_algorithms_config.labeling_job_resource_config.volume_kms_key_id #=> String
resp.labeling_job_algorithms_config.labeling_job_resource_config.vpc_config.security_group_ids #=> Array
resp.labeling_job_algorithms_config.labeling_job_resource_config.vpc_config.security_group_ids[0] #=> String
resp.labeling_job_algorithms_config.labeling_job_resource_config.vpc_config.subnets #=> Array
resp.labeling_job_algorithms_config.labeling_job_resource_config.vpc_config.subnets[0] #=> String
resp.human_task_config.workteam_arn #=> String
resp.human_task_config.ui_config.ui_template_s3_uri #=> String
resp.human_task_config.ui_config.human_task_ui_arn #=> String
resp.human_task_config.pre_human_task_lambda_arn #=> String
resp.human_task_config.task_keywords #=> Array
resp.human_task_config.task_keywords[0] #=> String
resp.human_task_config.task_title #=> String
resp.human_task_config.task_description #=> String
resp.human_task_config.number_of_human_workers_per_data_object #=> Integer
resp.human_task_config.task_time_limit_in_seconds #=> Integer
resp.human_task_config.task_availability_lifetime_in_seconds #=> Integer
resp.human_task_config.max_concurrent_task_count #=> Integer
resp.human_task_config.annotation_consolidation_config.annotation_consolidation_lambda_arn #=> String
resp.human_task_config.public_workforce_task_price.amount_in_usd.dollars #=> Integer
resp.human_task_config.public_workforce_task_price.amount_in_usd.cents #=> Integer
resp.human_task_config.public_workforce_task_price.amount_in_usd.tenth_fractions_of_a_cent #=> Integer
resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String
resp.labeling_job_output.output_dataset_s3_uri #=> String
resp.labeling_job_output.final_active_learning_model_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :labeling_job_name (required, String) —

    The name of the labeling job to return information for.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14441

def describe_labeling_job(params = {}, options = {})
  req = build_request(:describe_labeling_job, params)
  req.send_request(options)
end

#describe_lineage_group(params = {}) ⇒ Types::DescribeLineageGroupResponse

Provides a list of properties for the requested lineage group. For more information, see [ Cross-Account Lineage Tracking ][1] in the *Amazon SageMaker Developer Guide*.

[1]: docs.aws.amazon.com/sagemaker/latest/dg/xaccount-lineage-tracking.html

Examples:

Request syntax with placeholder values


resp = client.describe_lineage_group({
  lineage_group_name: "ExperimentEntityName", # required
})

Response structure


resp.lineage_group_name #=> String
resp.lineage_group_arn #=> String
resp.display_name #=> String
resp.description #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :lineage_group_name (required, String) —

    The name of the lineage group.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14499

def describe_lineage_group(params = {}, options = {})
  req = build_request(:describe_lineage_group, params)
  req.send_request(options)
end

#describe_mlflow_tracking_server(params = {}) ⇒ Types::DescribeMlflowTrackingServerResponse

Returns information about an MLflow Tracking Server.

Examples:

Request syntax with placeholder values


resp = client.describe_mlflow_tracking_server({
  tracking_server_name: "TrackingServerName", # required
})

Response structure


resp.tracking_server_arn #=> String
resp.tracking_server_name #=> String
resp.artifact_store_uri #=> String
resp.tracking_server_size #=> String, one of "Small", "Medium", "Large"
resp.mlflow_version #=> String
resp.role_arn #=> String
resp.tracking_server_status #=> String, one of "Creating", "Created", "CreateFailed", "Updating", "Updated", "UpdateFailed", "Deleting", "DeleteFailed", "Stopping", "Stopped", "StopFailed", "Starting", "Started", "StartFailed", "MaintenanceInProgress", "MaintenanceComplete", "MaintenanceFailed"
resp.is_active #=> String, one of "Active", "Inactive"
resp.tracking_server_url #=> String
resp.weekly_maintenance_window_start #=> String
resp.automatic_model_registration #=> Boolean
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    The name of the MLflow Tracking Server to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14565

def describe_mlflow_tracking_server(params = {}, options = {})
  req = build_request(:describe_mlflow_tracking_server, params)
  req.send_request(options)
end

#describe_model(params = {}) ⇒ Types::DescribeModelOutput

Describes a model that you created using the ‘CreateModel` API.

Examples:

Request syntax with placeholder values


resp = client.describe_model({
  model_name: "ModelName", # required
})

Response structure


resp.model_name #=> String
resp.primary_container.container_hostname #=> String
resp.primary_container.image #=> String
resp.primary_container.image_config.repository_access_mode #=> String, one of "Platform", "Vpc"
resp.primary_container.image_config.repository_auth_config.repository_credentials_provider_arn #=> String
resp.primary_container.mode #=> String, one of "SingleModel", "MultiModel"
resp.primary_container.model_data_url #=> String
resp.primary_container.model_data_source.s3_data_source.s3_uri #=> String
resp.primary_container.model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.primary_container.model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.primary_container.model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.primary_container.model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.primary_container.additional_model_data_sources #=> Array
resp.primary_container.additional_model_data_sources[0].channel_name #=> String
resp.primary_container.additional_model_data_sources[0].s3_data_source.s3_uri #=> String
resp.primary_container.additional_model_data_sources[0].s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.primary_container.additional_model_data_sources[0].s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.primary_container.additional_model_data_sources[0].s3_data_source.model_access_config.accept_eula #=> Boolean
resp.primary_container.additional_model_data_sources[0].s3_data_source.hub_access_config.hub_content_arn #=> String
resp.primary_container.environment #=> Hash
resp.primary_container.environment["EnvironmentKey"] #=> String
resp.primary_container.model_package_name #=> String
resp.primary_container.inference_specification_name #=> String
resp.primary_container.multi_model_config.model_cache_setting #=> String, one of "Enabled", "Disabled"
resp.containers #=> Array
resp.containers[0].container_hostname #=> String
resp.containers[0].image #=> String
resp.containers[0].image_config.repository_access_mode #=> String, one of "Platform", "Vpc"
resp.containers[0].image_config.repository_auth_config.repository_credentials_provider_arn #=> String
resp.containers[0].mode #=> String, one of "SingleModel", "MultiModel"
resp.containers[0].model_data_url #=> String
resp.containers[0].model_data_source.s3_data_source.s3_uri #=> String
resp.containers[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.containers[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.containers[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.containers[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.containers[0].additional_model_data_sources #=> Array
resp.containers[0].additional_model_data_sources[0].channel_name #=> String
resp.containers[0].additional_model_data_sources[0].s3_data_source.s3_uri #=> String
resp.containers[0].additional_model_data_sources[0].s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.containers[0].additional_model_data_sources[0].s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.containers[0].additional_model_data_sources[0].s3_data_source.model_access_config.accept_eula #=> Boolean
resp.containers[0].additional_model_data_sources[0].s3_data_source.hub_access_config.hub_content_arn #=> String
resp.containers[0].environment #=> Hash
resp.containers[0].environment["EnvironmentKey"] #=> String
resp.containers[0].model_package_name #=> String
resp.containers[0].inference_specification_name #=> String
resp.containers[0].multi_model_config.model_cache_setting #=> String, one of "Enabled", "Disabled"
resp.inference_execution_config.mode #=> String, one of "Serial", "Direct"
resp.execution_role_arn #=> String
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String
resp.creation_time #=> Time
resp.model_arn #=> String
resp.enable_network_isolation #=> Boolean
resp.deployment_recommendation.recommendation_status #=> String, one of "IN_PROGRESS", "COMPLETED", "FAILED", "NOT_APPLICABLE"
resp.deployment_recommendation.real_time_inference_recommendations #=> Array
resp.deployment_recommendation.real_time_inference_recommendations[0].recommendation_id #=> String
resp.deployment_recommendation.real_time_inference_recommendations[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.deployment_recommendation.real_time_inference_recommendations[0].environment #=> Hash
resp.deployment_recommendation.real_time_inference_recommendations[0].environment["EnvironmentKey"] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_name (required, String) —

    The name of the model.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14664

def describe_model(params = {}, options = {})
  req = build_request(:describe_model, params)
  req.send_request(options)
end

#describe_model_bias_job_definition(params = {}) ⇒ Types::DescribeModelBiasJobDefinitionResponse

Returns a description of a model bias job definition.

Examples:

Request syntax with placeholder values


resp = client.describe_model_bias_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Response structure


resp.job_definition_arn #=> String
resp.job_definition_name #=> String
resp.creation_time #=> Time
resp.model_bias_baseline_config.baselining_job_name #=> String
resp.model_bias_baseline_config.constraints_resource.s3_uri #=> String
resp.model_bias_app_specification.image_uri #=> String
resp.model_bias_app_specification.config_uri #=> String
resp.model_bias_app_specification.environment #=> Hash
resp.model_bias_app_specification.environment["ProcessingEnvironmentKey"] #=> String
resp.model_bias_job_input.endpoint_input.endpoint_name #=> String
resp.model_bias_job_input.endpoint_input.local_path #=> String
resp.model_bias_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.model_bias_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.model_bias_job_input.endpoint_input.features_attribute #=> String
resp.model_bias_job_input.endpoint_input.inference_attribute #=> String
resp.model_bias_job_input.endpoint_input.probability_attribute #=> String
resp.model_bias_job_input.endpoint_input.probability_threshold_attribute #=> Float
resp.model_bias_job_input.endpoint_input.start_time_offset #=> String
resp.model_bias_job_input.endpoint_input.end_time_offset #=> String
resp.model_bias_job_input.endpoint_input.exclude_features_attribute #=> String
resp.model_bias_job_input.batch_transform_input.data_captured_destination_s3_uri #=> String
resp.model_bias_job_input.batch_transform_input.dataset_format.csv.header #=> Boolean
resp.model_bias_job_input.batch_transform_input.dataset_format.json.line #=> Boolean
resp.model_bias_job_input.batch_transform_input.local_path #=> String
resp.model_bias_job_input.batch_transform_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.model_bias_job_input.batch_transform_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.model_bias_job_input.batch_transform_input.features_attribute #=> String
resp.model_bias_job_input.batch_transform_input.inference_attribute #=> String
resp.model_bias_job_input.batch_transform_input.probability_attribute #=> String
resp.model_bias_job_input.batch_transform_input.probability_threshold_attribute #=> Float
resp.model_bias_job_input.batch_transform_input.start_time_offset #=> String
resp.model_bias_job_input.batch_transform_input.end_time_offset #=> String
resp.model_bias_job_input.batch_transform_input.exclude_features_attribute #=> String
resp.model_bias_job_input.ground_truth_s3_input.s3_uri #=> String
resp.model_bias_job_output_config.monitoring_outputs #=> Array
resp.model_bias_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String
resp.model_bias_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String
resp.model_bias_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob"
resp.model_bias_job_output_config.kms_key_id #=> String
resp.job_resources.cluster_config.instance_count #=> Integer
resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.job_resources.cluster_config.volume_size_in_gb #=> Integer
resp.job_resources.cluster_config.volume_kms_key_id #=> String
resp.network_config.enable_inter_container_traffic_encryption #=> Boolean
resp.network_config.enable_network_isolation #=> Boolean
resp.network_config.vpc_config.security_group_ids #=> Array
resp.network_config.vpc_config.security_group_ids[0] #=> String
resp.network_config.vpc_config.subnets #=> Array
resp.network_config.vpc_config.subnets[0] #=> String
resp.role_arn #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14754

def describe_model_bias_job_definition(params = {}, options = {})
  req = build_request(:describe_model_bias_job_definition, params)
  req.send_request(options)
end

#describe_model_card(params = {}) ⇒ Types::DescribeModelCardResponse

Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.

Examples:

Request syntax with placeholder values


resp = client.describe_model_card({
  model_card_name: "ModelCardNameOrArn", # required
  model_card_version: 1,
})

Response structure


resp.model_card_arn #=> String
resp.model_card_name #=> String
resp.model_card_version #=> Integer
resp.content #=> String
resp.model_card_status #=> String, one of "Draft", "PendingReview", "Approved", "Archived"
resp.security_config.kms_key_id #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.model_card_processing_status #=> String, one of "DeleteInProgress", "DeletePending", "ContentDeleted", "ExportJobsDeleted", "DeleteCompleted", "DeleteFailed"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_name (required, String) —

    The name or Amazon Resource Name (ARN) of the model card to describe.

  • :model_card_version (Integer) —

    The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14818

def describe_model_card(params = {}, options = {})
  req = build_request(:describe_model_card, params)
  req.send_request(options)
end

#describe_model_card_export_job(params = {}) ⇒ Types::DescribeModelCardExportJobResponse

Describes an Amazon SageMaker Model Card export job.

Examples:

Request syntax with placeholder values


resp = client.describe_model_card_export_job({
  model_card_export_job_arn: "ModelCardExportJobArn", # required
})

Response structure


resp.model_card_export_job_name #=> String
resp.model_card_export_job_arn #=> String
resp.status #=> String, one of "InProgress", "Completed", "Failed"
resp.model_card_name #=> String
resp.model_card_version #=> Integer
resp.output_config.s3_output_path #=> String
resp.created_at #=> Time
resp.last_modified_at #=> Time
resp.failure_reason #=> String
resp.export_artifacts.s3_export_artifacts #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_export_job_arn (required, String) —

    The Amazon Resource Name (ARN) of the model card export job to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14865

def describe_model_card_export_job(params = {}, options = {})
  req = build_request(:describe_model_card_export_job, params)
  req.send_request(options)
end

#describe_model_explainability_job_definition(params = {}) ⇒ Types::DescribeModelExplainabilityJobDefinitionResponse

Returns a description of a model explainability job definition.

Examples:

Request syntax with placeholder values


resp = client.describe_model_explainability_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Response structure


resp.job_definition_arn #=> String
resp.job_definition_name #=> String
resp.creation_time #=> Time
resp.model_explainability_baseline_config.baselining_job_name #=> String
resp.model_explainability_baseline_config.constraints_resource.s3_uri #=> String
resp.model_explainability_app_specification.image_uri #=> String
resp.model_explainability_app_specification.config_uri #=> String
resp.model_explainability_app_specification.environment #=> Hash
resp.model_explainability_app_specification.environment["ProcessingEnvironmentKey"] #=> String
resp.model_explainability_job_input.endpoint_input.endpoint_name #=> String
resp.model_explainability_job_input.endpoint_input.local_path #=> String
resp.model_explainability_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.model_explainability_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.model_explainability_job_input.endpoint_input.features_attribute #=> String
resp.model_explainability_job_input.endpoint_input.inference_attribute #=> String
resp.model_explainability_job_input.endpoint_input.probability_attribute #=> String
resp.model_explainability_job_input.endpoint_input.probability_threshold_attribute #=> Float
resp.model_explainability_job_input.endpoint_input.start_time_offset #=> String
resp.model_explainability_job_input.endpoint_input.end_time_offset #=> String
resp.model_explainability_job_input.endpoint_input.exclude_features_attribute #=> String
resp.model_explainability_job_input.batch_transform_input.data_captured_destination_s3_uri #=> String
resp.model_explainability_job_input.batch_transform_input.dataset_format.csv.header #=> Boolean
resp.model_explainability_job_input.batch_transform_input.dataset_format.json.line #=> Boolean
resp.model_explainability_job_input.batch_transform_input.local_path #=> String
resp.model_explainability_job_input.batch_transform_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.model_explainability_job_input.batch_transform_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.model_explainability_job_input.batch_transform_input.features_attribute #=> String
resp.model_explainability_job_input.batch_transform_input.inference_attribute #=> String
resp.model_explainability_job_input.batch_transform_input.probability_attribute #=> String
resp.model_explainability_job_input.batch_transform_input.probability_threshold_attribute #=> Float
resp.model_explainability_job_input.batch_transform_input.start_time_offset #=> String
resp.model_explainability_job_input.batch_transform_input.end_time_offset #=> String
resp.model_explainability_job_input.batch_transform_input.exclude_features_attribute #=> String
resp.model_explainability_job_output_config.monitoring_outputs #=> Array
resp.model_explainability_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String
resp.model_explainability_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String
resp.model_explainability_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob"
resp.model_explainability_job_output_config.kms_key_id #=> String
resp.job_resources.cluster_config.instance_count #=> Integer
resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.job_resources.cluster_config.volume_size_in_gb #=> Integer
resp.job_resources.cluster_config.volume_kms_key_id #=> String
resp.network_config.enable_inter_container_traffic_encryption #=> Boolean
resp.network_config.enable_network_isolation #=> Boolean
resp.network_config.vpc_config.security_group_ids #=> Array
resp.network_config.vpc_config.security_group_ids[0] #=> String
resp.network_config.vpc_config.subnets #=> Array
resp.network_config.vpc_config.subnets[0] #=> String
resp.role_arn #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 14954

def describe_model_explainability_job_definition(params = {}, options = {})
  req = build_request(:describe_model_explainability_job_definition, params)
  req.send_request(options)
end

#describe_model_package(params = {}) ⇒ Types::DescribeModelPackageOutput

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.

If you provided a KMS Key ID when you created your model package, you will see the [KMS Decrypt] API call in your CloudTrail logs when you use this API.

To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

[1]: docs.aws.amazon.com/kms/latest/APIReference/API_Decrypt.html

Examples:

Request syntax with placeholder values


resp = client.describe_model_package({
  model_package_name: "VersionedArnOrName", # required
})

Response structure


resp.model_package_name #=> String
resp.model_package_group_name #=> String
resp.model_package_version #=> Integer
resp.model_package_arn #=> String
resp.model_package_description #=> String
resp.creation_time #=> Time
resp.inference_specification.containers #=> Array
resp.inference_specification.containers[0].container_hostname #=> String
resp.inference_specification.containers[0].image #=> String
resp.inference_specification.containers[0].image_digest #=> String
resp.inference_specification.containers[0].model_data_url #=> String
resp.inference_specification.containers[0].model_data_source.s3_data_source.s3_uri #=> String
resp.inference_specification.containers[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.inference_specification.containers[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.inference_specification.containers[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.inference_specification.containers[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.inference_specification.containers[0].product_id #=> String
resp.inference_specification.containers[0].environment #=> Hash
resp.inference_specification.containers[0].environment["EnvironmentKey"] #=> String
resp.inference_specification.containers[0].model_input.data_input_config #=> String
resp.inference_specification.containers[0].framework #=> String
resp.inference_specification.containers[0].framework_version #=> String
resp.inference_specification.containers[0].nearest_model_name #=> String
resp.inference_specification.containers[0].additional_s3_data_source.s3_data_type #=> String, one of "S3Object", "S3Prefix"
resp.inference_specification.containers[0].additional_s3_data_source.s3_uri #=> String
resp.inference_specification.containers[0].additional_s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.inference_specification.supported_transform_instance_types #=> Array
resp.inference_specification.supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.inference_specification.supported_realtime_inference_instance_types #=> Array
resp.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.inference_specification.supported_content_types #=> Array
resp.inference_specification.supported_content_types[0] #=> String
resp.inference_specification.supported_response_mime_types #=> Array
resp.inference_specification.supported_response_mime_types[0] #=> String
resp.source_algorithm_specification.source_algorithms #=> Array
resp.source_algorithm_specification.source_algorithms[0].model_data_url #=> String
resp.source_algorithm_specification.source_algorithms[0].model_data_source.s3_data_source.s3_uri #=> String
resp.source_algorithm_specification.source_algorithms[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.source_algorithm_specification.source_algorithms[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.source_algorithm_specification.source_algorithms[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.source_algorithm_specification.source_algorithms[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.source_algorithm_specification.source_algorithms[0].algorithm_name #=> String
resp.validation_specification.validation_role #=> String
resp.validation_specification.validation_profiles #=> Array
resp.validation_specification.validation_profiles[0].profile_name #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash
resp.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String
resp.model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.model_package_status_details.validation_statuses #=> Array
resp.model_package_status_details.validation_statuses[0].name #=> String
resp.model_package_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.model_package_status_details.validation_statuses[0].failure_reason #=> String
resp.model_package_status_details.image_scan_statuses #=> Array
resp.model_package_status_details.image_scan_statuses[0].name #=> String
resp.model_package_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.model_package_status_details.image_scan_statuses[0].failure_reason #=> String
resp.certify_for_marketplace #=> Boolean
resp.model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval"
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.metadata_properties.commit_id #=> String
resp.metadata_properties.repository #=> String
resp.metadata_properties.generated_by #=> String
resp.metadata_properties.project_id #=> String
resp.model_metrics.model_quality.statistics.content_type #=> String
resp.model_metrics.model_quality.statistics.content_digest #=> String
resp.model_metrics.model_quality.statistics.s3_uri #=> String
resp.model_metrics.model_quality.constraints.content_type #=> String
resp.model_metrics.model_quality.constraints.content_digest #=> String
resp.model_metrics.model_quality.constraints.s3_uri #=> String
resp.model_metrics.model_data_quality.statistics.content_type #=> String
resp.model_metrics.model_data_quality.statistics.content_digest #=> String
resp.model_metrics.model_data_quality.statistics.s3_uri #=> String
resp.model_metrics.model_data_quality.constraints.content_type #=> String
resp.model_metrics.model_data_quality.constraints.content_digest #=> String
resp.model_metrics.model_data_quality.constraints.s3_uri #=> String
resp.model_metrics.bias.report.content_type #=> String
resp.model_metrics.bias.report.content_digest #=> String
resp.model_metrics.bias.report.s3_uri #=> String
resp.model_metrics.bias.pre_training_report.content_type #=> String
resp.model_metrics.bias.pre_training_report.content_digest #=> String
resp.model_metrics.bias.pre_training_report.s3_uri #=> String
resp.model_metrics.bias.post_training_report.content_type #=> String
resp.model_metrics.bias.post_training_report.content_digest #=> String
resp.model_metrics.bias.post_training_report.s3_uri #=> String
resp.model_metrics.explainability.report.content_type #=> String
resp.model_metrics.explainability.report.content_digest #=> String
resp.model_metrics.explainability.report.s3_uri #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.approval_description #=> String
resp.domain #=> String
resp.task #=> String
resp.sample_payload_url #=> String
resp.customer_metadata_properties #=> Hash
resp.customer_metadata_properties["CustomerMetadataKey"] #=> String
resp.drift_check_baselines.bias.config_file.content_type #=> String
resp.drift_check_baselines.bias.config_file.content_digest #=> String
resp.drift_check_baselines.bias.config_file.s3_uri #=> String
resp.drift_check_baselines.bias.pre_training_constraints.content_type #=> String
resp.drift_check_baselines.bias.pre_training_constraints.content_digest #=> String
resp.drift_check_baselines.bias.pre_training_constraints.s3_uri #=> String
resp.drift_check_baselines.bias.post_training_constraints.content_type #=> String
resp.drift_check_baselines.bias.post_training_constraints.content_digest #=> String
resp.drift_check_baselines.bias.post_training_constraints.s3_uri #=> String
resp.drift_check_baselines.explainability.constraints.content_type #=> String
resp.drift_check_baselines.explainability.constraints.content_digest #=> String
resp.drift_check_baselines.explainability.constraints.s3_uri #=> String
resp.drift_check_baselines.explainability.config_file.content_type #=> String
resp.drift_check_baselines.explainability.config_file.content_digest #=> String
resp.drift_check_baselines.explainability.config_file.s3_uri #=> String
resp.drift_check_baselines.model_quality.statistics.content_type #=> String
resp.drift_check_baselines.model_quality.statistics.content_digest #=> String
resp.drift_check_baselines.model_quality.statistics.s3_uri #=> String
resp.drift_check_baselines.model_quality.constraints.content_type #=> String
resp.drift_check_baselines.model_quality.constraints.content_digest #=> String
resp.drift_check_baselines.model_quality.constraints.s3_uri #=> String
resp.drift_check_baselines.model_data_quality.statistics.content_type #=> String
resp.drift_check_baselines.model_data_quality.statistics.content_digest #=> String
resp.drift_check_baselines.model_data_quality.statistics.s3_uri #=> String
resp.drift_check_baselines.model_data_quality.constraints.content_type #=> String
resp.drift_check_baselines.model_data_quality.constraints.content_digest #=> String
resp.drift_check_baselines.model_data_quality.constraints.s3_uri #=> String
resp.additional_inference_specifications #=> Array
resp.additional_inference_specifications[0].name #=> String
resp.additional_inference_specifications[0].description #=> String
resp.additional_inference_specifications[0].containers #=> Array
resp.additional_inference_specifications[0].containers[0].container_hostname #=> String
resp.additional_inference_specifications[0].containers[0].image #=> String
resp.additional_inference_specifications[0].containers[0].image_digest #=> String
resp.additional_inference_specifications[0].containers[0].model_data_url #=> String
resp.additional_inference_specifications[0].containers[0].model_data_source.s3_data_source.s3_uri #=> String
resp.additional_inference_specifications[0].containers[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.additional_inference_specifications[0].containers[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.additional_inference_specifications[0].containers[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.additional_inference_specifications[0].containers[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.additional_inference_specifications[0].containers[0].product_id #=> String
resp.additional_inference_specifications[0].containers[0].environment #=> Hash
resp.additional_inference_specifications[0].containers[0].environment["EnvironmentKey"] #=> String
resp.additional_inference_specifications[0].containers[0].model_input.data_input_config #=> String
resp.additional_inference_specifications[0].containers[0].framework #=> String
resp.additional_inference_specifications[0].containers[0].framework_version #=> String
resp.additional_inference_specifications[0].containers[0].nearest_model_name #=> String
resp.additional_inference_specifications[0].containers[0].additional_s3_data_source.s3_data_type #=> String, one of "S3Object", "S3Prefix"
resp.additional_inference_specifications[0].containers[0].additional_s3_data_source.s3_uri #=> String
resp.additional_inference_specifications[0].containers[0].additional_s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.additional_inference_specifications[0].supported_transform_instance_types #=> Array
resp.additional_inference_specifications[0].supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.additional_inference_specifications[0].supported_realtime_inference_instance_types #=> Array
resp.additional_inference_specifications[0].supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.additional_inference_specifications[0].supported_content_types #=> Array
resp.additional_inference_specifications[0].supported_content_types[0] #=> String
resp.additional_inference_specifications[0].supported_response_mime_types #=> Array
resp.additional_inference_specifications[0].supported_response_mime_types[0] #=> String
resp.skip_model_validation #=> String, one of "All", "None"
resp.source_uri #=> String
resp.security_config.kms_key_id #=> String
resp.model_card.model_card_content #=> String
resp.model_card.model_card_status #=> String, one of "Draft", "PendingReview", "Approved", "Archived"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_name (required, String) —

    The name or Amazon Resource Name (ARN) of the model package to describe.

    When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15209

def describe_model_package(params = {}, options = {})
  req = build_request(:describe_model_package, params)
  req.send_request(options)
end

#describe_model_package_group(params = {}) ⇒ Types::DescribeModelPackageGroupOutput

Gets a description for the specified model group.

Examples:

Request syntax with placeholder values


resp = client.describe_model_package_group({
  model_package_group_name: "ArnOrName", # required
})

Response structure


resp.model_package_group_name #=> String
resp.model_package_group_arn #=> String
resp.model_package_group_description #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.model_package_group_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting", "DeleteFailed"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_group_name (required, String) —

    The name of the model group to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15252

def describe_model_package_group(params = {}, options = {})
  req = build_request(:describe_model_package_group, params)
  req.send_request(options)
end

#describe_model_quality_job_definition(params = {}) ⇒ Types::DescribeModelQualityJobDefinitionResponse

Returns a description of a model quality job definition.

Examples:

Request syntax with placeholder values


resp = client.describe_model_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
})

Response structure


resp.job_definition_arn #=> String
resp.job_definition_name #=> String
resp.creation_time #=> Time
resp.model_quality_baseline_config.baselining_job_name #=> String
resp.model_quality_baseline_config.constraints_resource.s3_uri #=> String
resp.model_quality_app_specification.image_uri #=> String
resp.model_quality_app_specification.container_entrypoint #=> Array
resp.model_quality_app_specification.container_entrypoint[0] #=> String
resp.model_quality_app_specification.container_arguments #=> Array
resp.model_quality_app_specification.container_arguments[0] #=> String
resp.model_quality_app_specification.record_preprocessor_source_uri #=> String
resp.model_quality_app_specification.post_analytics_processor_source_uri #=> String
resp.model_quality_app_specification.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.model_quality_app_specification.environment #=> Hash
resp.model_quality_app_specification.environment["ProcessingEnvironmentKey"] #=> String
resp.model_quality_job_input.endpoint_input.endpoint_name #=> String
resp.model_quality_job_input.endpoint_input.local_path #=> String
resp.model_quality_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.model_quality_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.model_quality_job_input.endpoint_input.features_attribute #=> String
resp.model_quality_job_input.endpoint_input.inference_attribute #=> String
resp.model_quality_job_input.endpoint_input.probability_attribute #=> String
resp.model_quality_job_input.endpoint_input.probability_threshold_attribute #=> Float
resp.model_quality_job_input.endpoint_input.start_time_offset #=> String
resp.model_quality_job_input.endpoint_input.end_time_offset #=> String
resp.model_quality_job_input.endpoint_input.exclude_features_attribute #=> String
resp.model_quality_job_input.batch_transform_input.data_captured_destination_s3_uri #=> String
resp.model_quality_job_input.batch_transform_input.dataset_format.csv.header #=> Boolean
resp.model_quality_job_input.batch_transform_input.dataset_format.json.line #=> Boolean
resp.model_quality_job_input.batch_transform_input.local_path #=> String
resp.model_quality_job_input.batch_transform_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.model_quality_job_input.batch_transform_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.model_quality_job_input.batch_transform_input.features_attribute #=> String
resp.model_quality_job_input.batch_transform_input.inference_attribute #=> String
resp.model_quality_job_input.batch_transform_input.probability_attribute #=> String
resp.model_quality_job_input.batch_transform_input.probability_threshold_attribute #=> Float
resp.model_quality_job_input.batch_transform_input.start_time_offset #=> String
resp.model_quality_job_input.batch_transform_input.end_time_offset #=> String
resp.model_quality_job_input.batch_transform_input.exclude_features_attribute #=> String
resp.model_quality_job_input.ground_truth_s3_input.s3_uri #=> String
resp.model_quality_job_output_config.monitoring_outputs #=> Array
resp.model_quality_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String
resp.model_quality_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String
resp.model_quality_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob"
resp.model_quality_job_output_config.kms_key_id #=> String
resp.job_resources.cluster_config.instance_count #=> Integer
resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.job_resources.cluster_config.volume_size_in_gb #=> Integer
resp.job_resources.cluster_config.volume_kms_key_id #=> String
resp.network_config.enable_inter_container_traffic_encryption #=> Boolean
resp.network_config.enable_network_isolation #=> Boolean
resp.network_config.vpc_config.security_group_ids #=> Array
resp.network_config.vpc_config.security_group_ids[0] #=> String
resp.network_config.vpc_config.subnets #=> Array
resp.network_config.vpc_config.subnets[0] #=> String
resp.role_arn #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15347

def describe_model_quality_job_definition(params = {}, options = {})
  req = build_request(:describe_model_quality_job_definition, params)
  req.send_request(options)
end

#describe_monitoring_schedule(params = {}) ⇒ Types::DescribeMonitoringScheduleResponse

Describes the schedule for a monitoring job.

Examples:

Request syntax with placeholder values


resp = client.describe_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
})

Response structure


resp.monitoring_schedule_arn #=> String
resp.monitoring_schedule_name #=> String
resp.monitoring_schedule_status #=> String, one of "Pending", "Failed", "Scheduled", "Stopped"
resp.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability"
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.monitoring_schedule_config.schedule_config.schedule_expression #=> String
resp.monitoring_schedule_config.schedule_config.data_analysis_start_time #=> String
resp.monitoring_schedule_config.schedule_config.data_analysis_end_time #=> String
resp.monitoring_schedule_config.monitoring_job_definition.baseline_config.baselining_job_name #=> String
resp.monitoring_schedule_config.monitoring_job_definition.baseline_config.constraints_resource.s3_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.baseline_config.statistics_resource.s3_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs #=> Array
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.endpoint_name #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.local_path #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.features_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.inference_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.probability_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.probability_threshold_attribute #=> Float
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.start_time_offset #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.end_time_offset #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.exclude_features_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.data_captured_destination_s3_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.dataset_format.csv.header #=> Boolean
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.dataset_format.json.line #=> Boolean
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.local_path #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.features_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.inference_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.probability_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.probability_threshold_attribute #=> Float
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.start_time_offset #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.end_time_offset #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].batch_transform_input.exclude_features_attribute #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs #=> Array
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.local_path #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob"
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.kms_key_id #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_count #=> Integer
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_size_in_gb #=> Integer
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_kms_key_id #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.image_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint #=> Array
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint[0] #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments #=> Array
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments[0] #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.record_preprocessor_source_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.post_analytics_processor_source_uri #=> String
resp.monitoring_schedule_config.monitoring_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer
resp.monitoring_schedule_config.monitoring_job_definition.environment #=> Hash
resp.monitoring_schedule_config.monitoring_job_definition.environment["ProcessingEnvironmentKey"] #=> String
resp.monitoring_schedule_config.monitoring_job_definition.network_config.enable_inter_container_traffic_encryption #=> Boolean
resp.monitoring_schedule_config.monitoring_job_definition.network_config.enable_network_isolation #=> Boolean
resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids #=> Array
resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids[0] #=> String
resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets #=> Array
resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets[0] #=> String
resp.monitoring_schedule_config.monitoring_job_definition.role_arn #=> String
resp.monitoring_schedule_config.monitoring_job_definition_name #=> String
resp.monitoring_schedule_config.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability"
resp.endpoint_name #=> String
resp.last_monitoring_execution_summary.monitoring_schedule_name #=> String
resp.last_monitoring_execution_summary.scheduled_time #=> Time
resp.last_monitoring_execution_summary.creation_time #=> Time
resp.last_monitoring_execution_summary.last_modified_time #=> Time
resp.last_monitoring_execution_summary.monitoring_execution_status #=> String, one of "Pending", "Completed", "CompletedWithViolations", "InProgress", "Failed", "Stopping", "Stopped"
resp.last_monitoring_execution_summary.processing_job_arn #=> String
resp.last_monitoring_execution_summary.endpoint_name #=> String
resp.last_monitoring_execution_summary.failure_reason #=> String
resp.last_monitoring_execution_summary.monitoring_job_definition_name #=> String
resp.last_monitoring_execution_summary.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    Name of a previously created monitoring schedule.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15460

def describe_monitoring_schedule(params = {}, options = {})
  req = build_request(:describe_monitoring_schedule, params)
  req.send_request(options)
end

#describe_notebook_instance(params = {}) ⇒ Types::DescribeNotebookInstanceOutput

Returns information about a notebook instance.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* notebook_instance_deleted
* notebook_instance_in_service
* notebook_instance_stopped

Examples:

Request syntax with placeholder values


resp = client.describe_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Response structure


resp.notebook_instance_arn #=> String
resp.notebook_instance_name #=> String
resp.notebook_instance_status #=> String, one of "Pending", "InService", "Stopping", "Stopped", "Failed", "Deleting", "Updating"
resp.failure_reason #=> String
resp.url #=> String
resp.instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge"
resp.subnet_id #=> String
resp.security_groups #=> Array
resp.security_groups[0] #=> String
resp.role_arn #=> String
resp.kms_key_id #=> String
resp.network_interface_id #=> String
resp.last_modified_time #=> Time
resp.creation_time #=> Time
resp.notebook_instance_lifecycle_config_name #=> String
resp.direct_internet_access #=> String, one of "Enabled", "Disabled"
resp.volume_size_in_gb #=> Integer
resp.accelerator_types #=> Array
resp.accelerator_types[0] #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.default_code_repository #=> String
resp.additional_code_repositories #=> Array
resp.additional_code_repositories[0] #=> String
resp.root_access #=> String, one of "Enabled", "Disabled"
resp.platform_identifier #=> String
resp.instance_metadata_service_configuration.minimum_instance_metadata_service_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the notebook instance that you want information about.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15540

def describe_notebook_instance(params = {}, options = {})
  req = build_request(:describe_notebook_instance, params)
  req.send_request(options)
end

#describe_notebook_instance_lifecycle_config(params = {}) ⇒ Types::DescribeNotebookInstanceLifecycleConfigOutput

Returns a description of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html

Examples:

Request syntax with placeholder values


resp = client.describe_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
})

Response structure


resp.notebook_instance_lifecycle_config_arn #=> String
resp.notebook_instance_lifecycle_config_name #=> String
resp.on_create #=> Array
resp.on_create[0].content #=> String
resp.on_start #=> Array
resp.on_start[0].content #=> String
resp.last_modified_time #=> Time
resp.creation_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_lifecycle_config_name (required, String) —

    The name of the lifecycle configuration to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15587

def describe_notebook_instance_lifecycle_config(params = {}, options = {})
  req = build_request(:describe_notebook_instance_lifecycle_config, params)
  req.send_request(options)
end

#describe_optimization_job(params = {}) ⇒ Types::DescribeOptimizationJobResponse

Provides the properties of the specified optimization job.

Examples:

Request syntax with placeholder values


resp = client.describe_optimization_job({
  optimization_job_name: "EntityName", # required
})

Response structure


resp.optimization_job_arn #=> String
resp.optimization_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED"
resp.optimization_start_time #=> Time
resp.optimization_end_time #=> Time
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.optimization_job_name #=> String
resp.model_source.s3.s3_uri #=> String
resp.model_source.s3.model_access_config.accept_eula #=> Boolean
resp.optimization_environment #=> Hash
resp.optimization_environment["NonEmptyString256"] #=> String
resp.deployment_instance_type #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge"
resp.optimization_configs #=> Array
resp.optimization_configs[0].model_quantization_config.image #=> String
resp.optimization_configs[0].model_quantization_config.override_environment #=> Hash
resp.optimization_configs[0].model_quantization_config.override_environment["NonEmptyString256"] #=> String
resp.optimization_configs[0].model_compilation_config.image #=> String
resp.optimization_configs[0].model_compilation_config.override_environment #=> Hash
resp.optimization_configs[0].model_compilation_config.override_environment["NonEmptyString256"] #=> String
resp.output_config.kms_key_id #=> String
resp.output_config.s3_output_location #=> String
resp.optimization_output.recommended_inference_image #=> String
resp.role_arn #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer
resp.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.stopping_condition.max_pending_time_in_seconds #=> Integer
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :optimization_job_name (required, String) —

    The name that you assigned to the optimization job.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15661

def describe_optimization_job(params = {}, options = {})
  req = build_request(:describe_optimization_job, params)
  req.send_request(options)
end

#describe_pipeline(params = {}) ⇒ Types::DescribePipelineResponse

Describes the details of a pipeline.

Examples:

Request syntax with placeholder values


resp = client.describe_pipeline({
  pipeline_name: "PipelineNameOrArn", # required
})

Response structure


resp.pipeline_arn #=> String
resp.pipeline_name #=> String
resp.pipeline_display_name #=> String
resp.pipeline_definition #=> String
resp.pipeline_description #=> String
resp.role_arn #=> String
resp.pipeline_status #=> String, one of "Active", "Deleting"
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.last_run_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.parallelism_configuration.max_parallel_execution_steps #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name (required, String) —

    The name or Amazon Resource Name (ARN) of the pipeline to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15723

def describe_pipeline(params = {}, options = {})
  req = build_request(:describe_pipeline, params)
  req.send_request(options)
end

#describe_pipeline_definition_for_execution(params = {}) ⇒ Types::DescribePipelineDefinitionForExecutionResponse

Describes the details of an execution’s pipeline definition.

Examples:

Request syntax with placeholder values


resp = client.describe_pipeline_definition_for_execution({
  pipeline_execution_arn: "PipelineExecutionArn", # required
})

Response structure


resp.pipeline_definition #=> String
resp.creation_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (required, String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15753

def describe_pipeline_definition_for_execution(params = {}, options = {})
  req = build_request(:describe_pipeline_definition_for_execution, params)
  req.send_request(options)
end

#describe_pipeline_execution(params = {}) ⇒ Types::DescribePipelineExecutionResponse

Describes the details of a pipeline execution.

Examples:

Request syntax with placeholder values


resp = client.describe_pipeline_execution({
  pipeline_execution_arn: "PipelineExecutionArn", # required
})

Response structure


resp.pipeline_arn #=> String
resp.pipeline_execution_arn #=> String
resp.pipeline_execution_display_name #=> String
resp.pipeline_execution_status #=> String, one of "Executing", "Stopping", "Stopped", "Failed", "Succeeded"
resp.pipeline_execution_description #=> String
resp.pipeline_experiment_config.experiment_name #=> String
resp.pipeline_experiment_config.trial_name #=> String
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.parallelism_configuration.max_parallel_execution_steps #=> Integer
resp.selective_execution_config.source_pipeline_execution_arn #=> String
resp.selective_execution_config.selected_steps #=> Array
resp.selective_execution_config.selected_steps[0].step_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (required, String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15818

def describe_pipeline_execution(params = {}, options = {})
  req = build_request(:describe_pipeline_execution, params)
  req.send_request(options)
end

#describe_processing_job(params = {}) ⇒ Types::DescribeProcessingJobResponse

Returns a description of a processing job.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* processing_job_completed_or_stopped

Examples:

Request syntax with placeholder values


resp = client.describe_processing_job({
  processing_job_name: "ProcessingJobName", # required
})

Response structure


resp.processing_inputs #=> Array
resp.processing_inputs[0].input_name #=> String
resp.processing_inputs[0].app_managed #=> Boolean
resp.processing_inputs[0].s3_input.s3_uri #=> String
resp.processing_inputs[0].s3_input.local_path #=> String
resp.processing_inputs[0].s3_input.s3_data_type #=> String, one of "ManifestFile", "S3Prefix"
resp.processing_inputs[0].s3_input.s3_input_mode #=> String, one of "Pipe", "File"
resp.processing_inputs[0].s3_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.processing_inputs[0].s3_input.s3_compression_type #=> String, one of "None", "Gzip"
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.catalog #=> String
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.database #=> String
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.query_string #=> String
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.work_group #=> String
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.output_s3_uri #=> String
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.kms_key_id #=> String
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.output_format #=> String, one of "PARQUET", "ORC", "AVRO", "JSON", "TEXTFILE"
resp.processing_inputs[0].dataset_definition.athena_dataset_definition.output_compression #=> String, one of "GZIP", "SNAPPY", "ZLIB"
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.cluster_id #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.database #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.db_user #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.query_string #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.cluster_role_arn #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_s3_uri #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.kms_key_id #=> String
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_format #=> String, one of "PARQUET", "CSV"
resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_compression #=> String, one of "None", "GZIP", "BZIP2", "ZSTD", "SNAPPY"
resp.processing_inputs[0].dataset_definition.local_path #=> String
resp.processing_inputs[0].dataset_definition.data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.processing_inputs[0].dataset_definition.input_mode #=> String, one of "Pipe", "File"
resp.processing_output_config.outputs #=> Array
resp.processing_output_config.outputs[0].output_name #=> String
resp.processing_output_config.outputs[0].s3_output.s3_uri #=> String
resp.processing_output_config.outputs[0].s3_output.local_path #=> String
resp.processing_output_config.outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob"
resp.processing_output_config.outputs[0].feature_store_output.feature_group_name #=> String
resp.processing_output_config.outputs[0].app_managed #=> Boolean
resp.processing_output_config.kms_key_id #=> String
resp.processing_job_name #=> String
resp.processing_resources.cluster_config.instance_count #=> Integer
resp.processing_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.processing_resources.cluster_config.volume_size_in_gb #=> Integer
resp.processing_resources.cluster_config.volume_kms_key_id #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer
resp.app_specification.image_uri #=> String
resp.app_specification.container_entrypoint #=> Array
resp.app_specification.container_entrypoint[0] #=> String
resp.app_specification.container_arguments #=> Array
resp.app_specification.container_arguments[0] #=> String
resp.environment #=> Hash
resp.environment["ProcessingEnvironmentKey"] #=> String
resp.network_config.enable_inter_container_traffic_encryption #=> Boolean
resp.network_config.enable_network_isolation #=> Boolean
resp.network_config.vpc_config.security_group_ids #=> Array
resp.network_config.vpc_config.security_group_ids[0] #=> String
resp.network_config.vpc_config.subnets #=> Array
resp.network_config.vpc_config.subnets[0] #=> String
resp.role_arn #=> String
resp.experiment_config.experiment_name #=> String
resp.experiment_config.trial_name #=> String
resp.experiment_config.trial_component_display_name #=> String
resp.experiment_config.run_name #=> String
resp.processing_job_arn #=> String
resp.processing_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.exit_message #=> String
resp.failure_reason #=> String
resp.processing_end_time #=> Time
resp.processing_start_time #=> Time
resp.last_modified_time #=> Time
resp.creation_time #=> Time
resp.monitoring_schedule_arn #=> String
resp.auto_ml_job_arn #=> String
resp.training_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :processing_job_name (required, String) —

    The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 15943

def describe_processing_job(params = {}, options = {})
  req = build_request(:describe_processing_job, params)
  req.send_request(options)
end

#describe_project(params = {}) ⇒ Types::DescribeProjectOutput

Describes the details of a project.

Examples:

Request syntax with placeholder values


resp = client.describe_project({
  project_name: "ProjectEntityName", # required
})

Response structure


resp.project_arn #=> String
resp.project_name #=> String
resp.project_id #=> String
resp.project_description #=> String
resp.service_catalog_provisioning_details.product_id #=> String
resp.service_catalog_provisioning_details.provisioning_artifact_id #=> String
resp.service_catalog_provisioning_details.path_id #=> String
resp.service_catalog_provisioning_details.provisioning_parameters #=> Array
resp.service_catalog_provisioning_details.provisioning_parameters[0].key #=> String
resp.service_catalog_provisioning_details.provisioning_parameters[0].value #=> String
resp.service_catalog_provisioned_product_details.provisioned_product_id #=> String
resp.service_catalog_provisioned_product_details.provisioned_product_status_message #=> String
resp.project_status #=> String, one of "Pending", "CreateInProgress", "CreateCompleted", "CreateFailed", "DeleteInProgress", "DeleteFailed", "DeleteCompleted", "UpdateInProgress", "UpdateCompleted", "UpdateFailed"
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :project_name (required, String) —

    The name of the project to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16007

def describe_project(params = {}, options = {})
  req = build_request(:describe_project, params)
  req.send_request(options)
end

#describe_space(params = {}) ⇒ Types::DescribeSpaceResponse

Describes the space.

Examples:

Request syntax with placeholder values


resp = client.describe_space({
  domain_id: "DomainId", # required
  space_name: "SpaceName", # required
})

Response structure


resp.domain_id #=> String
resp.space_arn #=> String
resp.space_name #=> String
resp.home_efs_file_system_uid #=> String
resp.status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed"
resp.last_modified_time #=> Time
resp.creation_time #=> Time
resp.failure_reason #=> String
resp.space_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.space_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.space_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.space_settings.jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.space_settings.jupyter_server_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.space_settings.jupyter_server_app_settings.lifecycle_config_arns #=> Array
resp.space_settings.jupyter_server_app_settings.lifecycle_config_arns[0] #=> String
resp.space_settings.jupyter_server_app_settings.code_repositories #=> Array
resp.space_settings.jupyter_server_app_settings.code_repositories[0].repository_url #=> String
resp.space_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.space_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.space_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.space_settings.kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.space_settings.kernel_gateway_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.space_settings.kernel_gateway_app_settings.custom_images #=> Array
resp.space_settings.kernel_gateway_app_settings.custom_images[0].image_name #=> String
resp.space_settings.kernel_gateway_app_settings.custom_images[0].image_version_number #=> Integer
resp.space_settings.kernel_gateway_app_settings.custom_images[0].app_image_config_name #=> String
resp.space_settings.kernel_gateway_app_settings.lifecycle_config_arns #=> Array
resp.space_settings.kernel_gateway_app_settings.lifecycle_config_arns[0] #=> String
resp.space_settings.code_editor_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.space_settings.code_editor_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.space_settings.code_editor_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.space_settings.code_editor_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.space_settings.code_editor_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.space_settings.code_editor_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp.space_settings.jupyter_lab_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp.space_settings.jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp.space_settings.jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp.space_settings.jupyter_lab_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.space_settings.jupyter_lab_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp.space_settings.jupyter_lab_app_settings.code_repositories #=> Array
resp.space_settings.jupyter_lab_app_settings.code_repositories[0].repository_url #=> String
resp.space_settings.jupyter_lab_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp.space_settings.app_type #=> String, one of "JupyterServer", "KernelGateway", "DetailedProfiler", "TensorBoard", "CodeEditor", "JupyterLab", "RStudioServerPro", "RSessionGateway", "Canvas"
resp.space_settings.space_storage_settings.ebs_storage_settings.ebs_volume_size_in_gb #=> Integer
resp.space_settings.custom_file_systems #=> Array
resp.space_settings.custom_file_systems[0].efs_file_system.file_system_id #=> String
resp.ownership_settings. #=> String
resp.space_sharing_settings.sharing_type #=> String, one of "Private", "Shared"
resp.space_display_name #=> String
resp.url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the associated domain.

  • :space_name (required, String) —

    The name of the space.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16100

def describe_space(params = {}, options = {})
  req = build_request(:describe_space, params)
  req.send_request(options)
end

#describe_studio_lifecycle_config(params = {}) ⇒ Types::DescribeStudioLifecycleConfigResponse

Describes the Amazon SageMaker Studio Lifecycle Configuration.

Examples:

Request syntax with placeholder values


resp = client.describe_studio_lifecycle_config({
  studio_lifecycle_config_name: "StudioLifecycleConfigName", # required
})

Response structure


resp.studio_lifecycle_config_arn #=> String
resp.studio_lifecycle_config_name #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.studio_lifecycle_config_content #=> String
resp.studio_lifecycle_config_app_type #=> String, one of "JupyterServer", "KernelGateway", "CodeEditor", "JupyterLab"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :studio_lifecycle_config_name (required, String) —

    The name of the Amazon SageMaker Studio Lifecycle Configuration to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16139

def describe_studio_lifecycle_config(params = {}, options = {})
  req = build_request(:describe_studio_lifecycle_config, params)
  req.send_request(options)
end

#describe_subscribed_workteam(params = {}) ⇒ Types::DescribeSubscribedWorkteamResponse

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

Examples:

Request syntax with placeholder values


resp = client.describe_subscribed_workteam({
  workteam_arn: "WorkteamArn", # required
})

Response structure


resp.subscribed_workteam.workteam_arn #=> String
resp.subscribed_workteam.marketplace_title #=> String
resp.subscribed_workteam.seller_name #=> String
resp.subscribed_workteam.marketplace_description #=> String
resp.subscribed_workteam.listing_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_arn (required, String) —

    The Amazon Resource Name (ARN) of the subscribed work team to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16174

def describe_subscribed_workteam(params = {}, options = {})
  req = build_request(:describe_subscribed_workteam, params)
  req.send_request(options)
end

#describe_training_job(params = {}) ⇒ Types::DescribeTrainingJobResponse

Returns information about a training job.

Some of the attributes below only appear if the training job successfully starts. If the training job fails, ‘TrainingJobStatus` is `Failed` and, depending on the `FailureReason`, attributes like `TrainingStartTime`, `TrainingTimeInSeconds`, `TrainingEndTime`, and `BillableTimeInSeconds` may not be present in the response.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* training_job_completed_or_stopped

Examples:

Request syntax with placeholder values


resp = client.describe_training_job({
  training_job_name: "TrainingJobName", # required
})

Response structure


resp.training_job_name #=> String
resp.training_job_arn #=> String
resp.tuning_job_arn #=> String
resp.labeling_job_arn #=> String
resp.auto_ml_job_arn #=> String
resp.model_artifacts.s3_model_artifacts #=> String
resp.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.secondary_status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting", "Pending"
resp.failure_reason #=> String
resp.hyper_parameters #=> Hash
resp.hyper_parameters["HyperParameterKey"] #=> String
resp.algorithm_specification.training_image #=> String
resp.algorithm_specification.algorithm_name #=> String
resp.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.algorithm_specification.metric_definitions #=> Array
resp.algorithm_specification.metric_definitions[0].name #=> String
resp.algorithm_specification.metric_definitions[0].regex #=> String
resp.algorithm_specification.enable_sage_maker_metrics_time_series #=> Boolean
resp.algorithm_specification.container_entrypoint #=> Array
resp.algorithm_specification.container_entrypoint[0] #=> String
resp.algorithm_specification.container_arguments #=> Array
resp.algorithm_specification.container_arguments[0] #=> String
resp.algorithm_specification.training_image_config.training_repository_access_mode #=> String, one of "Platform", "Vpc"
resp.algorithm_specification.training_image_config.training_repository_auth_config.training_repository_credentials_provider_arn #=> String
resp.role_arn #=> String
resp.input_data_config #=> Array
resp.input_data_config[0].channel_name #=> String
resp.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.input_data_config[0].data_source.s3_data_source.instance_group_names #=> Array
resp.input_data_config[0].data_source.s3_data_source.instance_group_names[0] #=> String
resp.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.input_data_config[0].content_type #=> String
resp.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.input_data_config[0].input_mode #=> String, one of "Pipe", "File", "FastFile"
resp.input_data_config[0].shuffle_config.seed #=> Integer
resp.output_data_config.kms_key_id #=> String
resp.output_data_config.s3_output_path #=> String
resp.output_data_config.compression_type #=> String, one of "GZIP", "NONE"
resp.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.resource_config.instance_count #=> Integer
resp.resource_config.volume_size_in_gb #=> Integer
resp.resource_config.volume_kms_key_id #=> String
resp.resource_config.keep_alive_period_in_seconds #=> Integer
resp.resource_config.instance_groups #=> Array
resp.resource_config.instance_groups[0].instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.8xlarge", "ml.c6i.4xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge"
resp.resource_config.instance_groups[0].instance_count #=> Integer
resp.resource_config.instance_groups[0].instance_group_name #=> String
resp.warm_pool_status.status #=> String, one of "Available", "Terminated", "Reused", "InUse"
resp.warm_pool_status.resource_retained_billable_time_in_seconds #=> Integer
resp.warm_pool_status.reused_by_job #=> String
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer
resp.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.stopping_condition.max_pending_time_in_seconds #=> Integer
resp.creation_time #=> Time
resp.training_start_time #=> Time
resp.training_end_time #=> Time
resp.last_modified_time #=> Time
resp.secondary_status_transitions #=> Array
resp.secondary_status_transitions[0].status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting", "Pending"
resp.secondary_status_transitions[0].start_time #=> Time
resp.secondary_status_transitions[0].end_time #=> Time
resp.secondary_status_transitions[0].status_message #=> String
resp.final_metric_data_list #=> Array
resp.final_metric_data_list[0].metric_name #=> String
resp.final_metric_data_list[0].value #=> Float
resp.final_metric_data_list[0].timestamp #=> Time
resp.enable_network_isolation #=> Boolean
resp.enable_inter_container_traffic_encryption #=> Boolean
resp.enable_managed_spot_training #=> Boolean
resp.checkpoint_config.s3_uri #=> String
resp.checkpoint_config.local_path #=> String
resp.training_time_in_seconds #=> Integer
resp.billable_time_in_seconds #=> Integer
resp.debug_hook_config.local_path #=> String
resp.debug_hook_config.s3_output_path #=> String
resp.debug_hook_config.hook_parameters #=> Hash
resp.debug_hook_config.hook_parameters["ConfigKey"] #=> String
resp.debug_hook_config.collection_configurations #=> Array
resp.debug_hook_config.collection_configurations[0].collection_name #=> String
resp.debug_hook_config.collection_configurations[0].collection_parameters #=> Hash
resp.debug_hook_config.collection_configurations[0].collection_parameters["ConfigKey"] #=> String
resp.experiment_config.experiment_name #=> String
resp.experiment_config.trial_name #=> String
resp.experiment_config.trial_component_display_name #=> String
resp.experiment_config.run_name #=> String
resp.debug_rule_configurations #=> Array
resp.debug_rule_configurations[0].rule_configuration_name #=> String
resp.debug_rule_configurations[0].local_path #=> String
resp.debug_rule_configurations[0].s3_output_path #=> String
resp.debug_rule_configurations[0].rule_evaluator_image #=> String
resp.debug_rule_configurations[0].instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.debug_rule_configurations[0].volume_size_in_gb #=> Integer
resp.debug_rule_configurations[0].rule_parameters #=> Hash
resp.debug_rule_configurations[0].rule_parameters["ConfigKey"] #=> String
resp.tensor_board_output_config.local_path #=> String
resp.tensor_board_output_config.s3_output_path #=> String
resp.debug_rule_evaluation_statuses #=> Array
resp.debug_rule_evaluation_statuses[0].rule_configuration_name #=> String
resp.debug_rule_evaluation_statuses[0].rule_evaluation_job_arn #=> String
resp.debug_rule_evaluation_statuses[0].rule_evaluation_status #=> String, one of "InProgress", "NoIssuesFound", "IssuesFound", "Error", "Stopping", "Stopped"
resp.debug_rule_evaluation_statuses[0].status_details #=> String
resp.debug_rule_evaluation_statuses[0].last_modified_time #=> Time
resp.profiler_config.s3_output_path #=> String
resp.profiler_config.profiling_interval_in_milliseconds #=> Integer
resp.profiler_config.profiling_parameters #=> Hash
resp.profiler_config.profiling_parameters["ConfigKey"] #=> String
resp.profiler_config.disable_profiler #=> Boolean
resp.profiler_rule_configurations #=> Array
resp.profiler_rule_configurations[0].rule_configuration_name #=> String
resp.profiler_rule_configurations[0].local_path #=> String
resp.profiler_rule_configurations[0].s3_output_path #=> String
resp.profiler_rule_configurations[0].rule_evaluator_image #=> String
resp.profiler_rule_configurations[0].instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.8xlarge", "ml.r5d.12xlarge", "ml.r5d.16xlarge", "ml.r5d.24xlarge"
resp.profiler_rule_configurations[0].volume_size_in_gb #=> Integer
resp.profiler_rule_configurations[0].rule_parameters #=> Hash
resp.profiler_rule_configurations[0].rule_parameters["ConfigKey"] #=> String
resp.profiler_rule_evaluation_statuses #=> Array
resp.profiler_rule_evaluation_statuses[0].rule_configuration_name #=> String
resp.profiler_rule_evaluation_statuses[0].rule_evaluation_job_arn #=> String
resp.profiler_rule_evaluation_statuses[0].rule_evaluation_status #=> String, one of "InProgress", "NoIssuesFound", "IssuesFound", "Error", "Stopping", "Stopped"
resp.profiler_rule_evaluation_statuses[0].status_details #=> String
resp.profiler_rule_evaluation_statuses[0].last_modified_time #=> Time
resp.profiling_status #=> String, one of "Enabled", "Disabled"
resp.environment #=> Hash
resp.environment["TrainingEnvironmentKey"] #=> String
resp.retry_strategy.maximum_retry_attempts #=> Integer
resp.remote_debug_config.enable_remote_debug #=> Boolean
resp.infra_check_config.enable_infra_check #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :training_job_name (required, String) —

    The name of the training job.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16394

def describe_training_job(params = {}, options = {})
  req = build_request(:describe_training_job, params)
  req.send_request(options)
end

#describe_transform_job(params = {}) ⇒ Types::DescribeTransformJobResponse

Returns information about a transform job.

The following waiters are defined for this operation (see #wait_until for detailed usage):

* transform_job_completed_or_stopped

Examples:

Request syntax with placeholder values


resp = client.describe_transform_job({
  transform_job_name: "TransformJobName", # required
})

Response structure


resp.transform_job_name #=> String
resp.transform_job_arn #=> String
resp.transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.failure_reason #=> String
resp.model_name #=> String
resp.max_concurrent_transforms #=> Integer
resp.model_client_config.invocations_timeout_in_seconds #=> Integer
resp.model_client_config.invocations_max_retries #=> Integer
resp.max_payload_in_mb #=> Integer
resp.batch_strategy #=> String, one of "MultiRecord", "SingleRecord"
resp.environment #=> Hash
resp.environment["TransformEnvironmentKey"] #=> String
resp.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.transform_input.data_source.s3_data_source.s3_uri #=> String
resp.transform_input.content_type #=> String
resp.transform_input.compression_type #=> String, one of "None", "Gzip"
resp.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord"
resp.transform_output.s3_output_path #=> String
resp.transform_output.accept #=> String
resp.transform_output.assemble_with #=> String, one of "None", "Line"
resp.transform_output.kms_key_id #=> String
resp.data_capture_config.destination_s3_uri #=> String
resp.data_capture_config.kms_key_id #=> String
resp.data_capture_config.generate_inference_id #=> Boolean
resp.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge"
resp.transform_resources.instance_count #=> Integer
resp.transform_resources.volume_kms_key_id #=> String
resp.creation_time #=> Time
resp.transform_start_time #=> Time
resp.transform_end_time #=> Time
resp.labeling_job_arn #=> String
resp.auto_ml_job_arn #=> String
resp.data_processing.input_filter #=> String
resp.data_processing.output_filter #=> String
resp.data_processing.join_source #=> String, one of "Input", "None"
resp.experiment_config.experiment_name #=> String
resp.experiment_config.trial_name #=> String
resp.experiment_config.trial_component_display_name #=> String
resp.experiment_config.run_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :transform_job_name (required, String) —

    The name of the transform job that you want to view details of.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16485

def describe_transform_job(params = {}, options = {})
  req = build_request(:describe_transform_job, params)
  req.send_request(options)
end

#describe_trial(params = {}) ⇒ Types::DescribeTrialResponse

Provides a list of a trial’s properties.

Examples:

Request syntax with placeholder values


resp = client.describe_trial({
  trial_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_name #=> String
resp.trial_arn #=> String
resp.display_name #=> String
resp.experiment_name #=> String
resp.source.source_arn #=> String
resp.source.source_type #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.metadata_properties.commit_id #=> String
resp.metadata_properties.repository #=> String
resp.metadata_properties.generated_by #=> String
resp.metadata_properties.project_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_name (required, String) —

    The name of the trial to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16545

def describe_trial(params = {}, options = {})
  req = build_request(:describe_trial, params)
  req.send_request(options)
end

#describe_trial_component(params = {}) ⇒ Types::DescribeTrialComponentResponse

Provides a list of a trials component’s properties.

Examples:

Request syntax with placeholder values


resp = client.describe_trial_component({
  trial_component_name: "ExperimentEntityNameOrArn", # required
})

Response structure


resp.trial_component_name #=> String
resp.trial_component_arn #=> String
resp.display_name #=> String
resp.source.source_arn #=> String
resp.source.source_type #=> String
resp.status.primary_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.status.message #=> String
resp.start_time #=> Time
resp.end_time #=> Time
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.created_by.iam_identity.arn #=> String
resp.created_by.iam_identity.principal_id #=> String
resp.created_by.iam_identity.source_identity #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String
resp.last_modified_by.iam_identity.arn #=> String
resp.last_modified_by.iam_identity.principal_id #=> String
resp.last_modified_by.iam_identity.source_identity #=> String
resp.parameters #=> Hash
resp.parameters["TrialComponentKey320"].string_value #=> String
resp.parameters["TrialComponentKey320"].number_value #=> Float
resp.input_artifacts #=> Hash
resp.input_artifacts["TrialComponentKey128"].media_type #=> String
resp.input_artifacts["TrialComponentKey128"].value #=> String
resp.output_artifacts #=> Hash
resp.output_artifacts["TrialComponentKey128"].media_type #=> String
resp.output_artifacts["TrialComponentKey128"].value #=> String
resp.metadata_properties.commit_id #=> String
resp.metadata_properties.repository #=> String
resp.metadata_properties.generated_by #=> String
resp.metadata_properties.project_id #=> String
resp.metrics #=> Array
resp.metrics[0].metric_name #=> String
resp.metrics[0].source_arn #=> String
resp.metrics[0].time_stamp #=> Time
resp.metrics[0].max #=> Float
resp.metrics[0].min #=> Float
resp.metrics[0].last #=> Float
resp.metrics[0].count #=> Integer
resp.metrics[0].avg #=> Float
resp.metrics[0].std_dev #=> Float
resp.lineage_group_arn #=> String
resp.sources #=> Array
resp.sources[0].source_arn #=> String
resp.sources[0].source_type #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the trial component to describe.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16639

def describe_trial_component(params = {}, options = {})
  req = build_request(:describe_trial_component, params)
  req.send_request(options)
end

#describe_user_profile(params = {}) ⇒ Types::DescribeUserProfileResponse

Describes a user profile. For more information, see ‘CreateUserProfile`.

Examples:

Request syntax with placeholder values


resp = client.({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
})

Response structure


resp.domain_id #=> String
resp. #=> String
resp. #=> String
resp.home_efs_file_system_uid #=> String
resp.status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed"
resp.last_modified_time #=> Time
resp.creation_time #=> Time
resp.failure_reason #=> String
resp.single_sign_on_user_identifier #=> String
resp.single_sign_on_user_value #=> String
resp..execution_role #=> String
resp..security_groups #=> Array
resp..security_groups[0] #=> String
resp..sharing_settings.notebook_output_option #=> String, one of "Allowed", "Disabled"
resp..sharing_settings.s3_output_path #=> String
resp..sharing_settings.s3_kms_key_id #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..jupyter_server_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..jupyter_server_app_settings.lifecycle_config_arns #=> Array
resp..jupyter_server_app_settings.lifecycle_config_arns[0] #=> String
resp..jupyter_server_app_settings.code_repositories #=> Array
resp..jupyter_server_app_settings.code_repositories[0].repository_url #=> String
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..kernel_gateway_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..kernel_gateway_app_settings.custom_images #=> Array
resp..kernel_gateway_app_settings.custom_images[0].image_name #=> String
resp..kernel_gateway_app_settings.custom_images[0].image_version_number #=> Integer
resp..kernel_gateway_app_settings.custom_images[0].app_image_config_name #=> String
resp..kernel_gateway_app_settings.lifecycle_config_arns #=> Array
resp..kernel_gateway_app_settings.lifecycle_config_arns[0] #=> String
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..tensor_board_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..tensor_board_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..r_studio_server_pro_app_settings.access_status #=> String, one of "ENABLED", "DISABLED"
resp..r_studio_server_pro_app_settings.user_group #=> String, one of "R_STUDIO_ADMIN", "R_STUDIO_USER"
resp..r_session_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..r_session_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..r_session_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..r_session_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..r_session_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..r_session_app_settings.custom_images #=> Array
resp..r_session_app_settings.custom_images[0].image_name #=> String
resp..r_session_app_settings.custom_images[0].image_version_number #=> Integer
resp..r_session_app_settings.custom_images[0].app_image_config_name #=> String
resp..canvas_app_settings.time_series_forecasting_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.time_series_forecasting_settings.amazon_forecast_role_arn #=> String
resp..canvas_app_settings.model_register_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.model_register_settings. #=> String
resp..canvas_app_settings.workspace_settings.s3_artifact_path #=> String
resp..canvas_app_settings.workspace_settings.s3_kms_key_id #=> String
resp..canvas_app_settings.identity_provider_o_auth_settings #=> Array
resp..canvas_app_settings.identity_provider_o_auth_settings[0].data_source_name #=> String, one of "SalesforceGenie", "Snowflake"
resp..canvas_app_settings.identity_provider_o_auth_settings[0].status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.identity_provider_o_auth_settings[0].secret_arn #=> String
resp..canvas_app_settings.direct_deploy_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.kendra_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..canvas_app_settings.generative_ai_settings.amazon_bedrock_role_arn #=> String
resp..canvas_app_settings.emr_serverless_settings.execution_role_arn #=> String
resp..canvas_app_settings.emr_serverless_settings.status #=> String, one of "ENABLED", "DISABLED"
resp..code_editor_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..code_editor_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..code_editor_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..code_editor_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..code_editor_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..code_editor_app_settings.custom_images #=> Array
resp..code_editor_app_settings.custom_images[0].image_name #=> String
resp..code_editor_app_settings.custom_images[0].image_version_number #=> Integer
resp..code_editor_app_settings.custom_images[0].app_image_config_name #=> String
resp..code_editor_app_settings.lifecycle_config_arns #=> Array
resp..code_editor_app_settings.lifecycle_config_arns[0] #=> String
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.lifecycle_management #=> String, one of "ENABLED", "DISABLED"
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.min_idle_timeout_in_minutes #=> Integer
resp..code_editor_app_settings.app_lifecycle_management.idle_settings.max_idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String
resp..jupyter_lab_app_settings.default_resource_spec.sage_maker_image_version_alias #=> String
resp..jupyter_lab_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp..jupyter_lab_app_settings.default_resource_spec.lifecycle_config_arn #=> String
resp..jupyter_lab_app_settings.custom_images #=> Array
resp..jupyter_lab_app_settings.custom_images[0].image_name #=> String
resp..jupyter_lab_app_settings.custom_images[0].image_version_number #=> Integer
resp..jupyter_lab_app_settings.custom_images[0].app_image_config_name #=> String
resp..jupyter_lab_app_settings.lifecycle_config_arns #=> Array
resp..jupyter_lab_app_settings.lifecycle_config_arns[0] #=> String
resp..jupyter_lab_app_settings.code_repositories #=> Array
resp..jupyter_lab_app_settings.code_repositories[0].repository_url #=> String
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.lifecycle_management #=> String, one of "ENABLED", "DISABLED"
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.min_idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.app_lifecycle_management.idle_settings.max_idle_timeout_in_minutes #=> Integer
resp..jupyter_lab_app_settings.emr_settings.assumable_role_arns #=> Array
resp..jupyter_lab_app_settings.emr_settings.assumable_role_arns[0] #=> String
resp..jupyter_lab_app_settings.emr_settings.execution_role_arns #=> Array
resp..jupyter_lab_app_settings.emr_settings.execution_role_arns[0] #=> String
resp..space_storage_settings.default_ebs_storage_settings.default_ebs_volume_size_in_gb #=> Integer
resp..space_storage_settings.default_ebs_storage_settings.maximum_ebs_volume_size_in_gb #=> Integer
resp..default_landing_uri #=> String
resp..studio_web_portal #=> String, one of "ENABLED", "DISABLED"
resp..custom_posix_user_config.uid #=> Integer
resp..custom_posix_user_config.gid #=> Integer
resp..custom_file_system_configs #=> Array
resp..custom_file_system_configs[0].efs_file_system_config.file_system_id #=> String
resp..custom_file_system_configs[0].efs_file_system_config.file_system_path #=> String
resp..studio_web_portal_settings.hidden_ml_tools #=> Array
resp..studio_web_portal_settings.hidden_ml_tools[0] #=> String, one of "DataWrangler", "FeatureStore", "EmrClusters", "AutoMl", "Experiments", "Training", "ModelEvaluation", "Pipelines", "Models", "JumpStart", "InferenceRecommender", "Endpoints", "Projects", "InferenceOptimization"
resp..studio_web_portal_settings.hidden_app_types #=> Array
resp..studio_web_portal_settings.hidden_app_types[0] #=> String, one of "JupyterServer", "KernelGateway", "DetailedProfiler", "TensorBoard", "CodeEditor", "JupyterLab", "RStudioServerPro", "RSessionGateway", "Canvas"
resp..auto_mount_home_efs #=> String, one of "Enabled", "Disabled", "DefaultAsDomain"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name. This value is not case sensitive.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16798

def (params = {}, options = {})
  req = build_request(:describe_user_profile, params)
  req.send_request(options)
end

#describe_workforce(params = {}) ⇒ Types::DescribeWorkforceResponse

Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges ([CIDRs]). Allowable IP address ranges are the IP addresses that workers can use to access tasks.

This operation applies only to private workforces.

[1]: docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html

Examples:

Request syntax with placeholder values


resp = client.describe_workforce({
  workforce_name: "WorkforceName", # required
})

Response structure


resp.workforce.workforce_name #=> String
resp.workforce.workforce_arn #=> String
resp.workforce.last_updated_date #=> Time
resp.workforce.source_ip_config.cidrs #=> Array
resp.workforce.source_ip_config.cidrs[0] #=> String
resp.workforce.sub_domain #=> String
resp.workforce.cognito_config.user_pool #=> String
resp.workforce.cognito_config.client_id #=> String
resp.workforce.oidc_config.client_id #=> String
resp.workforce.oidc_config.issuer #=> String
resp.workforce.oidc_config.authorization_endpoint #=> String
resp.workforce.oidc_config.token_endpoint #=> String
resp.workforce.oidc_config. #=> String
resp.workforce.oidc_config.logout_endpoint #=> String
resp.workforce.oidc_config.jwks_uri #=> String
resp.workforce.oidc_config.scope #=> String
resp.workforce.oidc_config.authentication_request_extra_params #=> Hash
resp.workforce.oidc_config.authentication_request_extra_params["AuthenticationRequestExtraParamsKey"] #=> String
resp.workforce.create_date #=> Time
resp.workforce.workforce_vpc_config.vpc_id #=> String
resp.workforce.workforce_vpc_config.security_group_ids #=> Array
resp.workforce.workforce_vpc_config.security_group_ids[0] #=> String
resp.workforce.workforce_vpc_config.subnets #=> Array
resp.workforce.workforce_vpc_config.subnets[0] #=> String
resp.workforce.workforce_vpc_config.vpc_endpoint_id #=> String
resp.workforce.status #=> String, one of "Initializing", "Updating", "Deleting", "Failed", "Active"
resp.workforce.failure_reason #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workforce_name (required, String) —

    The name of the private workforce whose access you want to restrict. ‘WorkforceName` is automatically set to `default` when a workforce is created and cannot be modified.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16863

def describe_workforce(params = {}, options = {})
  req = build_request(:describe_workforce, params)
  req.send_request(options)
end

#describe_workteam(params = {}) ⇒ Types::DescribeWorkteamResponse

Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team’s Amazon Resource Name (ARN).

Examples:

Request syntax with placeholder values


resp = client.describe_workteam({
  workteam_name: "WorkteamName", # required
})

Response structure


resp.workteam.workteam_name #=> String
resp.workteam.member_definitions #=> Array
resp.workteam.member_definitions[0].cognito_member_definition.user_pool #=> String
resp.workteam.member_definitions[0].cognito_member_definition.user_group #=> String
resp.workteam.member_definitions[0].cognito_member_definition.client_id #=> String
resp.workteam.member_definitions[0].oidc_member_definition.groups #=> Array
resp.workteam.member_definitions[0].oidc_member_definition.groups[0] #=> String
resp.workteam.workteam_arn #=> String
resp.workteam.workforce_arn #=> String
resp.workteam.product_listing_ids #=> Array
resp.workteam.product_listing_ids[0] #=> String
resp.workteam.description #=> String
resp.workteam.sub_domain #=> String
resp.workteam.create_date #=> Time
resp.workteam.last_updated_date #=> Time
resp.workteam.notification_configuration.notification_topic_arn #=> String
resp.workteam.worker_access_configuration.s3_presign.iam_policy_constraints.source_ip #=> String, one of "Enabled", "Disabled"
resp.workteam.worker_access_configuration.s3_presign.iam_policy_constraints.vpc_source_ip #=> String, one of "Enabled", "Disabled"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_name (required, String) —

    The name of the work team to return a description of.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16910

def describe_workteam(params = {}, options = {})
  req = build_request(:describe_workteam, params)
  req.send_request(options)
end

#disable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct

Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16924

def disable_sagemaker_servicecatalog_portfolio(params = {}, options = {})
  req = build_request(:disable_sagemaker_servicecatalog_portfolio, params)
  req.send_request(options)
end

#disassociate_trial_component(params = {}) ⇒ Types::DisassociateTrialComponentResponse

Disassociates a trial component from a trial. This doesn’t effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the

AssociateTrialComponent][1

API.

To get a list of the trials a component is associated with, use the

Search][2

API. Specify ‘ExperimentTrialComponent` for the `Resource`

parameter. The list appears in the response under ‘Results.TrialComponent.Parents`.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AssociateTrialComponent.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html

Examples:

Request syntax with placeholder values


resp = client.disassociate_trial_component({
  trial_component_name: "ExperimentEntityName", # required
  trial_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_component_arn #=> String
resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component to disassociate from the trial.

  • :trial_name (required, String) —

    The name of the trial to disassociate from.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16972

def disassociate_trial_component(params = {}, options = {})
  req = build_request(:disassociate_trial_component, params)
  req.send_request(options)
end

#enable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct

Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 16986

def enable_sagemaker_servicecatalog_portfolio(params = {}, options = {})
  req = build_request(:enable_sagemaker_servicecatalog_portfolio, params)
  req.send_request(options)
end

#get_device_fleet_report(params = {}) ⇒ Types::GetDeviceFleetReportResponse

Describes a fleet.

Examples:

Request syntax with placeholder values


resp = client.get_device_fleet_report({
  device_fleet_name: "EntityName", # required
})

Response structure


resp.device_fleet_arn #=> String
resp.device_fleet_name #=> String
resp.output_config.s3_output_location #=> String
resp.output_config.kms_key_id #=> String
resp.output_config.preset_deployment_type #=> String, one of "GreengrassV2Component"
resp.output_config.preset_deployment_config #=> String
resp.description #=> String
resp.report_generated #=> Time
resp.device_stats.connected_device_count #=> Integer
resp.device_stats.registered_device_count #=> Integer
resp.agent_versions #=> Array
resp.agent_versions[0].version #=> String
resp.agent_versions[0].agent_count #=> Integer
resp.model_stats #=> Array
resp.model_stats[0].model_name #=> String
resp.model_stats[0].model_version #=> String
resp.model_stats[0].offline_device_count #=> Integer
resp.model_stats[0].connected_device_count #=> Integer
resp.model_stats[0].active_device_count #=> Integer
resp.model_stats[0].sampling_device_count #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17040

def get_device_fleet_report(params = {}, options = {})
  req = build_request(:get_device_fleet_report, params)
  req.send_request(options)
end

#get_lineage_group_policy(params = {}) ⇒ Types::GetLineageGroupPolicyResponse

The resource policy for the lineage group.

Examples:

Request syntax with placeholder values


resp = client.get_lineage_group_policy({
  lineage_group_name: "LineageGroupNameOrArn", # required
})

Response structure


resp.lineage_group_arn #=> String
resp.resource_policy #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :lineage_group_name (required, String) —

    The name or Amazon Resource Name (ARN) of the lineage group.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17070

def get_lineage_group_policy(params = {}, options = {})
  req = build_request(:get_lineage_group_policy, params)
  req.send_request(options)
end

#get_model_package_group_policy(params = {}) ⇒ Types::GetModelPackageGroupPolicyOutput

Gets a resource policy that manages access for a model group. For information about resource policies, see [Identity-based policies and resource-based policies] in the *Amazon Web Services Identity and Access Management User Guide.*.

[1]: docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html

Examples:

Request syntax with placeholder values


resp = client.get_model_package_group_policy({
  model_package_group_name: "EntityName", # required
})

Response structure


resp.resource_policy #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_group_name (required, String) —

    The name of the model group for which to get the resource policy.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17105

def get_model_package_group_policy(params = {}, options = {})
  req = build_request(:get_model_package_group_policy, params)
  req.send_request(options)
end

#get_sagemaker_servicecatalog_portfolio_status(params = {}) ⇒ Types::GetSagemakerServicecatalogPortfolioStatusOutput

Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Examples:

Response structure


resp.status #=> String, one of "Enabled", "Disabled"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17125

def get_sagemaker_servicecatalog_portfolio_status(params = {}, options = {})
  req = build_request(:get_sagemaker_servicecatalog_portfolio_status, params)
  req.send_request(options)
end

#get_scaling_configuration_recommendation(params = {}) ⇒ Types::GetScalingConfigurationRecommendationResponse

Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.

Examples:

Request syntax with placeholder values


resp = client.get_scaling_configuration_recommendation({
  inference_recommendations_job_name: "RecommendationJobName", # required
  recommendation_id: "String",
  endpoint_name: "EndpointName",
  target_cpu_utilization_per_core: 1,
  scaling_policy_objective: {
    min_invocations_per_minute: 1,
    max_invocations_per_minute: 1,
  },
})

Response structure


resp.inference_recommendations_job_name #=> String
resp.recommendation_id #=> String
resp.endpoint_name #=> String
resp.target_cpu_utilization_per_core #=> Integer
resp.scaling_policy_objective.min_invocations_per_minute #=> Integer
resp.scaling_policy_objective.max_invocations_per_minute #=> Integer
resp.metric.invocations_per_instance #=> Integer
resp.metric.model_latency #=> Integer
resp.dynamic_scaling_configuration.min_capacity #=> Integer
resp.dynamic_scaling_configuration.max_capacity #=> Integer
resp.dynamic_scaling_configuration.scale_in_cooldown #=> Integer
resp.dynamic_scaling_configuration.scale_out_cooldown #=> Integer
resp.dynamic_scaling_configuration.scaling_policies #=> Array
resp.dynamic_scaling_configuration.scaling_policies[0].target_tracking.metric_specification.predefined.predefined_metric_type #=> String
resp.dynamic_scaling_configuration.scaling_policies[0].target_tracking.metric_specification.customized.metric_name #=> String
resp.dynamic_scaling_configuration.scaling_policies[0].target_tracking.metric_specification.customized.namespace #=> String
resp.dynamic_scaling_configuration.scaling_policies[0].target_tracking.metric_specification.customized.statistic #=> String, one of "Average", "Minimum", "Maximum", "SampleCount", "Sum"
resp.dynamic_scaling_configuration.scaling_policies[0].target_tracking.target_value #=> Float

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :inference_recommendations_job_name (required, String) —

    The name of a previously completed Inference Recommender job.

  • :recommendation_id (String) —

    The recommendation ID of a previously completed inference recommendation. This ID should come from one of the recommendations returned by the job specified in the ‘InferenceRecommendationsJobName` field.

    Specify either this field or the ‘EndpointName` field.

  • :endpoint_name (String) —

    The name of an endpoint benchmarked during a previously completed inference recommendation job. This name should come from one of the recommendations returned by the job specified in the ‘InferenceRecommendationsJobName` field.

    Specify either this field or the ‘RecommendationId` field.

  • :target_cpu_utilization_per_core (Integer) —

    The percentage of how much utilization you want an instance to use before autoscaling. The default value is 50%.

  • :scaling_policy_objective (Types::ScalingPolicyObjective) —

    An object where you specify the anticipated traffic pattern for an endpoint.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17209

def get_scaling_configuration_recommendation(params = {}, options = {})
  req = build_request(:get_scaling_configuration_recommendation, params)
  req.send_request(options)
end

#get_search_suggestions(params = {}) ⇒ Types::GetSearchSuggestionsResponse

An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in ‘Search` queries. Provides suggestions for `HyperParameters`, `Tags`, and `Metrics`.

Examples:

Request syntax with placeholder values


resp = client.get_search_suggestions({
  resource: "TrainingJob", # required, accepts TrainingJob, Experiment, ExperimentTrial, ExperimentTrialComponent, Endpoint, Model, ModelPackage, ModelPackageGroup, Pipeline, PipelineExecution, FeatureGroup, FeatureMetadata, Image, ImageVersion, Project, HyperParameterTuningJob, ModelCard
  suggestion_query: {
    property_name_query: {
      property_name_hint: "PropertyNameHint", # required
    },
  },
})

Response structure


resp.property_name_suggestions #=> Array
resp.property_name_suggestions[0].property_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :resource (required, String) —

    The name of the SageMaker resource to search for.

  • :suggestion_query (Types::SuggestionQuery) —

    Limits the property names that are included in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17249

def get_search_suggestions(params = {}, options = {})
  req = build_request(:get_search_suggestions, params)
  req.send_request(options)
end

#import_hub_content(params = {}) ⇒ Types::ImportHubContentResponse

Import hub content.

Examples:

Request syntax with placeholder values


resp = client.import_hub_content({
  hub_content_name: "HubContentName", # required
  hub_content_version: "HubContentVersion",
  hub_content_type: "Model", # required, accepts Model, Notebook, ModelReference
  document_schema_version: "DocumentSchemaVersion", # required
  hub_name: "HubNameOrArn", # required
  hub_content_display_name: "HubContentDisplayName",
  hub_content_description: "HubContentDescription",
  hub_content_markdown: "HubContentMarkdown",
  hub_content_document: "HubContentDocument", # required
  hub_content_search_keywords: ["HubSearchKeyword"],
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.hub_arn #=> String
resp.hub_content_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_content_name (required, String) —

    The name of the hub content to import.

  • :hub_content_version (String) —

    The version of the hub content to import.

  • :hub_content_type (required, String) —

    The type of hub content to import.

  • :document_schema_version (required, String) —

    The version of the hub content schema to import.

  • :hub_name (required, String) —

    The name of the hub to import content into.

  • :hub_content_display_name (String) —

    The display name of the hub content to import.

  • :hub_content_description (String) —

    A description of the hub content to import.

  • :hub_content_markdown (String) —

    A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.

  • :hub_content_document (required, String) —

    The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.

  • :hub_content_search_keywords (Array<String>) —

    The searchable keywords of the hub content.

  • :tags (Array<Types::Tag>) —

    Any tags associated with the hub content.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17326

def import_hub_content(params = {}, options = {})
  req = build_request(:import_hub_content, params)
  req.send_request(options)
end

#list_actions(params = {}) ⇒ Types::ListActionsResponse

Lists the actions in your account and their properties.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_actions({
  source_uri: "SourceUri",
  action_type: "String256",
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.action_summaries #=> Array
resp.action_summaries[0].action_arn #=> String
resp.action_summaries[0].action_name #=> String
resp.action_summaries[0].source.source_uri #=> String
resp.action_summaries[0].source.source_type #=> String
resp.action_summaries[0].source.source_id #=> String
resp.action_summaries[0].action_type #=> String
resp.action_summaries[0].status #=> String, one of "Unknown", "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.action_summaries[0].creation_time #=> Time
resp.action_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_uri (String) —

    A filter that returns only actions with the specified source URI.

  • :action_type (String) —

    A filter that returns only actions of the specified type.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only actions created on or after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only actions created on or before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :next_token (String) —

    If the previous call to ‘ListActions` didn’t return the full set of actions, the call returns a token for getting the next set of actions.

  • :max_results (Integer) —

    The maximum number of actions to return in the response. The default value is 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17400

def list_actions(params = {}, options = {})
  req = build_request(:list_actions, params)
  req.send_request(options)
end

#list_algorithms(params = {}) ⇒ Types::ListAlgorithmsOutput

Lists the machine learning algorithms that have been created.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_algorithms({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "NameContains",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.algorithm_summary_list #=> Array
resp.algorithm_summary_list[0].algorithm_name #=> String
resp.algorithm_summary_list[0].algorithm_arn #=> String
resp.algorithm_summary_list[0].algorithm_description #=> String
resp.algorithm_summary_list[0].creation_time #=> Time
resp.algorithm_summary_list[0].algorithm_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only algorithms created after the specified time (timestamp).

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only algorithms created before the specified time (timestamp).

  • :max_results (Integer) —

    The maximum number of algorithms to return in the response.

  • :name_contains (String) —

    A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

  • :next_token (String) —

    If the response to a previous ‘ListAlgorithms` request was truncated, the response includes a `NextToken`. To retrieve the next set of algorithms, use the token in the next request.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for the results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17467

def list_algorithms(params = {}, options = {})
  req = build_request(:list_algorithms, params)
  req.send_request(options)
end

#list_aliases(params = {}) ⇒ Types::ListAliasesResponse

Lists the aliases of a specified image or image version.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_aliases({
  image_name: "ImageName", # required
  alias: "SageMakerImageVersionAlias",
  version: 1,
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.sage_maker_image_version_aliases #=> Array
resp.sage_maker_image_version_aliases[0] #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image_name (required, String) —

    The name of the image.

  • :alias (String) —

    The alias of the image version.

  • :version (Integer) —

    The version of the image. If image version is not specified, the aliases of all versions of the image are listed.

  • :max_results (Integer) —

    The maximum number of aliases to return.

  • :next_token (String) —

    If the previous call to ‘ListAliases` didn’t return the full set of aliases, the call returns a token for retrieving the next set of aliases.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17519

def list_aliases(params = {}, options = {})
  req = build_request(:list_aliases, params)
  req.send_request(options)
end

#list_app_image_configs(params = {}) ⇒ Types::ListAppImageConfigsResponse

Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_app_image_configs({
  max_results: 1,
  next_token: "NextToken",
  name_contains: "AppImageConfigName",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  modified_time_before: Time.now,
  modified_time_after: Time.now,
  sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime, Name
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.next_token #=> String
resp.app_image_configs #=> Array
resp.app_image_configs[0].app_image_config_arn #=> String
resp.app_image_configs[0].app_image_config_name #=> String
resp.app_image_configs[0].creation_time #=> Time
resp.app_image_configs[0].last_modified_time #=> Time
resp.app_image_configs[0].kernel_gateway_image_config.kernel_specs #=> Array
resp.app_image_configs[0].kernel_gateway_image_config.kernel_specs[0].name #=> String
resp.app_image_configs[0].kernel_gateway_image_config.kernel_specs[0].display_name #=> String
resp.app_image_configs[0].kernel_gateway_image_config.file_system_config.mount_path #=> String
resp.app_image_configs[0].kernel_gateway_image_config.file_system_config.default_uid #=> Integer
resp.app_image_configs[0].kernel_gateway_image_config.file_system_config.default_gid #=> Integer
resp.app_image_configs[0].jupyter_lab_app_image_config.file_system_config.mount_path #=> String
resp.app_image_configs[0].jupyter_lab_app_image_config.file_system_config.default_uid #=> Integer
resp.app_image_configs[0].jupyter_lab_app_image_config.file_system_config.default_gid #=> Integer
resp.app_image_configs[0].jupyter_lab_app_image_config.container_config.container_arguments #=> Array
resp.app_image_configs[0].jupyter_lab_app_image_config.container_config.container_arguments[0] #=> String
resp.app_image_configs[0].jupyter_lab_app_image_config.container_config.container_entrypoint #=> Array
resp.app_image_configs[0].jupyter_lab_app_image_config.container_config.container_entrypoint[0] #=> String
resp.app_image_configs[0].jupyter_lab_app_image_config.container_config.container_environment_variables #=> Hash
resp.app_image_configs[0].jupyter_lab_app_image_config.container_config.container_environment_variables["NonEmptyString256"] #=> String
resp.app_image_configs[0].code_editor_app_image_config.file_system_config.mount_path #=> String
resp.app_image_configs[0].code_editor_app_image_config.file_system_config.default_uid #=> Integer
resp.app_image_configs[0].code_editor_app_image_config.file_system_config.default_gid #=> Integer
resp.app_image_configs[0].code_editor_app_image_config.container_config.container_arguments #=> Array
resp.app_image_configs[0].code_editor_app_image_config.container_config.container_arguments[0] #=> String
resp.app_image_configs[0].code_editor_app_image_config.container_config.container_entrypoint #=> Array
resp.app_image_configs[0].code_editor_app_image_config.container_config.container_entrypoint[0] #=> String
resp.app_image_configs[0].code_editor_app_image_config.container_config.container_environment_variables #=> Hash
resp.app_image_configs[0].code_editor_app_image_config.container_config.container_environment_variables["NonEmptyString256"] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :max_results (Integer) —

    The total number of items to return in the response. If the total number of items available is more than the value specified, a ‘NextToken` is provided in the response. To resume pagination, provide the `NextToken` value in the as part of a subsequent call. The default value is 10.

  • :next_token (String) —

    If the previous call to ‘ListImages` didn’t return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.

  • :name_contains (String) —

    A filter that returns only AppImageConfigs whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only AppImageConfigs created on or before the specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only AppImageConfigs created on or after the specified time.

  • :modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only AppImageConfigs modified on or before the specified time.

  • :modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only AppImageConfigs modified on or after the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17625

def list_app_image_configs(params = {}, options = {})
  req = build_request(:list_app_image_configs, params)
  req.send_request(options)
end

#list_apps(params = {}) ⇒ Types::ListAppsResponse

Lists apps.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_apps({
  next_token: "NextToken",
  max_results: 1,
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "CreationTime", # accepts CreationTime
  domain_id_equals: "DomainId",
  user_profile_name_equals: "UserProfileName",
  space_name_equals: "SpaceName",
})

Response structure


resp.apps #=> Array
resp.apps[0].domain_id #=> String
resp.apps[0]. #=> String
resp.apps[0].space_name #=> String
resp.apps[0].app_type #=> String, one of "JupyterServer", "KernelGateway", "DetailedProfiler", "TensorBoard", "CodeEditor", "JupyterLab", "RStudioServerPro", "RSessionGateway", "Canvas"
resp.apps[0].app_name #=> String
resp.apps[0].status #=> String, one of "Deleted", "Deleting", "Failed", "InService", "Pending"
resp.apps[0].creation_time #=> Time
resp.apps[0].resource_spec.sage_maker_image_arn #=> String
resp.apps[0].resource_spec.sage_maker_image_version_arn #=> String
resp.apps[0].resource_spec.sage_maker_image_version_alias #=> String
resp.apps[0].resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.geospatial.interactive", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge"
resp.apps[0].resource_spec.lifecycle_config_arn #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

  • :max_results (Integer) —

    This parameter defines the maximum number of results that can be return in a single response. The ‘MaxResults` parameter is an upper bound, not a target. If there are more results available than the value specified, a `NextToken` is provided in the response. The `NextToken` indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for `MaxResults` is 10.

  • :sort_order (String) —

    The sort order for the results. The default is Ascending.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is CreationTime.

  • :domain_id_equals (String) —

    A parameter to search for the domain ID.

  • :user_profile_name_equals (String) —

    A parameter to search by user profile name. If ‘SpaceNameEquals` is set, then this value cannot be set.

  • :space_name_equals (String) —

    A parameter to search by space name. If ‘UserProfileNameEquals` is set, then this value cannot be set.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17703

def list_apps(params = {}, options = {})
  req = build_request(:list_apps, params)
  req.send_request(options)
end

#list_artifacts(params = {}) ⇒ Types::ListArtifactsResponse

Lists the artifacts in your account and their properties.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_artifacts({
  source_uri: "SourceUri",
  artifact_type: "String256",
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "CreationTime", # accepts CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.artifact_summaries #=> Array
resp.artifact_summaries[0].artifact_arn #=> String
resp.artifact_summaries[0].artifact_name #=> String
resp.artifact_summaries[0].source.source_uri #=> String
resp.artifact_summaries[0].source.source_types #=> Array
resp.artifact_summaries[0].source.source_types[0].source_id_type #=> String, one of "MD5Hash", "S3ETag", "S3Version", "Custom"
resp.artifact_summaries[0].source.source_types[0].value #=> String
resp.artifact_summaries[0].artifact_type #=> String
resp.artifact_summaries[0].creation_time #=> Time
resp.artifact_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_uri (String) —

    A filter that returns only artifacts with the specified source URI.

  • :artifact_type (String) —

    A filter that returns only artifacts of the specified type.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only artifacts created on or after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only artifacts created on or before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :next_token (String) —

    If the previous call to ‘ListArtifacts` didn’t return the full set of artifacts, the call returns a token for getting the next set of artifacts.

  • :max_results (Integer) —

    The maximum number of artifacts to return in the response. The default value is 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17778

def list_artifacts(params = {}, options = {})
  req = build_request(:list_artifacts, params)
  req.send_request(options)
end

#list_associations(params = {}) ⇒ Types::ListAssociationsResponse

Lists the associations in your account and their properties.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_associations({
  source_arn: "AssociationEntityArn",
  destination_arn: "AssociationEntityArn",
  source_type: "String256",
  destination_type: "String256",
  association_type: "ContributedTo", # accepts ContributedTo, AssociatedWith, DerivedFrom, Produced, SameAs
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "SourceArn", # accepts SourceArn, DestinationArn, SourceType, DestinationType, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.association_summaries #=> Array
resp.association_summaries[0].source_arn #=> String
resp.association_summaries[0].destination_arn #=> String
resp.association_summaries[0].source_type #=> String
resp.association_summaries[0].destination_type #=> String
resp.association_summaries[0].association_type #=> String, one of "ContributedTo", "AssociatedWith", "DerivedFrom", "Produced", "SameAs"
resp.association_summaries[0].source_name #=> String
resp.association_summaries[0].destination_name #=> String
resp.association_summaries[0].creation_time #=> Time
resp.association_summaries[0].created_by. #=> String
resp.association_summaries[0].created_by. #=> String
resp.association_summaries[0].created_by.domain_id #=> String
resp.association_summaries[0].created_by.iam_identity.arn #=> String
resp.association_summaries[0].created_by.iam_identity.principal_id #=> String
resp.association_summaries[0].created_by.iam_identity.source_identity #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_arn (String) —

    A filter that returns only associations with the specified source ARN.

  • :destination_arn (String) —

    A filter that returns only associations with the specified destination Amazon Resource Name (ARN).

  • :source_type (String) —

    A filter that returns only associations with the specified source type.

  • :destination_type (String) —

    A filter that returns only associations with the specified destination type.

  • :association_type (String) —

    A filter that returns only associations of the specified type.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only associations created on or after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only associations created on or before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :next_token (String) —

    If the previous call to ‘ListAssociations` didn’t return the full set of associations, the call returns a token for getting the next set of associations.

  • :max_results (Integer) —

    The maximum number of associations to return in the response. The default value is 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17873

def list_associations(params = {}, options = {})
  req = build_request(:list_associations, params)
  req.send_request(options)
end

#list_auto_ml_jobs(params = {}) ⇒ Types::ListAutoMLJobsResponse

Request a list of jobs.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_auto_ml_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "AutoMLNameContains",
  status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "Name", # accepts Name, CreationTime, Status
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.auto_ml_job_summaries #=> Array
resp.auto_ml_job_summaries[0].auto_ml_job_name #=> String
resp.auto_ml_job_summaries[0].auto_ml_job_arn #=> String
resp.auto_ml_job_summaries[0].auto_ml_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.auto_ml_job_summaries[0].auto_ml_job_secondary_status #=> String, one of "Starting", "MaxCandidatesReached", "Failed", "Stopped", "MaxAutoMLJobRuntimeReached", "Stopping", "CandidateDefinitionsGenerated", "Completed", "ExplainabilityError", "DeployingModel", "ModelDeploymentError", "GeneratingModelInsightsReport", "ModelInsightsError", "AnalyzingData", "FeatureEngineering", "ModelTuning", "GeneratingExplainabilityReport", "TrainingModels", "PreTraining"
resp.auto_ml_job_summaries[0].creation_time #=> Time
resp.auto_ml_job_summaries[0].end_time #=> Time
resp.auto_ml_job_summaries[0].last_modified_time #=> Time
resp.auto_ml_job_summaries[0].failure_reason #=> String
resp.auto_ml_job_summaries[0].partial_failure_reasons #=> Array
resp.auto_ml_job_summaries[0].partial_failure_reasons[0].partial_failure_message #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Request a list of jobs, using a filter for time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Request a list of jobs, using a filter for time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Request a list of jobs, using a filter for time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Request a list of jobs, using a filter for time.

  • :name_contains (String) —

    Request a list of jobs, using a search filter for name.

  • :status_equals (String) —

    Request a list of jobs, using a filter for status.

  • :sort_order (String) —

    The sort order for the results. The default is ‘Descending`.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is ‘Name`.

  • :max_results (Integer) —

    Request a list of jobs up to a specified limit.

  • :next_token (String) —

    If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 17952

def list_auto_ml_jobs(params = {}, options = {})
  req = build_request(:list_auto_ml_jobs, params)
  req.send_request(options)
end

#list_candidates_for_auto_ml_job(params = {}) ⇒ Types::ListCandidatesForAutoMLJobResponse

List the candidates created for the job.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_candidates_for_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
  status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping
  candidate_name_equals: "CandidateName",
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "CreationTime", # accepts CreationTime, Status, FinalObjectiveMetricValue
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.candidates #=> Array
resp.candidates[0].candidate_name #=> String
resp.candidates[0].final_auto_ml_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.candidates[0].final_auto_ml_job_objective_metric.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.candidates[0].final_auto_ml_job_objective_metric.value #=> Float
resp.candidates[0].final_auto_ml_job_objective_metric.standard_metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.candidates[0].objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.candidates[0].candidate_steps #=> Array
resp.candidates[0].candidate_steps[0].candidate_step_type #=> String, one of "AWS::SageMaker::TrainingJob", "AWS::SageMaker::TransformJob", "AWS::SageMaker::ProcessingJob"
resp.candidates[0].candidate_steps[0].candidate_step_arn #=> String
resp.candidates[0].candidate_steps[0].candidate_step_name #=> String
resp.candidates[0].candidate_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.candidates[0].inference_containers #=> Array
resp.candidates[0].inference_containers[0].image #=> String
resp.candidates[0].inference_containers[0].model_data_url #=> String
resp.candidates[0].inference_containers[0].environment #=> Hash
resp.candidates[0].inference_containers[0].environment["EnvironmentKey"] #=> String
resp.candidates[0].creation_time #=> Time
resp.candidates[0].end_time #=> Time
resp.candidates[0].last_modified_time #=> Time
resp.candidates[0].failure_reason #=> String
resp.candidates[0].candidate_properties.candidate_artifact_locations.explainability #=> String
resp.candidates[0].candidate_properties.candidate_artifact_locations.model_insights #=> String
resp.candidates[0].candidate_properties.candidate_artifact_locations.backtest_results #=> String
resp.candidates[0].candidate_properties.candidate_metrics #=> Array
resp.candidates[0].candidate_properties.candidate_metrics[0].metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "BalancedAccuracy", "R2", "Recall", "RecallMacro", "Precision", "PrecisionMacro", "MAE", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss"
resp.candidates[0].candidate_properties.candidate_metrics[0].value #=> Float
resp.candidates[0].candidate_properties.candidate_metrics[0].set #=> String, one of "Train", "Validation", "Test"
resp.candidates[0].candidate_properties.candidate_metrics[0].standard_metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC", "RMSE", "MAE", "R2", "BalancedAccuracy", "Precision", "PrecisionMacro", "Recall", "RecallMacro", "LogLoss", "InferenceLatency", "MAPE", "MASE", "WAPE", "AverageWeightedQuantileLoss", "Rouge1", "Rouge2", "RougeL", "RougeLSum", "Perplexity", "ValidationLoss", "TrainingLoss"
resp.candidates[0].inference_container_definitions #=> Hash
resp.candidates[0].inference_container_definitions["AutoMLProcessingUnit"] #=> Array
resp.candidates[0].inference_container_definitions["AutoMLProcessingUnit"][0].image #=> String
resp.candidates[0].inference_container_definitions["AutoMLProcessingUnit"][0].model_data_url #=> String
resp.candidates[0].inference_container_definitions["AutoMLProcessingUnit"][0].environment #=> Hash
resp.candidates[0].inference_container_definitions["AutoMLProcessingUnit"][0].environment["EnvironmentKey"] #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    List the candidates created for the job by providing the job’s name.

  • :status_equals (String) —

    List the candidates for the job and filter by status.

  • :candidate_name_equals (String) —

    List the candidates for the job and filter by candidate name.

  • :sort_order (String) —

    The sort order for the results. The default is ‘Ascending`.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is ‘Descending`.

  • :max_results (Integer) —

    List the job’s candidates up to a specified limit.

  • :next_token (String) —

    If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18044

def list_candidates_for_auto_ml_job(params = {}, options = {})
  req = build_request(:list_candidates_for_auto_ml_job, params)
  req.send_request(options)
end

#list_cluster_nodes(params = {}) ⇒ Types::ListClusterNodesResponse

Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_cluster_nodes({
  cluster_name: "ClusterNameOrArn", # required
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  instance_group_name_contains: "ClusterInstanceGroupName",
  max_results: 1,
  next_token: "NextToken",
  sort_by: "CREATION_TIME", # accepts CREATION_TIME, NAME
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.next_token #=> String
resp.cluster_node_summaries #=> Array
resp.cluster_node_summaries[0].instance_group_name #=> String
resp.cluster_node_summaries[0].instance_id #=> String
resp.cluster_node_summaries[0].instance_type #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.c5n.large", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge"
resp.cluster_node_summaries[0].launch_time #=> Time
resp.cluster_node_summaries[0].instance_status.status #=> String, one of "Running", "Failure", "Pending", "ShuttingDown", "SystemUpdating"
resp.cluster_node_summaries[0].instance_status.message #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard.

    Acceptable formats include:

    • ‘YYYY-MM-DDThh:mm:ss.sssTZD` (UTC), for example, `2014-10-01T20:30:00.000Z`

    • ‘YYYY-MM-DDThh:mm:ss.sssTZD` (with offset), for example, `2014-10-01T12:30:00.000-08:00`

    • ‘YYYY-MM-DD`, for example, `2014-10-01`

    • Unix time in seconds, for example, ‘1412195400`. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.

    For more information about the timestamp format, see [Timestamp] in the *Amazon Web Services Command Line Interface User Guide*.

    [1]: docs.aws.amazon.com/cli/latest/userguide/cli-usage-parameters-types.html#parameter-type-timestamp

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for ‘CreationTimeAfter`. For more information about the timestamp format, see [Timestamp] in the *Amazon Web Services Command Line Interface User Guide*.

    [1]: docs.aws.amazon.com/cli/latest/userguide/cli-usage-parameters-types.html#parameter-type-timestamp

  • :instance_group_name_contains (String) —

    A filter that returns the instance groups whose name contain a specified string.

  • :max_results (Integer) —

    The maximum number of nodes to return in the response.

  • :next_token (String) —

    If the result of the previous ‘ListClusterNodes` request was truncated, the response includes a `NextToken`. To retrieve the next set of cluster nodes, use the token in the next request.

  • :sort_by (String) —

    The field by which to sort results. The default value is ‘CREATION_TIME`.

  • :sort_order (String) —

    The sort order for results. The default value is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18147

def list_cluster_nodes(params = {}, options = {})
  req = build_request(:list_cluster_nodes, params)
  req.send_request(options)
end

#list_clusters(params = {}) ⇒ Types::ListClustersResponse

Retrieves the list of SageMaker HyperPod clusters.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_clusters({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "NameContains",
  next_token: "NextToken",
  sort_by: "CREATION_TIME", # accepts CREATION_TIME, NAME
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.next_token #=> String
resp.cluster_summaries #=> Array
resp.cluster_summaries[0].cluster_arn #=> String
resp.cluster_summaries[0].cluster_name #=> String
resp.cluster_summaries[0].creation_time #=> Time
resp.cluster_summaries[0].cluster_status #=> String, one of "Creating", "Deleting", "Failed", "InService", "RollingBack", "SystemUpdating", "Updating"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard.

    Acceptable formats include:

    • ‘YYYY-MM-DDThh:mm:ss.sssTZD` (UTC), for example, `2014-10-01T20:30:00.000Z`

    • ‘YYYY-MM-DDThh:mm:ss.sssTZD` (with offset), for example, `2014-10-01T12:30:00.000-08:00`

    • ‘YYYY-MM-DD`, for example, `2014-10-01`

    • Unix time in seconds, for example, ‘1412195400`. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.

    For more information about the timestamp format, see [Timestamp] in the *Amazon Web Services Command Line Interface User Guide*.

    [1]: docs.aws.amazon.com/cli/latest/userguide/cli-usage-parameters-types.html#parameter-type-timestamp

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for ‘CreationTimeAfter`. For more information about the timestamp format, see [Timestamp] in the *Amazon Web Services Command Line Interface User Guide*.

    [1]: docs.aws.amazon.com/cli/latest/userguide/cli-usage-parameters-types.html#parameter-type-timestamp

  • :max_results (Integer) —

    Set the maximum number of SageMaker HyperPod clusters to list.

  • :name_contains (String) —

    Set the maximum number of instances to print in the list.

  • :next_token (String) —

    Set the next token to retrieve the list of SageMaker HyperPod clusters.

  • :sort_by (String) —

    The field by which to sort results. The default value is ‘CREATION_TIME`.

  • :sort_order (String) —

    The sort order for results. The default value is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18242

def list_clusters(params = {}, options = {})
  req = build_request(:list_clusters, params)
  req.send_request(options)
end

#list_code_repositories(params = {}) ⇒ Types::ListCodeRepositoriesOutput

Gets a list of the Git repositories in your account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_code_repositories({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  max_results: 1,
  name_contains: "CodeRepositoryNameContains",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime, LastModifiedTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.code_repository_summary_list #=> Array
resp.code_repository_summary_list[0].code_repository_name #=> String
resp.code_repository_summary_list[0].code_repository_arn #=> String
resp.code_repository_summary_list[0].creation_time #=> Time
resp.code_repository_summary_list[0].last_modified_time #=> Time
resp.code_repository_summary_list[0].git_config.repository_url #=> String
resp.code_repository_summary_list[0].git_config.branch #=> String
resp.code_repository_summary_list[0].git_config.secret_arn #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only Git repositories that were created after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only Git repositories that were created before the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only Git repositories that were last modified after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only Git repositories that were last modified before the specified time.

  • :max_results (Integer) —

    The maximum number of Git repositories to return in the response.

  • :name_contains (String) —

    A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

  • :next_token (String) —

    If the result of a ‘ListCodeRepositoriesOutput` request was truncated, the response includes a `NextToken`. To get the next set of Git repositories, use the token in the next request.

  • :sort_by (String) —

    The field to sort results by. The default is ‘Name`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18320

def list_code_repositories(params = {}, options = {})
  req = build_request(:list_code_repositories, params)
  req.send_request(options)
end

#list_compilation_jobs(params = {}) ⇒ Types::ListCompilationJobsResponse

Lists model compilation jobs that satisfy various filters.

To create a model compilation job, use [CreateCompilationJob]. To get information about a particular model compilation job you have created, use [DescribeCompilationJob].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeCompilationJob.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_compilation_jobs({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "INPROGRESS", # accepts INPROGRESS, COMPLETED, FAILED, STARTING, STOPPING, STOPPED
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.compilation_job_summaries #=> Array
resp.compilation_job_summaries[0].compilation_job_name #=> String
resp.compilation_job_summaries[0].compilation_job_arn #=> String
resp.compilation_job_summaries[0].creation_time #=> Time
resp.compilation_job_summaries[0].compilation_start_time #=> Time
resp.compilation_job_summaries[0].compilation_end_time #=> Time
resp.compilation_job_summaries[0].compilation_target_device #=> String, one of "lambda", "ml_m4", "ml_m5", "ml_m6g", "ml_c4", "ml_c5", "ml_c6g", "ml_p2", "ml_p3", "ml_g4dn", "ml_inf1", "ml_inf2", "ml_trn1", "ml_eia2", "jetson_tx1", "jetson_tx2", "jetson_nano", "jetson_xavier", "rasp3b", "rasp4b", "imx8qm", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603", "sitara_am57x", "amba_cv2", "amba_cv22", "amba_cv25", "x86_win32", "x86_win64", "coreml", "jacinto_tda4vm", "imx8mplus"
resp.compilation_job_summaries[0].compilation_target_platform_os #=> String, one of "ANDROID", "LINUX"
resp.compilation_job_summaries[0].compilation_target_platform_arch #=> String, one of "X86_64", "X86", "ARM64", "ARM_EABI", "ARM_EABIHF"
resp.compilation_job_summaries[0].compilation_target_platform_accelerator #=> String, one of "INTEL_GRAPHICS", "MALI", "NVIDIA", "NNA"
resp.compilation_job_summaries[0].last_modified_time #=> Time
resp.compilation_job_summaries[0].compilation_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the result of the previous ‘ListCompilationJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of model compilation jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of model compilation jobs to return in the response.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns the model compilation jobs that were created after a specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns the model compilation jobs that were created before a specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns the model compilation jobs that were modified after a specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns the model compilation jobs that were modified before a specified time.

  • :name_contains (String) —

    A filter that returns the model compilation jobs whose name contains a specified string.

  • :status_equals (String) —

    A filter that retrieves model compilation jobs with a specific ‘CompilationJobStatus` status.

  • :sort_by (String) —

    The field by which to sort results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18417

def list_compilation_jobs(params = {}, options = {})
  req = build_request(:list_compilation_jobs, params)
  req.send_request(options)
end

#list_contexts(params = {}) ⇒ Types::ListContextsResponse

Lists the contexts in your account and their properties.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_contexts({
  source_uri: "SourceUri",
  context_type: "String256",
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.context_summaries #=> Array
resp.context_summaries[0].context_arn #=> String
resp.context_summaries[0].context_name #=> String
resp.context_summaries[0].source.source_uri #=> String
resp.context_summaries[0].source.source_type #=> String
resp.context_summaries[0].source.source_id #=> String
resp.context_summaries[0].context_type #=> String
resp.context_summaries[0].creation_time #=> Time
resp.context_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_uri (String) —

    A filter that returns only contexts with the specified source URI.

  • :context_type (String) —

    A filter that returns only contexts of the specified type.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only contexts created on or after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only contexts created on or before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :next_token (String) —

    If the previous call to ‘ListContexts` didn’t return the full set of contexts, the call returns a token for getting the next set of contexts.

  • :max_results (Integer) —

    The maximum number of contexts to return in the response. The default value is 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18491

def list_contexts(params = {}, options = {})
  req = build_request(:list_contexts, params)
  req.send_request(options)
end

#list_data_quality_job_definitions(params = {}) ⇒ Types::ListDataQualityJobDefinitionsResponse

Lists the data quality job definitions in your account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_data_quality_job_definitions({
  endpoint_name: "EndpointName",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  name_contains: "NameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.job_definition_summaries #=> Array
resp.job_definition_summaries[0].monitoring_job_definition_name #=> String
resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String
resp.job_definition_summaries[0].creation_time #=> Time
resp.job_definition_summaries[0].endpoint_name #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (String) —

    A filter that lists the data quality job definitions associated with the specified endpoint.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    Whether to sort the results in ‘Ascending` or `Descending` order. The default is `Descending`.

  • :next_token (String) —

    If the result of the previous ‘ListDataQualityJobDefinitions` request was truncated, the response includes a `NextToken`. To retrieve the next set of transform jobs, use the token in the next request.&gt;

  • :max_results (Integer) —

    The maximum number of data quality monitoring job definitions to return in the response.

  • :name_contains (String) —

    A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only data quality monitoring job definitions created before the specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only data quality monitoring job definitions created after the specified time.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18564

def list_data_quality_job_definitions(params = {}, options = {})
  req = build_request(:list_data_quality_job_definitions, params)
  req.send_request(options)
end

#list_device_fleets(params = {}) ⇒ Types::ListDeviceFleetsResponse

Returns a list of devices in the fleet.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_device_fleets({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  sort_by: "NAME", # accepts NAME, CREATION_TIME, LAST_MODIFIED_TIME
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.device_fleet_summaries #=> Array
resp.device_fleet_summaries[0].device_fleet_arn #=> String
resp.device_fleet_summaries[0].device_fleet_name #=> String
resp.device_fleet_summaries[0].creation_time #=> Time
resp.device_fleet_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    The response from the last list when returning a list large enough to need tokening.

  • :max_results (Integer) —

    The maximum number of results to select.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Filter fleets where packaging job was created after specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Filter fleets where the edge packaging job was created before specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Select fleets where the job was updated after X

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Select fleets where the job was updated before X

  • :name_contains (String) —

    Filter for fleets containing this name in their fleet device name.

  • :sort_by (String) —

    The column to sort by.

  • :sort_order (String) —

    What direction to sort in.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18634

def list_device_fleets(params = {}, options = {})
  req = build_request(:list_device_fleets, params)
  req.send_request(options)
end

#list_devices(params = {}) ⇒ Types::ListDevicesResponse

A list of devices.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_devices({
  next_token: "NextToken",
  max_results: 1,
  latest_heartbeat_after: Time.now,
  model_name: "EntityName",
  device_fleet_name: "EntityName",
})

Response structure


resp.device_summaries #=> Array
resp.device_summaries[0].device_name #=> String
resp.device_summaries[0].device_arn #=> String
resp.device_summaries[0].description #=> String
resp.device_summaries[0].device_fleet_name #=> String
resp.device_summaries[0].iot_thing_name #=> String
resp.device_summaries[0].registration_time #=> Time
resp.device_summaries[0].latest_heartbeat #=> Time
resp.device_summaries[0].models #=> Array
resp.device_summaries[0].models[0].model_name #=> String
resp.device_summaries[0].models[0].model_version #=> String
resp.device_summaries[0].agent_version #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    The response from the last list when returning a list large enough to need tokening.

  • :max_results (Integer) —

    Maximum number of results to select.

  • :latest_heartbeat_after (Time, DateTime, Date, Integer, String) —

    Select fleets where the job was updated after X

  • :model_name (String) —

    A filter that searches devices that contains this name in any of their models.

  • :device_fleet_name (String) —

    Filter for fleets containing this name in their device fleet name.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18695

def list_devices(params = {}, options = {})
  req = build_request(:list_devices, params)
  req.send_request(options)
end

#list_domains(params = {}) ⇒ Types::ListDomainsResponse

Lists the domains.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_domains({
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.domains #=> Array
resp.domains[0].domain_arn #=> String
resp.domains[0].domain_id #=> String
resp.domains[0].domain_name #=> String
resp.domains[0].status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed"
resp.domains[0].creation_time #=> Time
resp.domains[0].last_modified_time #=> Time
resp.domains[0].url #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

  • :max_results (Integer) —

    This parameter defines the maximum number of results that can be return in a single response. The ‘MaxResults` parameter is an upper bound, not a target. If there are more results available than the value specified, a `NextToken` is provided in the response. The `NextToken` indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for `MaxResults` is 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18745

def list_domains(params = {}, options = {})
  req = build_request(:list_domains, params)
  req.send_request(options)
end

#list_edge_deployment_plans(params = {}) ⇒ Types::ListEdgeDeploymentPlansResponse

Lists all edge deployment plans.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_edge_deployment_plans({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  device_fleet_name_contains: "NameContains",
  sort_by: "NAME", # accepts NAME, DEVICE_FLEET_NAME, CREATION_TIME, LAST_MODIFIED_TIME
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.edge_deployment_plan_summaries #=> Array
resp.edge_deployment_plan_summaries[0].edge_deployment_plan_arn #=> String
resp.edge_deployment_plan_summaries[0].edge_deployment_plan_name #=> String
resp.edge_deployment_plan_summaries[0].device_fleet_name #=> String
resp.edge_deployment_plan_summaries[0].edge_deployment_success #=> Integer
resp.edge_deployment_plan_summaries[0].edge_deployment_pending #=> Integer
resp.edge_deployment_plan_summaries[0].edge_deployment_failed #=> Integer
resp.edge_deployment_plan_summaries[0].creation_time #=> Time
resp.edge_deployment_plan_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    The response from the last list when returning a list large enough to need tokening.

  • :max_results (Integer) —

    The maximum number of results to select (50 by default).

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Selects edge deployment plans created after this time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Selects edge deployment plans created before this time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Selects edge deployment plans that were last updated after this time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Selects edge deployment plans that were last updated before this time.

  • :name_contains (String) —

    Selects edge deployment plans with names containing this name.

  • :device_fleet_name_contains (String) —

    Selects edge deployment plans with a device fleet name containing this name.

  • :sort_by (String) —

    The column by which to sort the edge deployment plans. Can be one of ‘NAME`, `DEVICEFLEETNAME`, `CREATIONTIME`, `LASTMODIFIEDTIME`.

  • :sort_order (String) —

    The direction of the sorting (ascending or descending).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18824

def list_edge_deployment_plans(params = {}, options = {})
  req = build_request(:list_edge_deployment_plans, params)
  req.send_request(options)
end

#list_edge_packaging_jobs(params = {}) ⇒ Types::ListEdgePackagingJobsResponse

Returns a list of edge packaging jobs.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_edge_packaging_jobs({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  model_name_contains: "NameContains",
  status_equals: "STARTING", # accepts STARTING, INPROGRESS, COMPLETED, FAILED, STOPPING, STOPPED
  sort_by: "NAME", # accepts NAME, MODEL_NAME, CREATION_TIME, LAST_MODIFIED_TIME, STATUS
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.edge_packaging_job_summaries #=> Array
resp.edge_packaging_job_summaries[0].edge_packaging_job_arn #=> String
resp.edge_packaging_job_summaries[0].edge_packaging_job_name #=> String
resp.edge_packaging_job_summaries[0].edge_packaging_job_status #=> String, one of "STARTING", "INPROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED"
resp.edge_packaging_job_summaries[0].compilation_job_name #=> String
resp.edge_packaging_job_summaries[0].model_name #=> String
resp.edge_packaging_job_summaries[0].model_version #=> String
resp.edge_packaging_job_summaries[0].creation_time #=> Time
resp.edge_packaging_job_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    The response from the last list when returning a list large enough to need tokening.

  • :max_results (Integer) —

    Maximum number of results to select.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Select jobs where the job was created after specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Select jobs where the job was created before specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Select jobs where the job was updated after specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Select jobs where the job was updated before specified time.

  • :name_contains (String) —

    Filter for jobs containing this name in their packaging job name.

  • :model_name_contains (String) —

    Filter for jobs where the model name contains this string.

  • :status_equals (String) —

    The job status to filter for.

  • :sort_by (String) —

    Use to specify what column to sort by.

  • :sort_order (String) —

    What direction to sort by.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18905

def list_edge_packaging_jobs(params = {}, options = {})
  req = build_request(:list_edge_packaging_jobs, params)
  req.send_request(options)
end

#list_endpoint_configs(params = {}) ⇒ Types::ListEndpointConfigsOutput

Lists endpoint configurations.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_endpoint_configs({
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "EndpointConfigNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.endpoint_configs #=> Array
resp.endpoint_configs[0].endpoint_config_name #=> String
resp.endpoint_configs[0].endpoint_config_arn #=> String
resp.endpoint_configs[0].creation_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Descending`.

  • :next_token (String) —

    If the result of the previous ‘ListEndpointConfig` request was truncated, the response includes a `NextToken`. To retrieve the next set of endpoint configurations, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of training jobs to return in the response.

  • :name_contains (String) —

    A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only endpoint configurations created before the specified time (timestamp).

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 18969

def list_endpoint_configs(params = {}, options = {})
  req = build_request(:list_endpoint_configs, params)
  req.send_request(options)
end

#list_endpoints(params = {}) ⇒ Types::ListEndpointsOutput

Lists endpoints.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_endpoints({
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "EndpointNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "OutOfService", # accepts OutOfService, Creating, Updating, SystemUpdating, RollingBack, InService, Deleting, Failed, UpdateRollbackFailed
})

Response structure


resp.endpoints #=> Array
resp.endpoints[0].endpoint_name #=> String
resp.endpoints[0].endpoint_arn #=> String
resp.endpoints[0].creation_time #=> Time
resp.endpoints[0].last_modified_time #=> Time
resp.endpoints[0].endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed", "UpdateRollbackFailed"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :sort_by (String) —

    Sorts the list of results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Descending`.

  • :next_token (String) —

    If the result of a ‘ListEndpoints` request was truncated, the response includes a `NextToken`. To retrieve the next set of endpoints, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of endpoints to return in the response. This value defaults to 10.

  • :name_contains (String) —

    A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only endpoints that were created before the specified time (timestamp).

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only endpoints that were modified before the specified timestamp.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only endpoints that were modified after the specified timestamp.

  • :status_equals (String) —

    A filter that returns only endpoints with the specified status.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19050

def list_endpoints(params = {}, options = {})
  req = build_request(:list_endpoints, params)
  req.send_request(options)
end

#list_experiments(params = {}) ⇒ Types::ListExperimentsResponse

Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_experiments({
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.experiment_summaries #=> Array
resp.experiment_summaries[0].experiment_arn #=> String
resp.experiment_summaries[0].experiment_name #=> String
resp.experiment_summaries[0].display_name #=> String
resp.experiment_summaries[0].experiment_source.source_arn #=> String
resp.experiment_summaries[0].experiment_source.source_type #=> String
resp.experiment_summaries[0].creation_time #=> Time
resp.experiment_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only experiments created after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only experiments created before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :next_token (String) —

    If the previous call to ‘ListExperiments` didn’t return the full set of experiments, the call returns a token for getting the next set of experiments.

  • :max_results (Integer) —

    The maximum number of experiments to return in the response. The default value is 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19117

def list_experiments(params = {}, options = {})
  req = build_request(:list_experiments, params)
  req.send_request(options)
end

#list_feature_groups(params = {}) ⇒ Types::ListFeatureGroupsResponse

List ‘FeatureGroup`s based on given filter and order.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_feature_groups({
  name_contains: "FeatureGroupNameContains",
  feature_group_status_equals: "Creating", # accepts Creating, Created, CreateFailed, Deleting, DeleteFailed
  offline_store_status_equals: "Active", # accepts Active, Blocked, Disabled
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "Name", # accepts Name, FeatureGroupStatus, OfflineStoreStatus, CreationTime
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.feature_group_summaries #=> Array
resp.feature_group_summaries[0].feature_group_name #=> String
resp.feature_group_summaries[0].feature_group_arn #=> String
resp.feature_group_summaries[0].creation_time #=> Time
resp.feature_group_summaries[0].feature_group_status #=> String, one of "Creating", "Created", "CreateFailed", "Deleting", "DeleteFailed"
resp.feature_group_summaries[0].offline_store_status.status #=> String, one of "Active", "Blocked", "Disabled"
resp.feature_group_summaries[0].offline_store_status.blocked_reason #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name_contains (String) —

    A string that partially matches one or more ‘FeatureGroup`s names. Filters `FeatureGroup`s by name.

  • :feature_group_status_equals (String) —

    A ‘FeatureGroup` status. Filters by `FeatureGroup` status.

  • :offline_store_status_equals (String) —

    An ‘OfflineStore` status. Filters by `OfflineStore` status.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Use this parameter to search for ‘FeatureGroups`s created after a specific date and time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Use this parameter to search for ‘FeatureGroups`s created before a specific date and time.

  • :sort_order (String) —

    The order in which feature groups are listed.

  • :sort_by (String) —

    The value on which the feature group list is sorted.

  • :max_results (Integer) —

    The maximum number of results returned by ‘ListFeatureGroups`.

  • :next_token (String) —

    A token to resume pagination of ‘ListFeatureGroups` results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19190

def list_feature_groups(params = {}, options = {})
  req = build_request(:list_feature_groups, params)
  req.send_request(options)
end

#list_flow_definitions(params = {}) ⇒ Types::ListFlowDefinitionsResponse

Returns information about the flow definitions in your account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_flow_definitions({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.flow_definition_summaries #=> Array
resp.flow_definition_summaries[0].flow_definition_name #=> String
resp.flow_definition_summaries[0].flow_definition_arn #=> String
resp.flow_definition_summaries[0].flow_definition_status #=> String, one of "Initializing", "Active", "Failed", "Deleting"
resp.flow_definition_summaries[0].creation_time #=> Time
resp.flow_definition_summaries[0].failure_reason #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only flow definitions that were created before the specified timestamp.

  • :sort_order (String) —

    An optional value that specifies whether you want the results sorted in ‘Ascending` or `Descending` order.

  • :next_token (String) —

    A token to resume pagination.

  • :max_results (Integer) —

    The total number of items to return. If the total number of available items is more than the value specified in ‘MaxResults`, then a `NextToken` will be provided in the output that you can use to resume pagination.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19249

def list_flow_definitions(params = {}, options = {})
  req = build_request(:list_flow_definitions, params)
  req.send_request(options)
end

#list_hub_content_versions(params = {}) ⇒ Types::ListHubContentVersionsResponse

List hub content versions.

Examples:

Request syntax with placeholder values


resp = client.list_hub_content_versions({
  hub_name: "HubNameOrArn", # required
  hub_content_type: "Model", # required, accepts Model, Notebook, ModelReference
  hub_content_name: "HubContentName", # required
  min_version: "HubContentVersion",
  max_schema_version: "DocumentSchemaVersion",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  sort_by: "HubContentName", # accepts HubContentName, CreationTime, HubContentStatus
  sort_order: "Ascending", # accepts Ascending, Descending
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.hub_content_summaries #=> Array
resp.hub_content_summaries[0].hub_content_name #=> String
resp.hub_content_summaries[0].hub_content_arn #=> String
resp.hub_content_summaries[0].sage_maker_public_hub_content_arn #=> String
resp.hub_content_summaries[0].hub_content_version #=> String
resp.hub_content_summaries[0].hub_content_type #=> String, one of "Model", "Notebook", "ModelReference"
resp.hub_content_summaries[0].document_schema_version #=> String
resp.hub_content_summaries[0].hub_content_display_name #=> String
resp.hub_content_summaries[0].hub_content_description #=> String
resp.hub_content_summaries[0].support_status #=> String, one of "Supported", "Deprecated"
resp.hub_content_summaries[0].hub_content_search_keywords #=> Array
resp.hub_content_summaries[0].hub_content_search_keywords[0] #=> String
resp.hub_content_summaries[0].hub_content_status #=> String, one of "Available", "Importing", "Deleting", "ImportFailed", "DeleteFailed"
resp.hub_content_summaries[0].creation_time #=> Time
resp.hub_content_summaries[0].original_creation_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to list the content versions of.

  • :hub_content_type (required, String) —

    The type of hub content to list versions of.

  • :hub_content_name (required, String) —

    The name of the hub content.

  • :min_version (String) —

    The lower bound of the hub content versions to list.

  • :max_schema_version (String) —

    The upper bound of the hub content schema version.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Only list hub content versions that were created before the time specified.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Only list hub content versions that were created after the time specified.

  • :sort_by (String) —

    Sort hub content versions by either name or creation time.

  • :sort_order (String) —

    Sort hub content versions by ascending or descending order.

  • :max_results (Integer) —

    The maximum number of hub content versions to list.

  • :next_token (String) —

    If the response to a previous ‘ListHubContentVersions` request was truncated, the response includes a `NextToken`. To retrieve the next set of hub content versions, use the token in the next request.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19337

def list_hub_content_versions(params = {}, options = {})
  req = build_request(:list_hub_content_versions, params)
  req.send_request(options)
end

#list_hub_contents(params = {}) ⇒ Types::ListHubContentsResponse

List the contents of a hub.

Examples:

Request syntax with placeholder values


resp = client.list_hub_contents({
  hub_name: "HubNameOrArn", # required
  hub_content_type: "Model", # required, accepts Model, Notebook, ModelReference
  name_contains: "NameContains",
  max_schema_version: "DocumentSchemaVersion",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  sort_by: "HubContentName", # accepts HubContentName, CreationTime, HubContentStatus
  sort_order: "Ascending", # accepts Ascending, Descending
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.hub_content_summaries #=> Array
resp.hub_content_summaries[0].hub_content_name #=> String
resp.hub_content_summaries[0].hub_content_arn #=> String
resp.hub_content_summaries[0].sage_maker_public_hub_content_arn #=> String
resp.hub_content_summaries[0].hub_content_version #=> String
resp.hub_content_summaries[0].hub_content_type #=> String, one of "Model", "Notebook", "ModelReference"
resp.hub_content_summaries[0].document_schema_version #=> String
resp.hub_content_summaries[0].hub_content_display_name #=> String
resp.hub_content_summaries[0].hub_content_description #=> String
resp.hub_content_summaries[0].support_status #=> String, one of "Supported", "Deprecated"
resp.hub_content_summaries[0].hub_content_search_keywords #=> Array
resp.hub_content_summaries[0].hub_content_search_keywords[0] #=> String
resp.hub_content_summaries[0].hub_content_status #=> String, one of "Available", "Importing", "Deleting", "ImportFailed", "DeleteFailed"
resp.hub_content_summaries[0].creation_time #=> Time
resp.hub_content_summaries[0].original_creation_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to list the contents of.

  • :hub_content_type (required, String) —

    The type of hub content to list.

  • :name_contains (String) —

    Only list hub content if the name contains the specified string.

  • :max_schema_version (String) —

    The upper bound of the hub content schema verion.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Only list hub content that was created before the time specified.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Only list hub content that was created after the time specified.

  • :sort_by (String) —

    Sort hub content versions by either name or creation time.

  • :sort_order (String) —

    Sort hubs by ascending or descending order.

  • :max_results (Integer) —

    The maximum amount of hub content to list.

  • :next_token (String) —

    If the response to a previous ‘ListHubContents` request was truncated, the response includes a `NextToken`. To retrieve the next set of hub content, use the token in the next request.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19419

def list_hub_contents(params = {}, options = {})
  req = build_request(:list_hub_contents, params)
  req.send_request(options)
end

#list_hubs(params = {}) ⇒ Types::ListHubsResponse

List all existing hubs.

Examples:

Request syntax with placeholder values


resp = client.list_hubs({
  name_contains: "NameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  sort_by: "HubName", # accepts HubName, CreationTime, HubStatus, AccountIdOwner
  sort_order: "Ascending", # accepts Ascending, Descending
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.hub_summaries #=> Array
resp.hub_summaries[0].hub_name #=> String
resp.hub_summaries[0].hub_arn #=> String
resp.hub_summaries[0].hub_display_name #=> String
resp.hub_summaries[0].hub_description #=> String
resp.hub_summaries[0].hub_search_keywords #=> Array
resp.hub_summaries[0].hub_search_keywords[0] #=> String
resp.hub_summaries[0].hub_status #=> String, one of "InService", "Creating", "Updating", "Deleting", "CreateFailed", "UpdateFailed", "DeleteFailed"
resp.hub_summaries[0].creation_time #=> Time
resp.hub_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name_contains (String) —

    Only list hubs with names that contain the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Only list hubs that were created before the time specified.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Only list hubs that were created after the time specified.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Only list hubs that were last modified before the time specified.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Only list hubs that were last modified after the time specified.

  • :sort_by (String) —

    Sort hubs by either name or creation time.

  • :sort_order (String) —

    Sort hubs by ascending or descending order.

  • :max_results (Integer) —

    The maximum number of hubs to list.

  • :next_token (String) —

    If the response to a previous ‘ListHubs` request was truncated, the response includes a `NextToken`. To retrieve the next set of hubs, use the token in the next request.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19492

def list_hubs(params = {}, options = {})
  req = build_request(:list_hubs, params)
  req.send_request(options)
end

#list_human_task_uis(params = {}) ⇒ Types::ListHumanTaskUisResponse

Returns information about the human task user interfaces in your account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_human_task_uis({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.human_task_ui_summaries #=> Array
resp.human_task_ui_summaries[0].human_task_ui_name #=> String
resp.human_task_ui_summaries[0].human_task_ui_arn #=> String
resp.human_task_ui_summaries[0].creation_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only human task user interfaces that were created before the specified timestamp.

  • :sort_order (String) —

    An optional value that specifies whether you want the results sorted in ‘Ascending` or `Descending` order.

  • :next_token (String) —

    A token to resume pagination.

  • :max_results (Integer) —

    The total number of items to return. If the total number of available items is more than the value specified in ‘MaxResults`, then a `NextToken` will be provided in the output that you can use to resume pagination.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19550

def list_human_task_uis(params = {}, options = {})
  req = build_request(:list_human_task_uis, params)
  req.send_request(options)
end

#list_hyper_parameter_tuning_jobs(params = {}) ⇒ Types::ListHyperParameterTuningJobsResponse

Gets a list of [HyperParameterTuningJobSummary] objects that describe the hyperparameter tuning jobs launched in your account.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobSummary.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_hyper_parameter_tuning_jobs({
  next_token: "NextToken",
  max_results: 1,
  sort_by: "Name", # accepts Name, Status, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "NameContains",
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping, Deleting, DeleteFailed
})

Response structure


resp.hyper_parameter_tuning_job_summaries #=> Array
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_name #=> String
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_arn #=> String
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping", "Deleting", "DeleteFailed"
resp.hyper_parameter_tuning_job_summaries[0].strategy #=> String, one of "Bayesian", "Random", "Hyperband", "Grid"
resp.hyper_parameter_tuning_job_summaries[0].creation_time #=> Time
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_end_time #=> Time
resp.hyper_parameter_tuning_job_summaries[0].last_modified_time #=> Time
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.completed #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.in_progress #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.retryable_error #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.non_retryable_error #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.stopped #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.succeeded #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.pending #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.failed #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_number_of_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_parallel_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_runtime_in_seconds #=> Integer
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the result of the previous ‘ListHyperParameterTuningJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of tuning jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of tuning jobs to return. The default value is 10.

  • :sort_by (String) —

    The field to sort results by. The default is ‘Name`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

  • :name_contains (String) —

    A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only tuning jobs that were created after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only tuning jobs that were created before the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only tuning jobs that were modified after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only tuning jobs that were modified before the specified time.

  • :status_equals (String) —

    A filter that returns only tuning jobs with the specified status.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19648

def list_hyper_parameter_tuning_jobs(params = {}, options = {})
  req = build_request(:list_hyper_parameter_tuning_jobs, params)
  req.send_request(options)
end

#list_image_versions(params = {}) ⇒ Types::ListImageVersionsResponse

Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_image_versions({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  image_name: "ImageName", # required
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  max_results: 1,
  next_token: "NextToken",
  sort_by: "CREATION_TIME", # accepts CREATION_TIME, LAST_MODIFIED_TIME, VERSION
  sort_order: "ASCENDING", # accepts ASCENDING, DESCENDING
})

Response structure


resp.image_versions #=> Array
resp.image_versions[0].creation_time #=> Time
resp.image_versions[0].failure_reason #=> String
resp.image_versions[0].image_arn #=> String
resp.image_versions[0].image_version_arn #=> String
resp.image_versions[0].image_version_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "DELETING", "DELETE_FAILED"
resp.image_versions[0].last_modified_time #=> Time
resp.image_versions[0].version #=> Integer
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only versions created on or after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only versions created on or before the specified time.

  • :image_name (required, String) —

    The name of the image to list the versions of.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only versions modified on or after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only versions modified on or before the specified time.

  • :max_results (Integer) —

    The maximum number of versions to return in the response. The default value is 10.

  • :next_token (String) —

    If the previous call to ‘ListImageVersions` didn’t return the full set of versions, the call returns a token for getting the next set of versions.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CREATION_TIME`.

  • :sort_order (String) —

    The sort order. The default value is ‘DESCENDING`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19728

def list_image_versions(params = {}, options = {})
  req = build_request(:list_image_versions, params)
  req.send_request(options)
end

#list_images(params = {}) ⇒ Types::ListImagesResponse

Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_images({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  max_results: 1,
  name_contains: "ImageNameContains",
  next_token: "NextToken",
  sort_by: "CREATION_TIME", # accepts CREATION_TIME, LAST_MODIFIED_TIME, IMAGE_NAME
  sort_order: "ASCENDING", # accepts ASCENDING, DESCENDING
})

Response structure


resp.images #=> Array
resp.images[0].creation_time #=> Time
resp.images[0].description #=> String
resp.images[0].display_name #=> String
resp.images[0].failure_reason #=> String
resp.images[0].image_arn #=> String
resp.images[0].image_name #=> String
resp.images[0].image_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "UPDATING", "UPDATE_FAILED", "DELETING", "DELETE_FAILED"
resp.images[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only images created on or after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only images created on or before the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only images modified on or after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only images modified on or before the specified time.

  • :max_results (Integer) —

    The maximum number of images to return in the response. The default value is 10.

  • :name_contains (String) —

    A filter that returns only images whose name contains the specified string.

  • :next_token (String) —

    If the previous call to ‘ListImages` didn’t return the full set of images, the call returns a token for getting the next set of images.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CREATION_TIME`.

  • :sort_order (String) —

    The sort order. The default value is ‘DESCENDING`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19810

def list_images(params = {}, options = {})
  req = build_request(:list_images, params)
  req.send_request(options)
end

#list_inference_components(params = {}) ⇒ Types::ListInferenceComponentsOutput

Lists the inference components in your account and their properties.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_inference_components({
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "InferenceComponentNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "InService", # accepts InService, Creating, Updating, Failed, Deleting
  endpoint_name_equals: "EndpointName",
  variant_name_equals: "VariantName",
})

Response structure


resp.inference_components #=> Array
resp.inference_components[0].creation_time #=> Time
resp.inference_components[0].inference_component_arn #=> String
resp.inference_components[0].inference_component_name #=> String
resp.inference_components[0].endpoint_arn #=> String
resp.inference_components[0].endpoint_name #=> String
resp.inference_components[0].variant_name #=> String
resp.inference_components[0].inference_component_status #=> String, one of "InService", "Creating", "Updating", "Failed", "Deleting"
resp.inference_components[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :sort_by (String) —

    The field by which to sort the inference components in the response. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Descending`.

  • :next_token (String) —

    A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

  • :max_results (Integer) —

    The maximum number of inference components to return in the response. This value defaults to 10.

  • :name_contains (String) —

    Filters the results to only those inference components with a name that contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Filters the results to only those inference components that were created before the specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Filters the results to only those inference components that were created after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Filters the results to only those inference components that were updated before the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Filters the results to only those inference components that were updated after the specified time.

  • :status_equals (String) —

    Filters the results to only those inference components with the specified status.

  • :endpoint_name_equals (String) —

    An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.

  • :variant_name_equals (String) —

    A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 19908

def list_inference_components(params = {}, options = {})
  req = build_request(:list_inference_components, params)
  req.send_request(options)
end

#list_inference_experiments(params = {}) ⇒ Types::ListInferenceExperimentsResponse

Returns the list of all inference experiments.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_inference_experiments({
  name_contains: "NameContains",
  type: "ShadowMode", # accepts ShadowMode
  status_equals: "Creating", # accepts Creating, Created, Updating, Running, Starting, Stopping, Completed, Cancelled
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.inference_experiments #=> Array
resp.inference_experiments[0].name #=> String
resp.inference_experiments[0].type #=> String, one of "ShadowMode"
resp.inference_experiments[0].schedule.start_time #=> Time
resp.inference_experiments[0].schedule.end_time #=> Time
resp.inference_experiments[0].status #=> String, one of "Creating", "Created", "Updating", "Running", "Starting", "Stopping", "Completed", "Cancelled"
resp.inference_experiments[0].status_reason #=> String
resp.inference_experiments[0].description #=> String
resp.inference_experiments[0].creation_time #=> Time
resp.inference_experiments[0].completion_time #=> Time
resp.inference_experiments[0].last_modified_time #=> Time
resp.inference_experiments[0].role_arn #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name_contains (String) —

    Selects inference experiments whose names contain this name.

  • :type (String) —

    Selects inference experiments of this type. For the possible types of inference experiments, see [CreateInferenceExperiment].

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceExperiment.html

  • :status_equals (String) —

    Selects inference experiments which are in this status. For the possible statuses, see [DescribeInferenceExperiment].

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeInferenceExperiment.html

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Selects inference experiments which were created after this timestamp.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Selects inference experiments which were created before this timestamp.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Selects inference experiments which were last modified after this timestamp.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Selects inference experiments which were last modified before this timestamp.

  • :sort_by (String) —

    The column by which to sort the listed inference experiments.

  • :sort_order (String) —

    The direction of sorting (ascending or descending).

  • :next_token (String) —

    The response from the last list when returning a list large enough to need tokening.

  • :max_results (Integer) —

    The maximum number of results to select.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20005

def list_inference_experiments(params = {}, options = {})
  req = build_request(:list_inference_experiments, params)
  req.send_request(options)
end

#list_inference_recommendations_job_steps(params = {}) ⇒ Types::ListInferenceRecommendationsJobStepsResponse

Returns a list of the subtasks for an Inference Recommender job.

The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_inference_recommendations_job_steps({
  job_name: "RecommendationJobName", # required
  status: "PENDING", # accepts PENDING, IN_PROGRESS, COMPLETED, FAILED, STOPPING, STOPPED, DELETING, DELETED
  step_type: "BENCHMARK", # accepts BENCHMARK
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.steps #=> Array
resp.steps[0].step_type #=> String, one of "BENCHMARK"
resp.steps[0].job_name #=> String
resp.steps[0].status #=> String, one of "PENDING", "IN_PROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED", "DELETING", "DELETED"
resp.steps[0].inference_benchmark.metrics.cost_per_hour #=> Float
resp.steps[0].inference_benchmark.metrics.cost_per_inference #=> Float
resp.steps[0].inference_benchmark.metrics.max_invocations #=> Integer
resp.steps[0].inference_benchmark.metrics.model_latency #=> Integer
resp.steps[0].inference_benchmark.metrics.cpu_utilization #=> Float
resp.steps[0].inference_benchmark.metrics.memory_utilization #=> Float
resp.steps[0].inference_benchmark.metrics.model_setup_time #=> Integer
resp.steps[0].inference_benchmark.endpoint_metrics.max_invocations #=> Integer
resp.steps[0].inference_benchmark.endpoint_metrics.model_latency #=> Integer
resp.steps[0].inference_benchmark.endpoint_configuration.endpoint_name #=> String
resp.steps[0].inference_benchmark.endpoint_configuration.variant_name #=> String
resp.steps[0].inference_benchmark.endpoint_configuration.instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge"
resp.steps[0].inference_benchmark.endpoint_configuration.initial_instance_count #=> Integer
resp.steps[0].inference_benchmark.endpoint_configuration.serverless_config.memory_size_in_mb #=> Integer
resp.steps[0].inference_benchmark.endpoint_configuration.serverless_config.max_concurrency #=> Integer
resp.steps[0].inference_benchmark.endpoint_configuration.serverless_config.provisioned_concurrency #=> Integer
resp.steps[0].inference_benchmark.model_configuration.inference_specification_name #=> String
resp.steps[0].inference_benchmark.model_configuration.environment_parameters #=> Array
resp.steps[0].inference_benchmark.model_configuration.environment_parameters[0].key #=> String
resp.steps[0].inference_benchmark.model_configuration.environment_parameters[0].value_type #=> String
resp.steps[0].inference_benchmark.model_configuration.environment_parameters[0].value #=> String
resp.steps[0].inference_benchmark.model_configuration.compilation_job_name #=> String
resp.steps[0].inference_benchmark.failure_reason #=> String
resp.steps[0].inference_benchmark.invocation_end_time #=> Time
resp.steps[0].inference_benchmark.invocation_start_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_name (required, String) —

    The name for the Inference Recommender job.

  • :status (String) —

    A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.

  • :step_type (String) —

    A filter to return details about the specified type of subtask.

    ‘BENCHMARK`: Evaluate the performance of your model on different instance types.

  • :max_results (Integer) —

    The maximum number of results to return.

  • :next_token (String) —

    A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20090

def list_inference_recommendations_job_steps(params = {}, options = {})
  req = build_request(:list_inference_recommendations_job_steps, params)
  req.send_request(options)
end

#list_inference_recommendations_jobs(params = {}) ⇒ Types::ListInferenceRecommendationsJobsResponse

Lists recommendation jobs that satisfy various filters.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_inference_recommendations_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "PENDING", # accepts PENDING, IN_PROGRESS, COMPLETED, FAILED, STOPPING, STOPPED, DELETING, DELETED
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  model_name_equals: "ModelName",
  model_package_version_arn_equals: "ModelPackageArn",
})

Response structure


resp.inference_recommendations_jobs #=> Array
resp.inference_recommendations_jobs[0].job_name #=> String
resp.inference_recommendations_jobs[0].job_description #=> String
resp.inference_recommendations_jobs[0].job_type #=> String, one of "Default", "Advanced"
resp.inference_recommendations_jobs[0].job_arn #=> String
resp.inference_recommendations_jobs[0].status #=> String, one of "PENDING", "IN_PROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED", "DELETING", "DELETED"
resp.inference_recommendations_jobs[0].creation_time #=> Time
resp.inference_recommendations_jobs[0].completion_time #=> Time
resp.inference_recommendations_jobs[0].role_arn #=> String
resp.inference_recommendations_jobs[0].last_modified_time #=> Time
resp.inference_recommendations_jobs[0].failure_reason #=> String
resp.inference_recommendations_jobs[0].model_name #=> String
resp.inference_recommendations_jobs[0].sample_payload_url #=> String
resp.inference_recommendations_jobs[0].model_package_version_arn #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs created after the specified time (timestamp).

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs created before the specified time (timestamp).

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs that were last modified after the specified time (timestamp).

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs that were last modified before the specified time (timestamp).

  • :name_contains (String) —

    A string in the job name. This filter returns only recommendations whose name contains the specified string.

  • :status_equals (String) —

    A filter that retrieves only inference recommendations jobs with a specific status.

  • :sort_by (String) —

    The parameter by which to sort the results.

  • :sort_order (String) —

    The sort order for the results.

  • :next_token (String) —

    If the response to a previous ‘ListInferenceRecommendationsJobsRequest` request was truncated, the response includes a `NextToken`. To retrieve the next set of recommendations, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of recommendations to return in the response.

  • :model_name_equals (String) —

    A filter that returns only jobs that were created for this model.

  • :model_package_version_arn_equals (String) —

    A filter that returns only jobs that were created for this versioned model package.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20189

def list_inference_recommendations_jobs(params = {}, options = {})
  req = build_request(:list_inference_recommendations_jobs, params)
  req.send_request(options)
end

#list_labeling_jobs(params = {}) ⇒ Types::ListLabelingJobsResponse

Gets a list of labeling jobs.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_labeling_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  max_results: 1,
  next_token: "NextToken",
  name_contains: "NameContains",
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  status_equals: "Initializing", # accepts Initializing, InProgress, Completed, Failed, Stopping, Stopped
})

Response structure


resp.labeling_job_summary_list #=> Array
resp.labeling_job_summary_list[0].labeling_job_name #=> String
resp.labeling_job_summary_list[0].labeling_job_arn #=> String
resp.labeling_job_summary_list[0].creation_time #=> Time
resp.labeling_job_summary_list[0].last_modified_time #=> Time
resp.labeling_job_summary_list[0].labeling_job_status #=> String, one of "Initializing", "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.labeling_job_summary_list[0].label_counters.total_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.human_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.machine_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.failed_non_retryable_error #=> Integer
resp.labeling_job_summary_list[0].label_counters.unlabeled #=> Integer
resp.labeling_job_summary_list[0].workteam_arn #=> String
resp.labeling_job_summary_list[0].pre_human_task_lambda_arn #=> String
resp.labeling_job_summary_list[0].annotation_consolidation_lambda_arn #=> String
resp.labeling_job_summary_list[0].failure_reason #=> String
resp.labeling_job_summary_list[0].labeling_job_output.output_dataset_s3_uri #=> String
resp.labeling_job_summary_list[0].labeling_job_output.final_active_learning_model_arn #=> String
resp.labeling_job_summary_list[0].input_config.data_source.s3_data_source.manifest_s3_uri #=> String
resp.labeling_job_summary_list[0].input_config.data_source.sns_data_source.sns_topic_arn #=> String
resp.labeling_job_summary_list[0].input_config.data_attributes.content_classifiers #=> Array
resp.labeling_job_summary_list[0].input_config.data_attributes.content_classifiers[0] #=> String, one of "FreeOfPersonallyIdentifiableInformation", "FreeOfAdultContent"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only labeling jobs created after the specified time (timestamp).

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only labeling jobs created before the specified time (timestamp).

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only labeling jobs modified after the specified time (timestamp).

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only labeling jobs modified before the specified time (timestamp).

  • :max_results (Integer) —

    The maximum number of labeling jobs to return in each page of the response.

  • :next_token (String) —

    If the result of the previous ‘ListLabelingJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.

  • :name_contains (String) —

    A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

  • :status_equals (String) —

    A filter that retrieves only labeling jobs with a specific status.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20285

def list_labeling_jobs(params = {}, options = {})
  req = build_request(:list_labeling_jobs, params)
  req.send_request(options)
end

#list_labeling_jobs_for_workteam(params = {}) ⇒ Types::ListLabelingJobsForWorkteamResponse

Gets a list of labeling jobs assigned to a specified work team.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_labeling_jobs_for_workteam({
  workteam_arn: "WorkteamArn", # required
  max_results: 1,
  next_token: "NextToken",
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  job_reference_code_contains: "JobReferenceCodeContains",
  sort_by: "CreationTime", # accepts CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.labeling_job_summary_list #=> Array
resp.labeling_job_summary_list[0].labeling_job_name #=> String
resp.labeling_job_summary_list[0].job_reference_code #=> String
resp.labeling_job_summary_list[0]. #=> String
resp.labeling_job_summary_list[0].creation_time #=> Time
resp.labeling_job_summary_list[0].label_counters.human_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.pending_human #=> Integer
resp.labeling_job_summary_list[0].label_counters.total #=> Integer
resp.labeling_job_summary_list[0].number_of_human_workers_per_data_object #=> Integer
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_arn (required, String) —

    The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

  • :max_results (Integer) —

    The maximum number of labeling jobs to return in each page of the response.

  • :next_token (String) —

    If the result of the previous ‘ListLabelingJobsForWorkteam` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only labeling jobs created after the specified time (timestamp).

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only labeling jobs created before the specified time (timestamp).

  • :job_reference_code_contains (String) —

    A filter the limits jobs to only the ones whose job reference code contains the specified string.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20360

def list_labeling_jobs_for_workteam(params = {}, options = {})
  req = build_request(:list_labeling_jobs_for_workteam, params)
  req.send_request(options)
end

#list_lineage_groups(params = {}) ⇒ Types::ListLineageGroupsResponse

A list of lineage groups shared with your Amazon Web Services account. For more information, see [ Cross-Account Lineage Tracking ][1] in the *Amazon SageMaker Developer Guide*.

[1]: docs.aws.amazon.com/sagemaker/latest/dg/xaccount-lineage-tracking.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_lineage_groups({
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.lineage_group_summaries #=> Array
resp.lineage_group_summaries[0].lineage_group_arn #=> String
resp.lineage_group_summaries[0].lineage_group_name #=> String
resp.lineage_group_summaries[0].display_name #=> String
resp.lineage_group_summaries[0].creation_time #=> Time
resp.lineage_group_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :created_after (Time, DateTime, Date, Integer, String) —

    A timestamp to filter against lineage groups created after a certain point in time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A timestamp to filter against lineage groups created before a certain point in time.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for the results. The default is ‘Ascending`.

  • :next_token (String) —

    If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

  • :max_results (Integer) —

    The maximum number of endpoints to return in the response. This value defaults to 10.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20428

def list_lineage_groups(params = {}, options = {})
  req = build_request(:list_lineage_groups, params)
  req.send_request(options)
end

#list_mlflow_tracking_servers(params = {}) ⇒ Types::ListMlflowTrackingServersResponse

Lists all MLflow Tracking Servers.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_mlflow_tracking_servers({
  created_after: Time.now,
  created_before: Time.now,
  tracking_server_status: "Creating", # accepts Creating, Created, CreateFailed, Updating, Updated, UpdateFailed, Deleting, DeleteFailed, Stopping, Stopped, StopFailed, Starting, Started, StartFailed, MaintenanceInProgress, MaintenanceComplete, MaintenanceFailed
  mlflow_version: "MlflowVersion",
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.tracking_server_summaries #=> Array
resp.tracking_server_summaries[0].tracking_server_arn #=> String
resp.tracking_server_summaries[0].tracking_server_name #=> String
resp.tracking_server_summaries[0].creation_time #=> Time
resp.tracking_server_summaries[0].last_modified_time #=> Time
resp.tracking_server_summaries[0].tracking_server_status #=> String, one of "Creating", "Created", "CreateFailed", "Updating", "Updated", "UpdateFailed", "Deleting", "DeleteFailed", "Stopping", "Stopped", "StopFailed", "Starting", "Started", "StartFailed", "MaintenanceInProgress", "MaintenanceComplete", "MaintenanceFailed"
resp.tracking_server_summaries[0].is_active #=> String, one of "Active", "Inactive"
resp.tracking_server_summaries[0].mlflow_version #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :created_after (Time, DateTime, Date, Integer, String) —

    Use the ‘CreatedAfter` filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as `“2024-03-16T01:46:56+00:00”`. The `CreatedAfter` parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see [EpochConverter].

    [1]: www.epochconverter.com/

  • :created_before (Time, DateTime, Date, Integer, String) —

    Use the ‘CreatedBefore` filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as `“2024-03-16T01:46:56+00:00”`. The `CreatedBefore` parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see [EpochConverter].

    [1]: www.epochconverter.com/

  • :tracking_server_status (String) —

    Filter for tracking servers with a specified creation status.

  • :mlflow_version (String) —

    Filter for tracking servers using the specified MLflow version.

  • :sort_by (String) —

    Filter for trackings servers sorting by name, creation time, or creation status.

  • :sort_order (String) —

    Change the order of the listed tracking servers. By default, tracking servers are listed in ‘Descending` order by creation time. To change the list order, you can specify `SortOrder` to be `Ascending`.

  • :next_token (String) —

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

  • :max_results (Integer) —

    The maximum number of tracking servers to list.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20515

def list_mlflow_tracking_servers(params = {}, options = {})
  req = build_request(:list_mlflow_tracking_servers, params)
  req.send_request(options)
end

#list_model_bias_job_definitions(params = {}) ⇒ Types::ListModelBiasJobDefinitionsResponse

Lists model bias jobs definitions that satisfy various filters.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_bias_job_definitions({
  endpoint_name: "EndpointName",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  name_contains: "NameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.job_definition_summaries #=> Array
resp.job_definition_summaries[0].monitoring_job_definition_name #=> String
resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String
resp.job_definition_summaries[0].creation_time #=> Time
resp.job_definition_summaries[0].endpoint_name #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (String) —

    Name of the endpoint to monitor for model bias.

  • :sort_by (String) —

    Whether to sort results by the ‘Name` or `CreationTime` field. The default is `CreationTime`.

  • :sort_order (String) —

    Whether to sort the results in ‘Ascending` or `Descending` order. The default is `Descending`.

  • :next_token (String) —

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

  • :max_results (Integer) —

    The maximum number of model bias jobs to return in the response. The default value is 10.

  • :name_contains (String) —

    Filter for model bias jobs whose name contains a specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only model bias jobs created before a specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only model bias jobs created after a specified time.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20585

def list_model_bias_job_definitions(params = {}, options = {})
  req = build_request(:list_model_bias_job_definitions, params)
  req.send_request(options)
end

#list_model_card_export_jobs(params = {}) ⇒ Types::ListModelCardExportJobsResponse

List the export jobs for the Amazon SageMaker Model Card.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_card_export_jobs({
  model_card_name: "EntityName", # required
  model_card_version: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  model_card_export_job_name_contains: "EntityName",
  status_equals: "InProgress", # accepts InProgress, Completed, Failed
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.model_card_export_job_summaries #=> Array
resp.model_card_export_job_summaries[0].model_card_export_job_name #=> String
resp.model_card_export_job_summaries[0].model_card_export_job_arn #=> String
resp.model_card_export_job_summaries[0].status #=> String, one of "InProgress", "Completed", "Failed"
resp.model_card_export_job_summaries[0].model_card_name #=> String
resp.model_card_export_job_summaries[0].model_card_version #=> Integer
resp.model_card_export_job_summaries[0].created_at #=> Time
resp.model_card_export_job_summaries[0].last_modified_at #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_name (required, String) —

    List export jobs for the model card with the specified name.

  • :model_card_version (Integer) —

    List export jobs for the model card with the specified version.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Only list model card export jobs that were created after the time specified.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Only list model card export jobs that were created before the time specified.

  • :model_card_export_job_name_contains (String) —

    Only list model card export jobs with names that contain the specified string.

  • :status_equals (String) —

    Only list model card export jobs with the specified status.

  • :sort_by (String) —

    Sort model card export jobs by either name or creation time. Sorts by creation time by default.

  • :sort_order (String) —

    Sort model card export jobs by ascending or descending order.

  • :next_token (String) —

    If the response to a previous ‘ListModelCardExportJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of model card export jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of model card export jobs to list.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20666

def list_model_card_export_jobs(params = {}, options = {})
  req = build_request(:list_model_card_export_jobs, params)
  req.send_request(options)
end

#list_model_card_versions(params = {}) ⇒ Types::ListModelCardVersionsResponse

List existing versions of an Amazon SageMaker Model Card.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_card_versions({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  model_card_name: "ModelCardNameOrArn", # required
  model_card_status: "Draft", # accepts Draft, PendingReview, Approved, Archived
  next_token: "NextToken",
  sort_by: "Version", # accepts Version
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.model_card_version_summary_list #=> Array
resp.model_card_version_summary_list[0].model_card_name #=> String
resp.model_card_version_summary_list[0].model_card_arn #=> String
resp.model_card_version_summary_list[0].model_card_status #=> String, one of "Draft", "PendingReview", "Approved", "Archived"
resp.model_card_version_summary_list[0].model_card_version #=> Integer
resp.model_card_version_summary_list[0].creation_time #=> Time
resp.model_card_version_summary_list[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Only list model card versions that were created after the time specified.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Only list model card versions that were created before the time specified.

  • :max_results (Integer) —

    The maximum number of model card versions to list.

  • :model_card_name (required, String) —

    List model card versions for the model card with the specified name or Amazon Resource Name (ARN).

  • :model_card_status (String) —

    Only list model card versions with the specified approval status.

  • :next_token (String) —

    If the response to a previous ‘ListModelCardVersions` request was truncated, the response includes a `NextToken`. To retrieve the next set of model card versions, use the token in the next request.

  • :sort_by (String) —

    Sort listed model card versions by version. Sorts by version by default.

  • :sort_order (String) —

    Sort model card versions by ascending or descending order.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20738

def list_model_card_versions(params = {}, options = {})
  req = build_request(:list_model_card_versions, params)
  req.send_request(options)
end

#list_model_cards(params = {}) ⇒ Types::ListModelCardsResponse

List existing model cards.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_cards({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "EntityName",
  model_card_status: "Draft", # accepts Draft, PendingReview, Approved, Archived
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.model_card_summaries #=> Array
resp.model_card_summaries[0].model_card_name #=> String
resp.model_card_summaries[0].model_card_arn #=> String
resp.model_card_summaries[0].model_card_status #=> String, one of "Draft", "PendingReview", "Approved", "Archived"
resp.model_card_summaries[0].creation_time #=> Time
resp.model_card_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Only list model cards that were created after the time specified.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Only list model cards that were created before the time specified.

  • :max_results (Integer) —

    The maximum number of model cards to list.

  • :name_contains (String) —

    Only list model cards with names that contain the specified string.

  • :model_card_status (String) —

    Only list model cards with the specified approval status.

  • :next_token (String) —

    If the response to a previous ‘ListModelCards` request was truncated, the response includes a `NextToken`. To retrieve the next set of model cards, use the token in the next request.

  • :sort_by (String) —

    Sort model cards by either name or creation time. Sorts by creation time by default.

  • :sort_order (String) —

    Sort model cards by ascending or descending order.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20806

def list_model_cards(params = {}, options = {})
  req = build_request(:list_model_cards, params)
  req.send_request(options)
end

#list_model_explainability_job_definitions(params = {}) ⇒ Types::ListModelExplainabilityJobDefinitionsResponse

Lists model explainability job definitions that satisfy various filters.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_explainability_job_definitions({
  endpoint_name: "EndpointName",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  name_contains: "NameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.job_definition_summaries #=> Array
resp.job_definition_summaries[0].monitoring_job_definition_name #=> String
resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String
resp.job_definition_summaries[0].creation_time #=> Time
resp.job_definition_summaries[0].endpoint_name #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (String) —

    Name of the endpoint to monitor for model explainability.

  • :sort_by (String) —

    Whether to sort results by the ‘Name` or `CreationTime` field. The default is `CreationTime`.

  • :sort_order (String) —

    Whether to sort the results in ‘Ascending` or `Descending` order. The default is `Descending`.

  • :next_token (String) —

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

  • :max_results (Integer) —

    The maximum number of jobs to return in the response. The default value is 10.

  • :name_contains (String) —

    Filter for model explainability jobs whose name contains a specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only model explainability jobs created before a specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only model explainability jobs created after a specified time.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20878

def list_model_explainability_job_definitions(params = {}, options = {})
  req = build_request(:list_model_explainability_job_definitions, params)
  req.send_request(options)
end

#list_model_metadata(params = {}) ⇒ Types::ListModelMetadataResponse

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_metadata({
  search_expression: {
    filters: [
      {
        name: "Domain", # required, accepts Domain, Framework, Task, FrameworkVersion
        value: "String256", # required
      },
    ],
  },
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.model_metadata_summaries #=> Array
resp.model_metadata_summaries[0].domain #=> String
resp.model_metadata_summaries[0].framework #=> String
resp.model_metadata_summaries[0].task #=> String
resp.model_metadata_summaries[0].model #=> String
resp.model_metadata_summaries[0].framework_version #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :search_expression (Types::ModelMetadataSearchExpression) —

    One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression’s condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.

  • :next_token (String) —

    If the response to a previous ‘ListModelMetadataResponse` request was truncated, the response includes a NextToken. To retrieve the next set of model metadata, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of models to return in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 20937

def list_model_metadata(params = {}, options = {})
  req = build_request(:list_model_metadata, params)
  req.send_request(options)
end

#list_model_package_groups(params = {}) ⇒ Types::ListModelPackageGroupsOutput

Gets a list of the model groups in your Amazon Web Services account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_package_groups({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "NameContains",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  cross_account_filter_option: "SameAccount", # accepts SameAccount, CrossAccount
})

Response structure


resp.model_package_group_summary_list #=> Array
resp.model_package_group_summary_list[0].model_package_group_name #=> String
resp.model_package_group_summary_list[0].model_package_group_arn #=> String
resp.model_package_group_summary_list[0].model_package_group_description #=> String
resp.model_package_group_summary_list[0].creation_time #=> Time
resp.model_package_group_summary_list[0].model_package_group_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting", "DeleteFailed"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only model groups created after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only model groups created before the specified time.

  • :max_results (Integer) —

    The maximum number of results to return in the response.

  • :name_contains (String) —

    A string in the model group name. This filter returns only model groups whose name contains the specified string.

  • :next_token (String) —

    If the result of the previous ‘ListModelPackageGroups` request was truncated, the response includes a `NextToken`. To retrieve the next set of model groups, use the token in the next request.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

  • :cross_account_filter_option (String) —

    A filter that returns either model groups shared with you or model groups in your own account. When the value is ‘CrossAccount`, the results show the resources made discoverable to you from other accounts. When the value is `SameAccount` or `null`, the results show resources from your account. The default is `SameAccount`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21011

def list_model_package_groups(params = {}, options = {})
  req = build_request(:list_model_package_groups, params)
  req.send_request(options)
end

#list_model_packages(params = {}) ⇒ Types::ListModelPackagesOutput

Lists the model packages that have been created.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_packages({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "NameContains",
  model_approval_status: "Approved", # accepts Approved, Rejected, PendingManualApproval
  model_package_group_name: "ArnOrName",
  model_package_type: "Versioned", # accepts Versioned, Unversioned, Both
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.model_package_summary_list #=> Array
resp.model_package_summary_list[0].model_package_name #=> String
resp.model_package_summary_list[0].model_package_group_name #=> String
resp.model_package_summary_list[0].model_package_version #=> Integer
resp.model_package_summary_list[0].model_package_arn #=> String
resp.model_package_summary_list[0].model_package_description #=> String
resp.model_package_summary_list[0].creation_time #=> Time
resp.model_package_summary_list[0].model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.model_package_summary_list[0].model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only model packages created after the specified time (timestamp).

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only model packages created before the specified time (timestamp).

  • :max_results (Integer) —

    The maximum number of model packages to return in the response.

  • :name_contains (String) —

    A string in the model package name. This filter returns only model packages whose name contains the specified string.

  • :model_approval_status (String) —

    A filter that returns only the model packages with the specified approval status.

  • :model_package_group_name (String) —

    A filter that returns only model versions that belong to the specified model group.

  • :model_package_type (String) —

    A filter that returns only the model packages of the specified type. This can be one of the following values.

    • ‘UNVERSIONED` - List only unversioined models. This is the default value if no `ModelPackageType` is specified.

    • ‘VERSIONED` - List only versioned models.

    • ‘BOTH` - List both versioned and unversioned models.

  • :next_token (String) —

    If the response to a previous ‘ListModelPackages` request was truncated, the response includes a `NextToken`. To retrieve the next set of model packages, use the token in the next request.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for the results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21103

def list_model_packages(params = {}, options = {})
  req = build_request(:list_model_packages, params)
  req.send_request(options)
end

#list_model_quality_job_definitions(params = {}) ⇒ Types::ListModelQualityJobDefinitionsResponse

Gets a list of model quality monitoring job definitions in your account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_model_quality_job_definitions({
  endpoint_name: "EndpointName",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  name_contains: "NameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.job_definition_summaries #=> Array
resp.job_definition_summaries[0].monitoring_job_definition_name #=> String
resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String
resp.job_definition_summaries[0].creation_time #=> Time
resp.job_definition_summaries[0].endpoint_name #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (String) —

    A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    Whether to sort the results in ‘Ascending` or `Descending` order. The default is `Descending`.

  • :next_token (String) —

    If the result of the previous ‘ListModelQualityJobDefinitions` request was truncated, the response includes a `NextToken`. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of results to return in a call to ‘ListModelQualityJobDefinitions`.

  • :name_contains (String) —

    A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only model quality monitoring job definitions created before the specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only model quality monitoring job definitions created after the specified time.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21178

def list_model_quality_job_definitions(params = {}, options = {})
  req = build_request(:list_model_quality_job_definitions, params)
  req.send_request(options)
end

#list_models(params = {}) ⇒ Types::ListModelsOutput

Lists models created with the ‘CreateModel` API.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_models({
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "ModelNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.models #=> Array
resp.models[0].model_name #=> String
resp.models[0].model_arn #=> String
resp.models[0].creation_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :sort_by (String) —

    Sorts the list of results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Descending`.

  • :next_token (String) —

    If the response to a previous ‘ListModels` request was truncated, the response includes a `NextToken`. To retrieve the next set of models, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of models to return in the response.

  • :name_contains (String) —

    A string in the model name. This filter returns only models whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only models created before the specified time (timestamp).

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21242

def list_models(params = {}, options = {})
  req = build_request(:list_models, params)
  req.send_request(options)
end

#list_monitoring_alert_history(params = {}) ⇒ Types::ListMonitoringAlertHistoryResponse

Gets a list of past alerts in a model monitoring schedule.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_monitoring_alert_history({
  monitoring_schedule_name: "MonitoringScheduleName",
  monitoring_alert_name: "MonitoringAlertName",
  sort_by: "CreationTime", # accepts CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  status_equals: "InAlert", # accepts InAlert, OK
})

Response structure


resp.monitoring_alert_history #=> Array
resp.monitoring_alert_history[0].monitoring_schedule_name #=> String
resp.monitoring_alert_history[0].monitoring_alert_name #=> String
resp.monitoring_alert_history[0].creation_time #=> Time
resp.monitoring_alert_history[0].alert_status #=> String, one of "InAlert", "OK"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (String) —

    The name of a monitoring schedule.

  • :monitoring_alert_name (String) —

    The name of a monitoring alert.

  • :sort_by (String) —

    The field used to sort results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order, whether ‘Ascending` or `Descending`, of the alert history. The default is `Descending`.

  • :next_token (String) —

    If the result of the previous ‘ListMonitoringAlertHistory` request was truncated, the response includes a `NextToken`. To retrieve the next set of alerts in the history, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of results to display. The default is 100.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only alerts created on or before the specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only alerts created on or after the specified time.

  • :status_equals (String) —

    A filter that retrieves only alerts with a specific status.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21315

def list_monitoring_alert_history(params = {}, options = {})
  req = build_request(:list_monitoring_alert_history, params)
  req.send_request(options)
end

#list_monitoring_alerts(params = {}) ⇒ Types::ListMonitoringAlertsResponse

Gets the alerts for a single monitoring schedule.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_monitoring_alerts({
  monitoring_schedule_name: "MonitoringScheduleName", # required
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.monitoring_alert_summaries #=> Array
resp.monitoring_alert_summaries[0].monitoring_alert_name #=> String
resp.monitoring_alert_summaries[0].creation_time #=> Time
resp.monitoring_alert_summaries[0].last_modified_time #=> Time
resp.monitoring_alert_summaries[0].alert_status #=> String, one of "InAlert", "OK"
resp.monitoring_alert_summaries[0].datapoints_to_alert #=> Integer
resp.monitoring_alert_summaries[0].evaluation_period #=> Integer
resp.monitoring_alert_summaries[0].actions.model_dashboard_indicator.enabled #=> Boolean
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of a monitoring schedule.

  • :next_token (String) —

    If the result of the previous ‘ListMonitoringAlerts` request was truncated, the response includes a `NextToken`. To retrieve the next set of alerts in the history, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of results to display. The default is 100.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21364

def list_monitoring_alerts(params = {}, options = {})
  req = build_request(:list_monitoring_alerts, params)
  req.send_request(options)
end

#list_monitoring_executions(params = {}) ⇒ Types::ListMonitoringExecutionsResponse

Returns list of all monitoring job executions.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_monitoring_executions({
  monitoring_schedule_name: "MonitoringScheduleName",
  endpoint_name: "EndpointName",
  sort_by: "CreationTime", # accepts CreationTime, ScheduledTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  scheduled_time_before: Time.now,
  scheduled_time_after: Time.now,
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "Pending", # accepts Pending, Completed, CompletedWithViolations, InProgress, Failed, Stopping, Stopped
  monitoring_job_definition_name: "MonitoringJobDefinitionName",
  monitoring_type_equals: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability
})

Response structure


resp.monitoring_execution_summaries #=> Array
resp.monitoring_execution_summaries[0].monitoring_schedule_name #=> String
resp.monitoring_execution_summaries[0].scheduled_time #=> Time
resp.monitoring_execution_summaries[0].creation_time #=> Time
resp.monitoring_execution_summaries[0].last_modified_time #=> Time
resp.monitoring_execution_summaries[0].monitoring_execution_status #=> String, one of "Pending", "Completed", "CompletedWithViolations", "InProgress", "Failed", "Stopping", "Stopped"
resp.monitoring_execution_summaries[0].processing_job_arn #=> String
resp.monitoring_execution_summaries[0].endpoint_name #=> String
resp.monitoring_execution_summaries[0].failure_reason #=> String
resp.monitoring_execution_summaries[0].monitoring_job_definition_name #=> String
resp.monitoring_execution_summaries[0].monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (String) —

    Name of a specific schedule to fetch jobs for.

  • :endpoint_name (String) —

    Name of a specific endpoint to fetch jobs for.

  • :sort_by (String) —

    Whether to sort the results by the ‘Status`, `CreationTime`, or `ScheduledTime` field. The default is `CreationTime`.

  • :sort_order (String) —

    Whether to sort the results in ‘Ascending` or `Descending` order. The default is `Descending`.

  • :next_token (String) —

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

  • :max_results (Integer) —

    The maximum number of jobs to return in the response. The default value is 10.

  • :scheduled_time_before (Time, DateTime, Date, Integer, String) —

    Filter for jobs scheduled before a specified time.

  • :scheduled_time_after (Time, DateTime, Date, Integer, String) —

    Filter for jobs scheduled after a specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs created before a specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs created after a specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs modified after a specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only jobs modified before a specified time.

  • :status_equals (String) —

    A filter that retrieves only jobs with a specific status.

  • :monitoring_job_definition_name (String) —

    Gets a list of the monitoring job runs of the specified monitoring job definitions.

  • :monitoring_type_equals (String) —

    A filter that returns only the monitoring job runs of the specified monitoring type.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21468

def list_monitoring_executions(params = {}, options = {})
  req = build_request(:list_monitoring_executions, params)
  req.send_request(options)
end

#list_monitoring_schedules(params = {}) ⇒ Types::ListMonitoringSchedulesResponse

Returns list of all monitoring schedules.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_monitoring_schedules({
  endpoint_name: "EndpointName",
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  name_contains: "NameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "Pending", # accepts Pending, Failed, Scheduled, Stopped
  monitoring_job_definition_name: "MonitoringJobDefinitionName",
  monitoring_type_equals: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability
})

Response structure


resp.monitoring_schedule_summaries #=> Array
resp.monitoring_schedule_summaries[0].monitoring_schedule_name #=> String
resp.monitoring_schedule_summaries[0].monitoring_schedule_arn #=> String
resp.monitoring_schedule_summaries[0].creation_time #=> Time
resp.monitoring_schedule_summaries[0].last_modified_time #=> Time
resp.monitoring_schedule_summaries[0].monitoring_schedule_status #=> String, one of "Pending", "Failed", "Scheduled", "Stopped"
resp.monitoring_schedule_summaries[0].endpoint_name #=> String
resp.monitoring_schedule_summaries[0].monitoring_job_definition_name #=> String
resp.monitoring_schedule_summaries[0].monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (String) —

    Name of a specific endpoint to fetch schedules for.

  • :sort_by (String) —

    Whether to sort the results by the ‘Status`, `CreationTime`, or `ScheduledTime` field. The default is `CreationTime`.

  • :sort_order (String) —

    Whether to sort the results in ‘Ascending` or `Descending` order. The default is `Descending`.

  • :next_token (String) —

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

  • :max_results (Integer) —

    The maximum number of jobs to return in the response. The default value is 10.

  • :name_contains (String) —

    Filter for monitoring schedules whose name contains a specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only monitoring schedules created before a specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only monitoring schedules created after a specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only monitoring schedules modified before a specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only monitoring schedules modified after a specified time.

  • :status_equals (String) —

    A filter that returns only monitoring schedules modified before a specified time.

  • :monitoring_job_definition_name (String) —

    Gets a list of the monitoring schedules for the specified monitoring job definition.

  • :monitoring_type_equals (String) —

    A filter that returns only the monitoring schedules for the specified monitoring type.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21568

def list_monitoring_schedules(params = {}, options = {})
  req = build_request(:list_monitoring_schedules, params)
  req.send_request(options)
end

#list_notebook_instance_lifecycle_configs(params = {}) ⇒ Types::ListNotebookInstanceLifecycleConfigsOutput

Lists notebook instance lifestyle configurations created with the

CreateNotebookInstanceLifecycleConfig][1

API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateNotebookInstanceLifecycleConfig.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_notebook_instance_lifecycle_configs({
  next_token: "NextToken",
  max_results: 1,
  sort_by: "Name", # accepts Name, CreationTime, LastModifiedTime
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "NotebookInstanceLifecycleConfigNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
})

Response structure


resp.next_token #=> String
resp.notebook_instance_lifecycle_configs #=> Array
resp.notebook_instance_lifecycle_configs[0].notebook_instance_lifecycle_config_name #=> String
resp.notebook_instance_lifecycle_configs[0].notebook_instance_lifecycle_config_arn #=> String
resp.notebook_instance_lifecycle_configs[0].creation_time #=> Time
resp.notebook_instance_lifecycle_configs[0].last_modified_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the result of a ‘ListNotebookInstanceLifecycleConfigs` request was truncated, the response includes a `NextToken`. To get the next set of lifecycle configurations, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of lifecycle configurations to return in the response.

  • :sort_by (String) —

    Sorts the list of results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results.

  • :name_contains (String) —

    A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21649

def list_notebook_instance_lifecycle_configs(params = {}, options = {})
  req = build_request(:list_notebook_instance_lifecycle_configs, params)
  req.send_request(options)
end

#list_notebook_instances(params = {}) ⇒ Types::ListNotebookInstancesOutput

Returns a list of the SageMaker notebook instances in the requester’s account in an Amazon Web Services Region.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_notebook_instances({
  next_token: "NextToken",
  max_results: 1,
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "NotebookInstanceNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "Pending", # accepts Pending, InService, Stopping, Stopped, Failed, Deleting, Updating
  notebook_instance_lifecycle_config_name_contains: "NotebookInstanceLifecycleConfigName",
  default_code_repository_contains: "CodeRepositoryContains",
  additional_code_repository_equals: "CodeRepositoryNameOrUrl",
})

Response structure


resp.next_token #=> String
resp.notebook_instances #=> Array
resp.notebook_instances[0].notebook_instance_name #=> String
resp.notebook_instances[0].notebook_instance_arn #=> String
resp.notebook_instances[0].notebook_instance_status #=> String, one of "Pending", "InService", "Stopping", "Stopped", "Failed", "Deleting", "Updating"
resp.notebook_instances[0].url #=> String
resp.notebook_instances[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.16xlarge", "ml.g5.12xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge", "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.m6id.large", "ml.m6id.xlarge", "ml.m6id.2xlarge", "ml.m6id.4xlarge", "ml.m6id.8xlarge", "ml.m6id.12xlarge", "ml.m6id.16xlarge", "ml.m6id.24xlarge", "ml.m6id.32xlarge", "ml.c6id.large", "ml.c6id.xlarge", "ml.c6id.2xlarge", "ml.c6id.4xlarge", "ml.c6id.8xlarge", "ml.c6id.12xlarge", "ml.c6id.16xlarge", "ml.c6id.24xlarge", "ml.c6id.32xlarge", "ml.r6id.large", "ml.r6id.xlarge", "ml.r6id.2xlarge", "ml.r6id.4xlarge", "ml.r6id.8xlarge", "ml.r6id.12xlarge", "ml.r6id.16xlarge", "ml.r6id.24xlarge", "ml.r6id.32xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge"
resp.notebook_instances[0].creation_time #=> Time
resp.notebook_instances[0].last_modified_time #=> Time
resp.notebook_instances[0].notebook_instance_lifecycle_config_name #=> String
resp.notebook_instances[0].default_code_repository #=> String
resp.notebook_instances[0].additional_code_repositories #=> Array
resp.notebook_instances[0].additional_code_repositories[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the previous call to the ‘ListNotebookInstances` is truncated, the response includes a `NextToken`. You can use this token in your subsequent `ListNotebookInstances` request to fetch the next set of notebook instances.

    <note markdown=“1”> You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.

    </note>
  • :max_results (Integer) —

    The maximum number of notebook instances to return.

  • :sort_by (String) —

    The field to sort results by. The default is ‘Name`.

  • :sort_order (String) —

    The sort order for results.

  • :name_contains (String) —

    A string in the notebook instances’ name. This filter returns only notebook instances whose name contains the specified string.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only notebook instances that were created before the specified time (timestamp).

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only notebook instances that were created after the specified time (timestamp).

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only notebook instances that were modified before the specified time (timestamp).

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only notebook instances that were modified after the specified time (timestamp).

  • :status_equals (String) —

    A filter that returns only notebook instances with the specified status.

  • :notebook_instance_lifecycle_config_name_contains (String) —

    A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.

  • :default_code_repository_contains (String) —

    A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.

  • :additional_code_repository_equals (String) —

    A filter that returns only notebook instances with associated with the specified git repository.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21763

def list_notebook_instances(params = {}, options = {})
  req = build_request(:list_notebook_instances, params)
  req.send_request(options)
end

#list_optimization_jobs(params = {}) ⇒ Types::ListOptimizationJobsResponse

Lists the optimization jobs in your account and their properties.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_optimization_jobs({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  optimization_contains: "NameContains",
  name_contains: "NameContains",
  status_equals: "INPROGRESS", # accepts INPROGRESS, COMPLETED, FAILED, STARTING, STOPPING, STOPPED
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.optimization_job_summaries #=> Array
resp.optimization_job_summaries[0].optimization_job_name #=> String
resp.optimization_job_summaries[0].optimization_job_arn #=> String
resp.optimization_job_summaries[0].creation_time #=> Time
resp.optimization_job_summaries[0].optimization_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED"
resp.optimization_job_summaries[0].optimization_start_time #=> Time
resp.optimization_job_summaries[0].optimization_end_time #=> Time
resp.optimization_job_summaries[0].last_modified_time #=> Time
resp.optimization_job_summaries[0].deployment_instance_type #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge"
resp.optimization_job_summaries[0].optimization_types #=> Array
resp.optimization_job_summaries[0].optimization_types[0] #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

  • :max_results (Integer) —

    The maximum number of optimization jobs to return in the response. The default is 50.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Filters the results to only those optimization jobs that were created after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Filters the results to only those optimization jobs that were created before the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    Filters the results to only those optimization jobs that were updated after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    Filters the results to only those optimization jobs that were updated before the specified time.

  • :optimization_contains (String) —

    Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either ‘Quantization` or `Compilation`.

  • :name_contains (String) —

    Filters the results to only those optimization jobs with a name that contains the specified string.

  • :status_equals (String) —

    Filters the results to only those optimization jobs with the specified status.

  • :sort_by (String) —

    The field by which to sort the optimization jobs in the response. The default is ‘CreationTime`

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21857

def list_optimization_jobs(params = {}, options = {})
  req = build_request(:list_optimization_jobs, params)
  req.send_request(options)
end

#list_pipeline_execution_steps(params = {}) ⇒ Types::ListPipelineExecutionStepsResponse

Gets a list of ‘PipeLineExecutionStep` objects.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_pipeline_execution_steps({
  pipeline_execution_arn: "PipelineExecutionArn",
  next_token: "NextToken",
  max_results: 1,
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.pipeline_execution_steps #=> Array
resp.pipeline_execution_steps[0].step_name #=> String
resp.pipeline_execution_steps[0].step_display_name #=> String
resp.pipeline_execution_steps[0].step_description #=> String
resp.pipeline_execution_steps[0].start_time #=> Time
resp.pipeline_execution_steps[0].end_time #=> Time
resp.pipeline_execution_steps[0].step_status #=> String, one of "Starting", "Executing", "Stopping", "Stopped", "Failed", "Succeeded"
resp.pipeline_execution_steps[0].cache_hit_result.source_pipeline_execution_arn #=> String
resp.pipeline_execution_steps[0].failure_reason #=> String
resp.pipeline_execution_steps[0].metadata.training_job.arn #=> String
resp.pipeline_execution_steps[0].metadata.processing_job.arn #=> String
resp.pipeline_execution_steps[0].metadata.transform_job.arn #=> String
resp.pipeline_execution_steps[0].metadata.tuning_job.arn #=> String
resp.pipeline_execution_steps[0].metadata.model.arn #=> String
resp.pipeline_execution_steps[0].metadata.register_model.arn #=> String
resp.pipeline_execution_steps[0].metadata.condition.outcome #=> String, one of "True", "False"
resp.pipeline_execution_steps[0].metadata.callback.callback_token #=> String
resp.pipeline_execution_steps[0].metadata.callback.sqs_queue_url #=> String
resp.pipeline_execution_steps[0].metadata.callback.output_parameters #=> Array
resp.pipeline_execution_steps[0].metadata.callback.output_parameters[0].name #=> String
resp.pipeline_execution_steps[0].metadata.callback.output_parameters[0].value #=> String
resp.pipeline_execution_steps[0].metadata.lambda.arn #=> String
resp.pipeline_execution_steps[0].metadata.lambda.output_parameters #=> Array
resp.pipeline_execution_steps[0].metadata.lambda.output_parameters[0].name #=> String
resp.pipeline_execution_steps[0].metadata.lambda.output_parameters[0].value #=> String
resp.pipeline_execution_steps[0].metadata.emr.cluster_id #=> String
resp.pipeline_execution_steps[0].metadata.emr.step_id #=> String
resp.pipeline_execution_steps[0].metadata.emr.step_name #=> String
resp.pipeline_execution_steps[0].metadata.emr.log_file_path #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.check_type #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.baseline_used_for_drift_check_statistics #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.baseline_used_for_drift_check_constraints #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.calculated_baseline_statistics #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.calculated_baseline_constraints #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.model_package_group_name #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.violation_report #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.check_job_arn #=> String
resp.pipeline_execution_steps[0].metadata.quality_check.skip_check #=> Boolean
resp.pipeline_execution_steps[0].metadata.quality_check.register_new_baseline #=> Boolean
resp.pipeline_execution_steps[0].metadata.clarify_check.check_type #=> String
resp.pipeline_execution_steps[0].metadata.clarify_check.baseline_used_for_drift_check_constraints #=> String
resp.pipeline_execution_steps[0].metadata.clarify_check.calculated_baseline_constraints #=> String
resp.pipeline_execution_steps[0].metadata.clarify_check.model_package_group_name #=> String
resp.pipeline_execution_steps[0].metadata.clarify_check.violation_report #=> String
resp.pipeline_execution_steps[0].metadata.clarify_check.check_job_arn #=> String
resp.pipeline_execution_steps[0].metadata.clarify_check.skip_check #=> Boolean
resp.pipeline_execution_steps[0].metadata.clarify_check.register_new_baseline #=> Boolean
resp.pipeline_execution_steps[0].metadata.fail.error_message #=> String
resp.pipeline_execution_steps[0].metadata.auto_ml_job.arn #=> String
resp.pipeline_execution_steps[0].metadata.endpoint.arn #=> String
resp.pipeline_execution_steps[0].metadata.endpoint_config.arn #=> String
resp.pipeline_execution_steps[0].attempt_count #=> Integer
resp.pipeline_execution_steps[0].selective_execution_result.source_pipeline_execution_arn #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

  • :next_token (String) —

    If the result of the previous ‘ListPipelineExecutionSteps` request was truncated, the response includes a `NextToken`. To retrieve the next set of pipeline execution steps, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of pipeline execution steps to return in the response.

  • :sort_order (String) —

    The field by which to sort results. The default is ‘CreatedTime`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 21956

def list_pipeline_execution_steps(params = {}, options = {})
  req = build_request(:list_pipeline_execution_steps, params)
  req.send_request(options)
end

#list_pipeline_executions(params = {}) ⇒ Types::ListPipelineExecutionsResponse

Gets a list of the pipeline executions.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_pipeline_executions({
  pipeline_name: "PipelineNameOrArn", # required
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "CreationTime", # accepts CreationTime, PipelineExecutionArn
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.pipeline_execution_summaries #=> Array
resp.pipeline_execution_summaries[0].pipeline_execution_arn #=> String
resp.pipeline_execution_summaries[0].start_time #=> Time
resp.pipeline_execution_summaries[0].pipeline_execution_status #=> String, one of "Executing", "Stopping", "Stopped", "Failed", "Succeeded"
resp.pipeline_execution_summaries[0].pipeline_execution_description #=> String
resp.pipeline_execution_summaries[0].pipeline_execution_display_name #=> String
resp.pipeline_execution_summaries[0].pipeline_execution_failure_reason #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name (required, String) —

    The name or Amazon Resource Name (ARN) of the pipeline.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns the pipeline executions that were created after a specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns the pipeline executions that were created before a specified time.

  • :sort_by (String) —

    The field by which to sort results. The default is ‘CreatedTime`.

  • :sort_order (String) —

    The sort order for results.

  • :next_token (String) —

    If the result of the previous ‘ListPipelineExecutions` request was truncated, the response includes a `NextToken`. To retrieve the next set of pipeline executions, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of pipeline executions to return in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22022

def list_pipeline_executions(params = {}, options = {})
  req = build_request(:list_pipeline_executions, params)
  req.send_request(options)
end

#list_pipeline_parameters_for_execution(params = {}) ⇒ Types::ListPipelineParametersForExecutionResponse

Gets a list of parameters for a pipeline execution.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_pipeline_parameters_for_execution({
  pipeline_execution_arn: "PipelineExecutionArn", # required
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.pipeline_parameters #=> Array
resp.pipeline_parameters[0].name #=> String
resp.pipeline_parameters[0].value #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (required, String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

  • :next_token (String) —

    If the result of the previous ‘ListPipelineParametersForExecution` request was truncated, the response includes a `NextToken`. To retrieve the next set of parameters, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of parameters to return in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22067

def list_pipeline_parameters_for_execution(params = {}, options = {})
  req = build_request(:list_pipeline_parameters_for_execution, params)
  req.send_request(options)
end

#list_pipelines(params = {}) ⇒ Types::ListPipelinesResponse

Gets a list of pipelines.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_pipelines({
  pipeline_name_prefix: "PipelineName",
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.pipeline_summaries #=> Array
resp.pipeline_summaries[0].pipeline_arn #=> String
resp.pipeline_summaries[0].pipeline_name #=> String
resp.pipeline_summaries[0].pipeline_display_name #=> String
resp.pipeline_summaries[0].pipeline_description #=> String
resp.pipeline_summaries[0].role_arn #=> String
resp.pipeline_summaries[0].creation_time #=> Time
resp.pipeline_summaries[0].last_modified_time #=> Time
resp.pipeline_summaries[0].last_execution_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name_prefix (String) —

    The prefix of the pipeline name.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns the pipelines that were created after a specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns the pipelines that were created before a specified time.

  • :sort_by (String) —

    The field by which to sort results. The default is ‘CreatedTime`.

  • :sort_order (String) —

    The sort order for results.

  • :next_token (String) —

    If the result of the previous ‘ListPipelines` request was truncated, the response includes a `NextToken`. To retrieve the next set of pipelines, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of pipelines to return in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22135

def list_pipelines(params = {}, options = {})
  req = build_request(:list_pipelines, params)
  req.send_request(options)
end

#list_processing_jobs(params = {}) ⇒ Types::ListProcessingJobsResponse

Lists processing jobs that satisfy various filters.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_processing_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "String",
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.processing_job_summaries #=> Array
resp.processing_job_summaries[0].processing_job_name #=> String
resp.processing_job_summaries[0].processing_job_arn #=> String
resp.processing_job_summaries[0].creation_time #=> Time
resp.processing_job_summaries[0].processing_end_time #=> Time
resp.processing_job_summaries[0].last_modified_time #=> Time
resp.processing_job_summaries[0].processing_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.processing_job_summaries[0].failure_reason #=> String
resp.processing_job_summaries[0].exit_message #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only processing jobs created after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only processing jobs created after the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only processing jobs modified after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only processing jobs modified before the specified time.

  • :name_contains (String) —

    A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.

  • :status_equals (String) —

    A filter that retrieves only processing jobs with a specific status.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

  • :next_token (String) —

    If the result of the previous ‘ListProcessingJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of processing jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of processing jobs to return in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22218

def list_processing_jobs(params = {}, options = {})
  req = build_request(:list_processing_jobs, params)
  req.send_request(options)
end

#list_projects(params = {}) ⇒ Types::ListProjectsOutput

Gets a list of the projects in an Amazon Web Services account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_projects({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "ProjectEntityName",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.project_summary_list #=> Array
resp.project_summary_list[0].project_name #=> String
resp.project_summary_list[0].project_description #=> String
resp.project_summary_list[0].project_arn #=> String
resp.project_summary_list[0].project_id #=> String
resp.project_summary_list[0].creation_time #=> Time
resp.project_summary_list[0].project_status #=> String, one of "Pending", "CreateInProgress", "CreateCompleted", "CreateFailed", "DeleteInProgress", "DeleteFailed", "DeleteCompleted", "UpdateInProgress", "UpdateCompleted", "UpdateFailed"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns the projects that were created after a specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns the projects that were created before a specified time.

  • :max_results (Integer) —

    The maximum number of projects to return in the response.

  • :name_contains (String) —

    A filter that returns the projects whose name contains a specified string.

  • :next_token (String) —

    If the result of the previous ‘ListProjects` request was truncated, the response includes a `NextToken`. To retrieve the next set of projects, use the token in the next request.

  • :sort_by (String) —

    The field by which to sort results. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22285

def list_projects(params = {}, options = {})
  req = build_request(:list_projects, params)
  req.send_request(options)
end

#list_resource_catalogs(params = {}) ⇒ Types::ListResourceCatalogsResponse

Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ‘ResourceCatalog`s viewable is 1000.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_resource_catalogs({
  name_contains: "ResourceCatalogName",
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "CreationTime", # accepts CreationTime
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.resource_catalogs #=> Array
resp.resource_catalogs[0].resource_catalog_arn #=> String
resp.resource_catalogs[0].resource_catalog_name #=> String
resp.resource_catalogs[0].description #=> String
resp.resource_catalogs[0].creation_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name_contains (String) —

    A string that partially matches one or more ‘ResourceCatalog`s names. Filters `ResourceCatalog` by name.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    Use this parameter to search for ‘ResourceCatalog`s created after a specific date and time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    Use this parameter to search for ‘ResourceCatalog`s created before a specific date and time.

  • :sort_order (String) —

    The order in which the resource catalogs are listed.

  • :sort_by (String) —

    The value on which the resource catalog list is sorted.

  • :max_results (Integer) —

    The maximum number of results returned by ‘ListResourceCatalogs`.

  • :next_token (String) —

    A token to resume pagination of ‘ListResourceCatalogs` results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22349

def list_resource_catalogs(params = {}, options = {})
  req = build_request(:list_resource_catalogs, params)
  req.send_request(options)
end

#list_spaces(params = {}) ⇒ Types::ListSpacesResponse

Lists spaces.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_spaces({
  next_token: "NextToken",
  max_results: 1,
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime
  domain_id_equals: "DomainId",
  space_name_contains: "SpaceName",
})

Response structure


resp.spaces #=> Array
resp.spaces[0].domain_id #=> String
resp.spaces[0].space_name #=> String
resp.spaces[0].status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed"
resp.spaces[0].creation_time #=> Time
resp.spaces[0].last_modified_time #=> Time
resp.spaces[0].space_settings_summary.app_type #=> String, one of "JupyterServer", "KernelGateway", "DetailedProfiler", "TensorBoard", "CodeEditor", "JupyterLab", "RStudioServerPro", "RSessionGateway", "Canvas"
resp.spaces[0].space_settings_summary.space_storage_settings.ebs_storage_settings.ebs_volume_size_in_gb #=> Integer
resp.spaces[0].space_sharing_settings_summary.sharing_type #=> String, one of "Private", "Shared"
resp.spaces[0].ownership_settings_summary. #=> String
resp.spaces[0].space_display_name #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

  • :max_results (Integer) —

    This parameter defines the maximum number of results that can be return in a single response. The ‘MaxResults` parameter is an upper bound, not a target. If there are more results available than the value specified, a `NextToken` is provided in the response. The `NextToken` indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for `MaxResults` is 10.

  • :sort_order (String) —

    The sort order for the results. The default is ‘Ascending`.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is ‘CreationTime`.

  • :domain_id_equals (String) —

    A parameter to search for the domain ID.

  • :space_name_contains (String) —

    A parameter by which to filter the results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22419

def list_spaces(params = {}, options = {})
  req = build_request(:list_spaces, params)
  req.send_request(options)
end

#list_stage_devices(params = {}) ⇒ Types::ListStageDevicesResponse

Lists devices allocated to the stage, containing detailed device information and deployment status.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_stage_devices({
  next_token: "NextToken",
  max_results: 1,
  edge_deployment_plan_name: "EntityName", # required
  exclude_devices_deployed_in_other_stage: false,
  stage_name: "EntityName", # required
})

Response structure


resp.device_deployment_summaries #=> Array
resp.device_deployment_summaries[0].edge_deployment_plan_arn #=> String
resp.device_deployment_summaries[0].edge_deployment_plan_name #=> String
resp.device_deployment_summaries[0].stage_name #=> String
resp.device_deployment_summaries[0].deployed_stage_name #=> String
resp.device_deployment_summaries[0].device_fleet_name #=> String
resp.device_deployment_summaries[0].device_name #=> String
resp.device_deployment_summaries[0].device_arn #=> String
resp.device_deployment_summaries[0].device_deployment_status #=> String, one of "READYTODEPLOY", "INPROGRESS", "DEPLOYED", "FAILED", "STOPPING", "STOPPED"
resp.device_deployment_summaries[0].device_deployment_status_message #=> String
resp.device_deployment_summaries[0].description #=> String
resp.device_deployment_summaries[0].deployment_start_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    The response from the last list when returning a list large enough to neeed tokening.

  • :max_results (Integer) —

    The maximum number of requests to select.

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan.

  • :exclude_devices_deployed_in_other_stage (Boolean) —

    Toggle for excluding devices deployed in other stages.

  • :stage_name (required, String) —

    The name of the stage in the deployment.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22480

def list_stage_devices(params = {}, options = {})
  req = build_request(:list_stage_devices, params)
  req.send_request(options)
end

#list_studio_lifecycle_configs(params = {}) ⇒ Types::ListStudioLifecycleConfigsResponse

Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_studio_lifecycle_configs({
  max_results: 1,
  next_token: "NextToken",
  name_contains: "StudioLifecycleConfigName",
  app_type_equals: "JupyterServer", # accepts JupyterServer, KernelGateway, CodeEditor, JupyterLab
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  modified_time_before: Time.now,
  modified_time_after: Time.now,
  sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime, Name
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.next_token #=> String
resp.studio_lifecycle_configs #=> Array
resp.studio_lifecycle_configs[0].studio_lifecycle_config_arn #=> String
resp.studio_lifecycle_configs[0].studio_lifecycle_config_name #=> String
resp.studio_lifecycle_configs[0].creation_time #=> Time
resp.studio_lifecycle_configs[0].last_modified_time #=> Time
resp.studio_lifecycle_configs[0].studio_lifecycle_config_app_type #=> String, one of "JupyterServer", "KernelGateway", "CodeEditor", "JupyterLab"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :max_results (Integer) —

    The total number of items to return in the response. If the total number of items available is more than the value specified, a ‘NextToken` is provided in the response. To resume pagination, provide the `NextToken` value in the as part of a subsequent call. The default value is 10.

  • :next_token (String) —

    If the previous call to ListStudioLifecycleConfigs didn’t return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.

  • :name_contains (String) —

    A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.

  • :app_type_equals (String) —

    A parameter to search for the App Type to which the Lifecycle Configuration is attached.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only Lifecycle Configurations created on or before the specified time.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only Lifecycle Configurations created on or after the specified time.

  • :modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only Lifecycle Configurations modified before the specified time.

  • :modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only Lifecycle Configurations modified after the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is CreationTime.

  • :sort_order (String) —

    The sort order. The default value is Descending.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22566

def list_studio_lifecycle_configs(params = {}, options = {})
  req = build_request(:list_studio_lifecycle_configs, params)
  req.send_request(options)
end

#list_subscribed_workteams(params = {}) ⇒ Types::ListSubscribedWorkteamsResponse

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the ‘NameContains` parameter.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_subscribed_workteams({
  name_contains: "WorkteamName",
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.subscribed_workteams #=> Array
resp.subscribed_workteams[0].workteam_arn #=> String
resp.subscribed_workteams[0].marketplace_title #=> String
resp.subscribed_workteams[0].seller_name #=> String
resp.subscribed_workteams[0].marketplace_description #=> String
resp.subscribed_workteams[0].listing_id #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name_contains (String) —

    A string in the work team name. This filter returns only work teams whose name contains the specified string.

  • :next_token (String) —

    If the result of the previous ‘ListSubscribedWorkteams` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of work teams to return in each page of the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22617

def list_subscribed_workteams(params = {}, options = {})
  req = build_request(:list_subscribed_workteams, params)
  req.send_request(options)
end

#list_tags(params = {}) ⇒ Types::ListTagsOutput

Returns the tags for the specified SageMaker resource.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_tags({
  resource_arn: "ResourceArn", # required
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :resource_arn (required, String) —

    The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

  • :next_token (String) —

    If the response to the previous ‘ListTags` request is truncated, SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

  • :max_results (Integer) —

    Maximum number of tags to return.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22662

def list_tags(params = {}, options = {})
  req = build_request(:list_tags, params)
  req.send_request(options)
end

#list_training_jobs(params = {}) ⇒ Types::ListTrainingJobsResponse

Lists training jobs.

<note markdown=“1”> When ‘StatusEquals` and `MaxResults` are set at the same time, the `MaxResults` number of training jobs are first retrieved ignoring the `StatusEquals` parameter and then they are filtered by the `StatusEquals` parameter, which is returned as a response.

For example, if `ListTrainingJobs` is invoked with the following

parameters:

`\{ ... MaxResults: 100, StatusEquals: InProgress ... \}`

First, 100 trainings jobs with any status, including those other than

‘InProgress`, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of `InProgress` are returned.

You can quickly test the API using the following Amazon Web Services

CLI code.

`aws sagemaker list-training-jobs --max-results 100 --status-equals

InProgress`

</note>

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_training_jobs({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  warm_pool_status_equals: "Available", # accepts Available, Terminated, Reused, InUse
})

Response structure


resp.training_job_summaries #=> Array
resp.training_job_summaries[0].training_job_name #=> String
resp.training_job_summaries[0].training_job_arn #=> String
resp.training_job_summaries[0].creation_time #=> Time
resp.training_job_summaries[0].training_end_time #=> Time
resp.training_job_summaries[0].last_modified_time #=> Time
resp.training_job_summaries[0].training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.training_job_summaries[0].warm_pool_status.status #=> String, one of "Available", "Terminated", "Reused", "InUse"
resp.training_job_summaries[0].warm_pool_status.resource_retained_billable_time_in_seconds #=> Integer
resp.training_job_summaries[0].warm_pool_status.reused_by_job #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the result of the previous ‘ListTrainingJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of training jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of training jobs to return in the response.

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only training jobs created after the specified time (timestamp).

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only training jobs created before the specified time (timestamp).

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only training jobs modified after the specified time (timestamp).

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only training jobs modified before the specified time (timestamp).

  • :name_contains (String) —

    A string in the training job name. This filter returns only training jobs whose name contains the specified string.

  • :status_equals (String) —

    A filter that retrieves only training jobs with a specific status.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

  • :warm_pool_status_equals (String) —

    A filter that retrieves only training jobs with a specific warm pool status.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22774

def list_training_jobs(params = {}, options = {})
  req = build_request(:list_training_jobs, params)
  req.send_request(options)
end

#list_training_jobs_for_hyper_parameter_tuning_job(params = {}) ⇒ Types::ListTrainingJobsForHyperParameterTuningJobResponse

Gets a list of [TrainingJobSummary] objects that describe the training jobs that a hyperparameter tuning job launched.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobSummary.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_training_jobs_for_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
  next_token: "NextToken",
  max_results: 1,
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status, FinalObjectiveMetricValue
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.training_job_summaries #=> Array
resp.training_job_summaries[0].training_job_definition_name #=> String
resp.training_job_summaries[0].training_job_name #=> String
resp.training_job_summaries[0].training_job_arn #=> String
resp.training_job_summaries[0].tuning_job_name #=> String
resp.training_job_summaries[0].creation_time #=> Time
resp.training_job_summaries[0].training_start_time #=> Time
resp.training_job_summaries[0].training_end_time #=> Time
resp.training_job_summaries[0].training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.training_job_summaries[0].tuned_hyper_parameters #=> Hash
resp.training_job_summaries[0].tuned_hyper_parameters["HyperParameterKey"] #=> String
resp.training_job_summaries[0].failure_reason #=> String
resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String
resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.value #=> Float
resp.training_job_summaries[0].objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hyper_parameter_tuning_job_name (required, String) —

    The name of the tuning job whose training jobs you want to list.

  • :next_token (String) —

    If the result of the previous ‘ListTrainingJobsForHyperParameterTuningJob` request was truncated, the response includes a `NextToken`. To retrieve the next set of training jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of training jobs to return. The default value is 10.

  • :status_equals (String) —

    A filter that returns only training jobs with the specified status.

  • :sort_by (String) —

    The field to sort results by. The default is ‘Name`.

    If the value of this field is ‘FinalObjectiveMetricValue`, any training jobs that did not return an objective metric are not listed.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22853

def list_training_jobs_for_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:list_training_jobs_for_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#list_transform_jobs(params = {}) ⇒ Types::ListTransformJobsResponse

Lists transform jobs.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_transform_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.transform_job_summaries #=> Array
resp.transform_job_summaries[0].transform_job_name #=> String
resp.transform_job_summaries[0].transform_job_arn #=> String
resp.transform_job_summaries[0].creation_time #=> Time
resp.transform_job_summaries[0].transform_end_time #=> Time
resp.transform_job_summaries[0].last_modified_time #=> Time
resp.transform_job_summaries[0].transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.transform_job_summaries[0].failure_reason #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :creation_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only transform jobs created after the specified time.

  • :creation_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only transform jobs created before the specified time.

  • :last_modified_time_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only transform jobs modified after the specified time.

  • :last_modified_time_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only transform jobs modified before the specified time.

  • :name_contains (String) —

    A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

  • :status_equals (String) —

    A filter that retrieves only transform jobs with a specific status.

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Descending`.

  • :next_token (String) —

    If the result of the previous ‘ListTransformJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of transform jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of transform jobs to return in the response. The default value is ‘10`.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 22936

def list_transform_jobs(params = {}, options = {})
  req = build_request(:list_transform_jobs, params)
  req.send_request(options)
end

#list_trial_components(params = {}) ⇒ Types::ListTrialComponentsResponse

Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:

  • ‘ExperimentName`

  • ‘SourceArn`

  • ‘TrialName`

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_trial_components({
  experiment_name: "ExperimentEntityName",
  trial_name: "ExperimentEntityName",
  source_arn: "String256",
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.trial_component_summaries #=> Array
resp.trial_component_summaries[0].trial_component_name #=> String
resp.trial_component_summaries[0].trial_component_arn #=> String
resp.trial_component_summaries[0].display_name #=> String
resp.trial_component_summaries[0].trial_component_source.source_arn #=> String
resp.trial_component_summaries[0].trial_component_source.source_type #=> String
resp.trial_component_summaries[0].status.primary_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.trial_component_summaries[0].status.message #=> String
resp.trial_component_summaries[0].start_time #=> Time
resp.trial_component_summaries[0].end_time #=> Time
resp.trial_component_summaries[0].creation_time #=> Time
resp.trial_component_summaries[0].created_by. #=> String
resp.trial_component_summaries[0].created_by. #=> String
resp.trial_component_summaries[0].created_by.domain_id #=> String
resp.trial_component_summaries[0].created_by.iam_identity.arn #=> String
resp.trial_component_summaries[0].created_by.iam_identity.principal_id #=> String
resp.trial_component_summaries[0].created_by.iam_identity.source_identity #=> String
resp.trial_component_summaries[0].last_modified_time #=> Time
resp.trial_component_summaries[0].last_modified_by. #=> String
resp.trial_component_summaries[0].last_modified_by. #=> String
resp.trial_component_summaries[0].last_modified_by.domain_id #=> String
resp.trial_component_summaries[0].last_modified_by.iam_identity.arn #=> String
resp.trial_component_summaries[0].last_modified_by.iam_identity.principal_id #=> String
resp.trial_component_summaries[0].last_modified_by.iam_identity.source_identity #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (String) —

    A filter that returns only components that are part of the specified experiment. If you specify ‘ExperimentName`, you can’t filter by ‘SourceArn` or `TrialName`.

  • :trial_name (String) —

    A filter that returns only components that are part of the specified trial. If you specify ‘TrialName`, you can’t filter by ‘ExperimentName` or `SourceArn`.

  • :source_arn (String) —

    A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify ‘SourceArn`, you can’t filter by ‘ExperimentName` or `TrialName`.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only components created after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only components created before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :max_results (Integer) —

    The maximum number of components to return in the response. The default value is 10.

  • :next_token (String) —

    If the previous call to ‘ListTrialComponents` didn’t return the full set of components, the call returns a token for getting the next set of components.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23044

def list_trial_components(params = {}, options = {})
  req = build_request(:list_trial_components, params)
  req.send_request(options)
end

#list_trials(params = {}) ⇒ Types::ListTrialsResponse

Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_trials({
  experiment_name: "ExperimentEntityName",
  trial_component_name: "ExperimentEntityName",
  created_after: Time.now,
  created_before: Time.now,
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  max_results: 1,
  next_token: "NextToken",
})

Response structure


resp.trial_summaries #=> Array
resp.trial_summaries[0].trial_arn #=> String
resp.trial_summaries[0].trial_name #=> String
resp.trial_summaries[0].display_name #=> String
resp.trial_summaries[0].trial_source.source_arn #=> String
resp.trial_summaries[0].trial_source.source_type #=> String
resp.trial_summaries[0].creation_time #=> Time
resp.trial_summaries[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (String) —

    A filter that returns only trials that are part of the specified experiment.

  • :trial_component_name (String) —

    A filter that returns only trials that are associated with the specified trial component.

  • :created_after (Time, DateTime, Date, Integer, String) —

    A filter that returns only trials created after the specified time.

  • :created_before (Time, DateTime, Date, Integer, String) —

    A filter that returns only trials created before the specified time.

  • :sort_by (String) —

    The property used to sort results. The default value is ‘CreationTime`.

  • :sort_order (String) —

    The sort order. The default value is ‘Descending`.

  • :max_results (Integer) —

    The maximum number of trials to return in the response. The default value is 10.

  • :next_token (String) —

    If the previous call to ‘ListTrials` didn’t return the full set of trials, the call returns a token for getting the next set of trials.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23121

def list_trials(params = {}, options = {})
  req = build_request(:list_trials, params)
  req.send_request(options)
end

#list_user_profiles(params = {}) ⇒ Types::ListUserProfilesResponse

Lists user profiles.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_user_profiles({
  next_token: "NextToken",
  max_results: 1,
  sort_order: "Ascending", # accepts Ascending, Descending
  sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime
  domain_id_equals: "DomainId",
  user_profile_name_contains: "UserProfileName",
})

Response structure


resp.user_profiles #=> Array
resp.user_profiles[0].domain_id #=> String
resp.user_profiles[0]. #=> String
resp.user_profiles[0].status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed"
resp.user_profiles[0].creation_time #=> Time
resp.user_profiles[0].last_modified_time #=> Time
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :next_token (String) —

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

  • :max_results (Integer) —

    This parameter defines the maximum number of results that can be return in a single response. The ‘MaxResults` parameter is an upper bound, not a target. If there are more results available than the value specified, a `NextToken` is provided in the response. The `NextToken` indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for `MaxResults` is 10.

  • :sort_order (String) —

    The sort order for the results. The default is Ascending.

  • :sort_by (String) —

    The parameter by which to sort the results. The default is CreationTime.

  • :domain_id_equals (String) —

    A parameter by which to filter the results.

  • :user_profile_name_contains (String) —

    A parameter by which to filter the results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23186

def list_user_profiles(params = {}, options = {})
  req = build_request(:list_user_profiles, params)
  req.send_request(options)
end

#list_workforces(params = {}) ⇒ Types::ListWorkforcesResponse

Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_workforces({
  sort_by: "Name", # accepts Name, CreateDate
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "WorkforceName",
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.workforces #=> Array
resp.workforces[0].workforce_name #=> String
resp.workforces[0].workforce_arn #=> String
resp.workforces[0].last_updated_date #=> Time
resp.workforces[0].source_ip_config.cidrs #=> Array
resp.workforces[0].source_ip_config.cidrs[0] #=> String
resp.workforces[0].sub_domain #=> String
resp.workforces[0].cognito_config.user_pool #=> String
resp.workforces[0].cognito_config.client_id #=> String
resp.workforces[0].oidc_config.client_id #=> String
resp.workforces[0].oidc_config.issuer #=> String
resp.workforces[0].oidc_config.authorization_endpoint #=> String
resp.workforces[0].oidc_config.token_endpoint #=> String
resp.workforces[0].oidc_config. #=> String
resp.workforces[0].oidc_config.logout_endpoint #=> String
resp.workforces[0].oidc_config.jwks_uri #=> String
resp.workforces[0].oidc_config.scope #=> String
resp.workforces[0].oidc_config.authentication_request_extra_params #=> Hash
resp.workforces[0].oidc_config.authentication_request_extra_params["AuthenticationRequestExtraParamsKey"] #=> String
resp.workforces[0].create_date #=> Time
resp.workforces[0].workforce_vpc_config.vpc_id #=> String
resp.workforces[0].workforce_vpc_config.security_group_ids #=> Array
resp.workforces[0].workforce_vpc_config.security_group_ids[0] #=> String
resp.workforces[0].workforce_vpc_config.subnets #=> Array
resp.workforces[0].workforce_vpc_config.subnets[0] #=> String
resp.workforces[0].workforce_vpc_config.vpc_endpoint_id #=> String
resp.workforces[0].status #=> String, one of "Initializing", "Updating", "Deleting", "Failed", "Active"
resp.workforces[0].failure_reason #=> String
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :sort_by (String) —

    Sort workforces using the workforce name or creation date.

  • :sort_order (String) —

    Sort workforces in ascending or descending order.

  • :name_contains (String) —

    A filter you can use to search for workforces using part of the workforce name.

  • :next_token (String) —

    A token to resume pagination.

  • :max_results (Integer) —

    The maximum number of workforces returned in the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23264

def list_workforces(params = {}, options = {})
  req = build_request(:list_workforces, params)
  req.send_request(options)
end

#list_workteams(params = {}) ⇒ Types::ListWorkteamsResponse

Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the ‘NameContains` parameter.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.list_workteams({
  sort_by: "Name", # accepts Name, CreateDate
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "WorkteamName",
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.workteams #=> Array
resp.workteams[0].workteam_name #=> String
resp.workteams[0].member_definitions #=> Array
resp.workteams[0].member_definitions[0].cognito_member_definition.user_pool #=> String
resp.workteams[0].member_definitions[0].cognito_member_definition.user_group #=> String
resp.workteams[0].member_definitions[0].cognito_member_definition.client_id #=> String
resp.workteams[0].member_definitions[0].oidc_member_definition.groups #=> Array
resp.workteams[0].member_definitions[0].oidc_member_definition.groups[0] #=> String
resp.workteams[0].workteam_arn #=> String
resp.workteams[0].workforce_arn #=> String
resp.workteams[0].product_listing_ids #=> Array
resp.workteams[0].product_listing_ids[0] #=> String
resp.workteams[0].description #=> String
resp.workteams[0].sub_domain #=> String
resp.workteams[0].create_date #=> Time
resp.workteams[0].last_updated_date #=> Time
resp.workteams[0].notification_configuration.notification_topic_arn #=> String
resp.workteams[0].worker_access_configuration.s3_presign.iam_policy_constraints.source_ip #=> String, one of "Enabled", "Disabled"
resp.workteams[0].worker_access_configuration.s3_presign.iam_policy_constraints.vpc_source_ip #=> String, one of "Enabled", "Disabled"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :sort_by (String) —

    The field to sort results by. The default is ‘CreationTime`.

  • :sort_order (String) —

    The sort order for results. The default is ‘Ascending`.

  • :name_contains (String) —

    A string in the work team’s name. This filter returns only work teams whose name contains the specified string.

  • :next_token (String) —

    If the result of the previous ‘ListWorkteams` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.

  • :max_results (Integer) —

    The maximum number of work teams to return in each page of the response.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23336

def list_workteams(params = {}, options = {})
  req = build_request(:list_workteams, params)
  req.send_request(options)
end

#put_model_package_group_policy(params = {}) ⇒ Types::PutModelPackageGroupPolicyOutput

Adds a resouce policy to control access to a model group. For information about resoure policies, see [Identity-based policies and resource-based policies] in the *Amazon Web Services Identity and Access Management User Guide.*.

[1]: docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html

Examples:

Request syntax with placeholder values


resp = client.put_model_package_group_policy({
  model_package_group_name: "EntityName", # required
  resource_policy: "PolicyString", # required
})

Response structure


resp.model_package_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_group_name (required, String) —

    The name of the model group to add a resource policy to.

  • :resource_policy (required, String) —

    The resource policy for the model group.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23375

def put_model_package_group_policy(params = {}, options = {})
  req = build_request(:put_model_package_group_policy, params)
  req.send_request(options)
end

#query_lineage(params = {}) ⇒ Types::QueryLineageResponse

Use this action to inspect your lineage and discover relationships between entities. For more information, see [ Querying Lineage Entities] in the *Amazon SageMaker Developer Guide*.

[1]: docs.aws.amazon.com/sagemaker/latest/dg/querying-lineage-entities.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.query_lineage({
  start_arns: ["AssociationEntityArn"],
  direction: "Both", # accepts Both, Ascendants, Descendants
  include_edges: false,
  filters: {
    types: ["String40"],
    lineage_types: ["TrialComponent"], # accepts TrialComponent, Artifact, Context, Action
    created_before: Time.now,
    created_after: Time.now,
    modified_before: Time.now,
    modified_after: Time.now,
    properties: {
      "String256" => "String256",
    },
  },
  max_depth: 1,
  max_results: 1,
  next_token: "String8192",
})

Response structure


resp.vertices #=> Array
resp.vertices[0].arn #=> String
resp.vertices[0].type #=> String
resp.vertices[0].lineage_type #=> String, one of "TrialComponent", "Artifact", "Context", "Action"
resp.edges #=> Array
resp.edges[0].source_arn #=> String
resp.edges[0].destination_arn #=> String
resp.edges[0].association_type #=> String, one of "ContributedTo", "AssociatedWith", "DerivedFrom", "Produced", "SameAs"
resp.next_token #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :start_arns (Array<String>) —

    A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.

  • :direction (String) —

    Associations between lineage entities have a direction. This parameter determines the direction from the StartArn(s) that the query traverses.

  • :include_edges (Boolean) —

    Setting this value to ‘True` retrieves not only the entities of interest but also the [Associations] and lineage entities on the path. Set to `False` to only return lineage entities that match your query.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking-entities.html

  • :filters (Types::QueryFilters) —

    A set of filtering parameters that allow you to specify which entities should be returned.

    • Properties - Key-value pairs to match on the lineage entities’ properties.

    • LineageTypes - A set of lineage entity types to match on. For example: ‘TrialComponent`, `Artifact`, or `Context`.

    • CreatedBefore - Filter entities created before this date.

    • ModifiedBefore - Filter entities modified before this date.

    • ModifiedAfter - Filter entities modified after this date.

  • :max_depth (Integer) —

    The maximum depth in lineage relationships from the ‘StartArns` that are traversed. Depth is a measure of the number of `Associations` from the `StartArn` entity to the matched results.

  • :max_results (Integer) —

    Limits the number of vertices in the results. Use the ‘NextToken` in a response to to retrieve the next page of results.

  • :next_token (String) —

    Limits the number of vertices in the request. Use the ‘NextToken` in a response to to retrieve the next page of results.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23482

def query_lineage(params = {}, options = {})
  req = build_request(:query_lineage, params)
  req.send_request(options)
end

#register_devices(params = {}) ⇒ Struct

Register devices.

Examples:

Request syntax with placeholder values


resp = client.register_devices({
  device_fleet_name: "EntityName", # required
  devices: [ # required
    {
      device_name: "DeviceName", # required
      description: "DeviceDescription",
      iot_thing_name: "ThingName",
    },
  ],
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet.

  • :devices (required, Array<Types::Device>) —

    A list of devices to register with SageMaker Edge Manager.

  • :tags (Array<Types::Tag>) —

    The tags associated with devices.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23523

def register_devices(params = {}, options = {})
  req = build_request(:register_devices, params)
  req.send_request(options)
end

#render_ui_template(params = {}) ⇒ Types::RenderUiTemplateResponse

Renders the UI template so that you can preview the worker’s experience.

Examples:

Request syntax with placeholder values


resp = client.render_ui_template({
  ui_template: {
    content: "TemplateContent", # required
  },
  task: { # required
    input: "TaskInput", # required
  },
  role_arn: "RoleArn", # required
  human_task_ui_arn: "HumanTaskUiArn",
})

Response structure


resp.rendered_content #=> String
resp.errors #=> Array
resp.errors[0].code #=> String
resp.errors[0].message #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :ui_template (Types::UiTemplate) —

    A ‘Template` object containing the worker UI template to render.

  • :task (required, Types::RenderableTask) —

    A ‘RenderableTask` object containing a representative task to render.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.

  • :human_task_ui_arn (String) —

    The ‘HumanTaskUiArn` of the worker UI that you want to render. Do not provide a `HumanTaskUiArn` if you use the `UiTemplate` parameter.

    See a list of available Human Ui Amazon Resource Names (ARNs) in [UiConfig].

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_UiConfig.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23581

def render_ui_template(params = {}, options = {})
  req = build_request(:render_ui_template, params)
  req.send_request(options)
end

#retry_pipeline_execution(params = {}) ⇒ Types::RetryPipelineExecutionResponse

Retry the execution of the pipeline.

Examples:

Request syntax with placeholder values


resp = client.retry_pipeline_execution({
  pipeline_execution_arn: "PipelineExecutionArn", # required
  client_request_token: "IdempotencyToken", # required
  parallelism_configuration: {
    max_parallel_execution_steps: 1, # required
  },
})

Response structure


resp.pipeline_execution_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (required, String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

  • :client_request_token (required, String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

  • :parallelism_configuration (Types::ParallelismConfiguration) —

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23625

def retry_pipeline_execution(params = {}, options = {})
  req = build_request(:retry_pipeline_execution, params)
  req.send_request(options)
end

#search(params = {}) ⇒ Types::SearchResponse

Finds SageMaker resources that match a search query. Matching resources are returned as a list of ‘SearchRecord` objects in the response. You can sort the search results by any resource property in a ascending or descending order.

You can query against the following value types: numeric, text, Boolean, and timestamp.

<note markdown=“1”> The Search API may provide access to otherwise restricted data. See [Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference] for more information.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/dg/api-permissions-reference.html

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

Examples:

Request syntax with placeholder values


resp = client.search({
  resource: "TrainingJob", # required, accepts TrainingJob, Experiment, ExperimentTrial, ExperimentTrialComponent, Endpoint, Model, ModelPackage, ModelPackageGroup, Pipeline, PipelineExecution, FeatureGroup, FeatureMetadata, Image, ImageVersion, Project, HyperParameterTuningJob, ModelCard
  search_expression: {
    filters: [
      {
        name: "ResourcePropertyName", # required
        operator: "Equals", # accepts Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains, Exists, NotExists, In
        value: "FilterValue",
      },
    ],
    nested_filters: [
      {
        nested_property_name: "ResourcePropertyName", # required
        filters: [ # required
          {
            name: "ResourcePropertyName", # required
            operator: "Equals", # accepts Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains, Exists, NotExists, In
            value: "FilterValue",
          },
        ],
      },
    ],
    sub_expressions: [
      {
        # recursive SearchExpression
      },
    ],
    operator: "And", # accepts And, Or
  },
  sort_by: "ResourcePropertyName",
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
  cross_account_filter_option: "SameAccount", # accepts SameAccount, CrossAccount
  visibility_conditions: [
    {
      key: "VisibilityConditionsKey",
      value: "VisibilityConditionsValue",
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :resource (required, String) —

    The name of the SageMaker resource to search for.

  • :search_expression (Types::SearchExpression) —

    A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive ‘SubExpressions`, `NestedFilters`, and `Filters` that can be included in a `SearchExpression` object is 50.

  • :sort_by (String) —

    The name of the resource property used to sort the ‘SearchResults`. The default is `LastModifiedTime`.

  • :sort_order (String) —

    How ‘SearchResults` are ordered. Valid values are `Ascending` or `Descending`. The default is `Descending`.

  • :next_token (String) —

    If more than ‘MaxResults` resources match the specified `SearchExpression`, the response includes a `NextToken`. The `NextToken` can be passed to the next `SearchRequest` to continue retrieving results.

  • :max_results (Integer) —

    The maximum number of results to return.

  • :cross_account_filter_option (String) —

    A cross account filter option. When the value is ‘“CrossAccount”` the search results will only include resources made discoverable to you from other accounts. When the value is `“SameAccount”` or `null` the search results will only include resources from your account. Default is `null`. For more information on searching for resources made discoverable to your account, see [ Search discoverable resources] in the SageMaker Developer Guide. The maximum number of `ResourceCatalog`s viewable is 1000.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/feature-store-cross-account-discoverability-use.html

  • :visibility_conditions (Array<Types::VisibilityConditions>) —

    Limits the results of your search request to the resources that you can access.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23748

def search(params = {}, options = {})
  req = build_request(:search, params)
  req.send_request(options)
end

#send_pipeline_execution_step_failure(params = {}) ⇒ Types::SendPipelineExecutionStepFailureResponse

Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Examples:

Request syntax with placeholder values


resp = client.send_pipeline_execution_step_failure({
  callback_token: "CallbackToken", # required
  failure_reason: "String256",
  client_request_token: "IdempotencyToken",
})

Response structure


resp.pipeline_execution_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :callback_token (required, String) —

    The pipeline generated token from the Amazon SQS queue.

  • :failure_reason (String) —

    A message describing why the step failed.

  • :client_request_token (String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23792

def send_pipeline_execution_step_failure(params = {}, options = {})
  req = build_request(:send_pipeline_execution_step_failure, params)
  req.send_request(options)
end

#send_pipeline_execution_step_success(params = {}) ⇒ Types::SendPipelineExecutionStepSuccessResponse

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step’s output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Examples:

Request syntax with placeholder values


resp = client.send_pipeline_execution_step_success({
  callback_token: "CallbackToken", # required
  output_parameters: [
    {
      name: "String256", # required
      value: "String1024", # required
    },
  ],
  client_request_token: "IdempotencyToken",
})

Response structure


resp.pipeline_execution_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :callback_token (required, String) —

    The pipeline generated token from the Amazon SQS queue.

  • :output_parameters (Array<Types::OutputParameter>) —

    A list of the output parameters of the callback step.

  • :client_request_token (String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23841

def send_pipeline_execution_step_success(params = {}, options = {})
  req = build_request(:send_pipeline_execution_step_success, params)
  req.send_request(options)
end

#start_edge_deployment_stage(params = {}) ⇒ Struct

Starts a stage in an edge deployment plan.

Examples:

Request syntax with placeholder values


resp = client.start_edge_deployment_stage({
  edge_deployment_plan_name: "EntityName", # required
  stage_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan to start.

  • :stage_name (required, String) —

    The name of the stage to start.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23867

def start_edge_deployment_stage(params = {}, options = {})
  req = build_request(:start_edge_deployment_stage, params)
  req.send_request(options)
end

#start_inference_experiment(params = {}) ⇒ Types::StartInferenceExperimentResponse

Starts an inference experiment.

Examples:

Request syntax with placeholder values


resp = client.start_inference_experiment({
  name: "InferenceExperimentName", # required
})

Response structure


resp.inference_experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the inference experiment to start.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23895

def start_inference_experiment(params = {}, options = {})
  req = build_request(:start_inference_experiment, params)
  req.send_request(options)
end

#start_mlflow_tracking_server(params = {}) ⇒ Types::StartMlflowTrackingServerResponse

Programmatically start an MLflow Tracking Server.

Examples:

Request syntax with placeholder values


resp = client.start_mlflow_tracking_server({
  tracking_server_name: "TrackingServerName", # required
})

Response structure


resp.tracking_server_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    The name of the tracking server to start.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23923

def start_mlflow_tracking_server(params = {}, options = {})
  req = build_request(:start_mlflow_tracking_server, params)
  req.send_request(options)
end

#start_monitoring_schedule(params = {}) ⇒ Struct

Starts a previously stopped monitoring schedule.

<note markdown=“1”> By default, when you successfully create a new schedule, the status of a monitoring schedule is ‘scheduled`.

</note>

Examples:

Request syntax with placeholder values


resp = client.start_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of the schedule to start.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23950

def start_monitoring_schedule(params = {}, options = {})
  req = build_request(:start_monitoring_schedule, params)
  req.send_request(options)
end

#start_notebook_instance(params = {}) ⇒ Struct

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to ‘InService`. A notebook instance’s status must be ‘InService` before you can connect to your Jupyter notebook.

Examples:

Request syntax with placeholder values


resp = client.start_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the notebook instance to start.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 23976

def start_notebook_instance(params = {}, options = {})
  req = build_request(:start_notebook_instance, params)
  req.send_request(options)
end

#start_pipeline_execution(params = {}) ⇒ Types::StartPipelineExecutionResponse

Starts a pipeline execution.

Examples:

Request syntax with placeholder values


resp = client.start_pipeline_execution({
  pipeline_name: "PipelineNameOrArn", # required
  pipeline_execution_display_name: "PipelineExecutionName",
  pipeline_parameters: [
    {
      name: "PipelineParameterName", # required
      value: "String1024", # required
    },
  ],
  pipeline_execution_description: "PipelineExecutionDescription",
  client_request_token: "IdempotencyToken", # required
  parallelism_configuration: {
    max_parallel_execution_steps: 1, # required
  },
  selective_execution_config: {
    source_pipeline_execution_arn: "PipelineExecutionArn",
    selected_steps: [ # required
      {
        step_name: "String256", # required
      },
    ],
  },
})

Response structure


resp.pipeline_execution_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name (required, String) —

    The name or Amazon Resource Name (ARN) of the pipeline.

  • :pipeline_execution_display_name (String) —

    The display name of the pipeline execution.

  • :pipeline_parameters (Array<Types::Parameter>) —

    Contains a list of pipeline parameters. This list can be empty.

  • :pipeline_execution_description (String) —

    The description of the pipeline execution.

  • :client_request_token (required, String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

  • :parallelism_configuration (Types::ParallelismConfiguration) —

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.

  • :selective_execution_config (Types::SelectiveExecutionConfig) —

    The selective execution configuration applied to the pipeline run.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24048

def start_pipeline_execution(params = {}, options = {})
  req = build_request(:start_pipeline_execution, params)
  req.send_request(options)
end

#stop_auto_ml_job(params = {}) ⇒ Struct

A method for forcing a running job to shut down.

Examples:

Request syntax with placeholder values


resp = client.stop_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    The name of the object you are requesting.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24070

def stop_auto_ml_job(params = {}, options = {})
  req = build_request(:stop_auto_ml_job, params)
  req.send_request(options)
end

#stop_compilation_job(params = {}) ⇒ Struct

Stops a model compilation job.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn’t stopped, it sends the SIGKILL signal.

When it receives a ‘StopCompilationJob` request, Amazon SageMaker changes the `CompilationJobStatus` of the job to `Stopping`. After Amazon SageMaker stops the job, it sets the `CompilationJobStatus` to `Stopped`.

Examples:

Request syntax with placeholder values


resp = client.stop_compilation_job({
  compilation_job_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :compilation_job_name (required, String) —

    The name of the model compilation job to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24101

def stop_compilation_job(params = {}, options = {})
  req = build_request(:stop_compilation_job, params)
  req.send_request(options)
end

#stop_edge_deployment_stage(params = {}) ⇒ Struct

Stops a stage in an edge deployment plan.

Examples:

Request syntax with placeholder values


resp = client.stop_edge_deployment_stage({
  edge_deployment_plan_name: "EntityName", # required
  stage_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan to stop.

  • :stage_name (required, String) —

    The name of the stage to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24127

def stop_edge_deployment_stage(params = {}, options = {})
  req = build_request(:stop_edge_deployment_stage, params)
  req.send_request(options)
end

#stop_edge_packaging_job(params = {}) ⇒ Struct

Request to stop an edge packaging job.

Examples:

Request syntax with placeholder values


resp = client.stop_edge_packaging_job({
  edge_packaging_job_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_packaging_job_name (required, String) —

    The name of the edge packaging job.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24149

def stop_edge_packaging_job(params = {}, options = {})
  req = build_request(:stop_edge_packaging_job, params)
  req.send_request(options)
end

#stop_hyper_parameter_tuning_job(params = {}) ⇒ Struct

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.

All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the ‘Stopped` state, it releases all reserved resources for the tuning job.

Examples:

Request syntax with placeholder values


resp = client.stop_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hyper_parameter_tuning_job_name (required, String) —

    The name of the tuning job to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24178

def stop_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:stop_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#stop_inference_experiment(params = {}) ⇒ Types::StopInferenceExperimentResponse

Stops an inference experiment.

Examples:

Request syntax with placeholder values


resp = client.stop_inference_experiment({
  name: "InferenceExperimentName", # required
  model_variant_actions: { # required
    "ModelVariantName" => "Retain", # accepts Retain, Remove, Promote
  },
  desired_model_variants: [
    {
      model_name: "ModelName", # required
      variant_name: "ModelVariantName", # required
      infrastructure_config: { # required
        infrastructure_type: "RealTimeInference", # required, accepts RealTimeInference
        real_time_inference_config: { # required
          instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
          instance_count: 1, # required
        },
      },
    },
  ],
  desired_state: "Completed", # accepts Completed, Cancelled
  reason: "InferenceExperimentStatusReason",
})

Response structure


resp.inference_experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the inference experiment to stop.

  • :model_variant_actions (required, Hash<String,String>) —

    Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following:

    • ‘Promote` - Promote the shadow variant to a production variant

    • ‘Remove` - Delete the variant

    • ‘Retain` - Keep the variant as it is

  • :desired_model_variants (Array<Types::ModelVariantConfig>) —

    An array of ‘ModelVariantConfig` objects. There is one for each variant that you want to deploy after the inference experiment stops. Each `ModelVariantConfig` describes the infrastructure configuration for deploying the corresponding variant.

  • :desired_state (String) —

    The desired state of the experiment after stopping. The possible states are the following:

    • ‘Completed`: The experiment completed successfully

    • ‘Cancelled`: The experiment was canceled

  • :reason (String) —

    The reason for stopping the experiment.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24251

def stop_inference_experiment(params = {}, options = {})
  req = build_request(:stop_inference_experiment, params)
  req.send_request(options)
end

#stop_inference_recommendations_job(params = {}) ⇒ Struct

Stops an Inference Recommender job.

Examples:

Request syntax with placeholder values


resp = client.stop_inference_recommendations_job({
  job_name: "RecommendationJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_name (required, String) —

    The name of the job you want to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24273

def stop_inference_recommendations_job(params = {}, options = {})
  req = build_request(:stop_inference_recommendations_job, params)
  req.send_request(options)
end

#stop_labeling_job(params = {}) ⇒ Struct

Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.

Examples:

Request syntax with placeholder values


resp = client.stop_labeling_job({
  labeling_job_name: "LabelingJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :labeling_job_name (required, String) —

    The name of the labeling job to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24297

def stop_labeling_job(params = {}, options = {})
  req = build_request(:stop_labeling_job, params)
  req.send_request(options)
end

#stop_mlflow_tracking_server(params = {}) ⇒ Types::StopMlflowTrackingServerResponse

Programmatically stop an MLflow Tracking Server.

Examples:

Request syntax with placeholder values


resp = client.stop_mlflow_tracking_server({
  tracking_server_name: "TrackingServerName", # required
})

Response structure


resp.tracking_server_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    The name of the tracking server to stop.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24325

def stop_mlflow_tracking_server(params = {}, options = {})
  req = build_request(:stop_mlflow_tracking_server, params)
  req.send_request(options)
end

#stop_monitoring_schedule(params = {}) ⇒ Struct

Stops a previously started monitoring schedule.

Examples:

Request syntax with placeholder values


resp = client.stop_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of the schedule to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24347

def stop_monitoring_schedule(params = {}, options = {})
  req = build_request(:stop_monitoring_schedule, params)
  req.send_request(options)
end

#stop_notebook_instance(params = {}) ⇒ Struct

Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when you call ‘StopNotebookInstance`.

To access data on the ML storage volume for a notebook instance that has been terminated, call the ‘StartNotebookInstance` API. `StartNotebookInstance` launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

Examples:

Request syntax with placeholder values


resp = client.stop_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the notebook instance to terminate.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24378

def stop_notebook_instance(params = {}, options = {})
  req = build_request(:stop_notebook_instance, params)
  req.send_request(options)
end

#stop_optimization_job(params = {}) ⇒ Struct

Ends a running inference optimization job.

Examples:

Request syntax with placeholder values


resp = client.stop_optimization_job({
  optimization_job_name: "EntityName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :optimization_job_name (required, String) —

    The name that you assigned to the optimization job.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24400

def stop_optimization_job(params = {}, options = {})
  req = build_request(:stop_optimization_job, params)
  req.send_request(options)
end

#stop_pipeline_execution(params = {}) ⇒ Types::StopPipelineExecutionResponse

Stops a pipeline execution.

**Callback Step**

A pipeline execution won’t stop while a callback step is running. When you call ‘StopPipelineExecution` on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a “Status” field which is set to “Stopping”.

You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to ‘SendPipelineExecutionStepSuccess` or `SendPipelineExecutionStepFailure`.

Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.

**Lambda Step**

A pipeline execution can’t be stopped while a lambda step is running because the Lambda function invoked by the lambda step can’t be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is ‘Stopped`. If the timeout is hit the pipeline execution status is `Failed`.

Examples:

Request syntax with placeholder values


resp = client.stop_pipeline_execution({
  pipeline_execution_arn: "PipelineExecutionArn", # required
  client_request_token: "IdempotencyToken", # required
})

Response structure


resp.pipeline_execution_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (required, String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

  • :client_request_token (required, String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

    **A suitable default value is auto-generated.** You should normally not need to pass this option.**

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24464

def stop_pipeline_execution(params = {}, options = {})
  req = build_request(:stop_pipeline_execution, params)
  req.send_request(options)
end

#stop_processing_job(params = {}) ⇒ Struct

Stops a processing job.

Examples:

Request syntax with placeholder values


resp = client.stop_processing_job({
  processing_job_name: "ProcessingJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :processing_job_name (required, String) —

    The name of the processing job to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24486

def stop_processing_job(params = {}, options = {})
  req = build_request(:stop_processing_job, params)
  req.send_request(options)
end

#stop_training_job(params = {}) ⇒ Struct

Stops a training job. To stop a job, SageMaker sends the algorithm the ‘SIGTERM` signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

When it receives a ‘StopTrainingJob` request, SageMaker changes the status of the job to `Stopping`. After SageMaker stops the job, it sets the status to `Stopped`.

Examples:

Request syntax with placeholder values


resp = client.stop_training_job({
  training_job_name: "TrainingJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :training_job_name (required, String) —

    The name of the training job to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24515

def stop_training_job(params = {}, options = {})
  req = build_request(:stop_training_job, params)
  req.send_request(options)
end

#stop_transform_job(params = {}) ⇒ Struct

Stops a batch transform job.

When Amazon SageMaker receives a ‘StopTransformJob` request, the status of the job changes to `Stopping`. After Amazon SageMaker stops the job, the status is set to `Stopped`. When you stop a batch transform job before it is completed, Amazon SageMaker doesn’t store the job’s output in Amazon S3.

Examples:

Request syntax with placeholder values


resp = client.stop_transform_job({
  transform_job_name: "TransformJobName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :transform_job_name (required, String) —

    The name of the batch transform job to stop.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24543

def stop_transform_job(params = {}, options = {})
  req = build_request(:stop_transform_job, params)
  req.send_request(options)
end

#update_action(params = {}) ⇒ Types::UpdateActionResponse

Updates an action.

Examples:

Request syntax with placeholder values


resp = client.update_action({
  action_name: "ExperimentEntityName", # required
  description: "ExperimentDescription",
  status: "Unknown", # accepts Unknown, InProgress, Completed, Failed, Stopping, Stopped
  properties: {
    "StringParameterValue" => "StringParameterValue",
  },
  properties_to_remove: ["StringParameterValue"],
})

Response structure


resp.action_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :action_name (required, String) —

    The name of the action to update.

  • :description (String) —

    The new description for the action.

  • :status (String) —

    The new status for the action.

  • :properties (Hash<String,String>) —

    The new list of properties. Overwrites the current property list.

  • :properties_to_remove (Array<String>) —

    A list of properties to remove.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24589

def update_action(params = {}, options = {})
  req = build_request(:update_action, params)
  req.send_request(options)
end

#update_app_image_config(params = {}) ⇒ Types::UpdateAppImageConfigResponse

Updates the properties of an AppImageConfig.

Examples:

Request syntax with placeholder values


resp = client.update_app_image_config({
  app_image_config_name: "AppImageConfigName", # required
  kernel_gateway_image_config: {
    kernel_specs: [ # required
      {
        name: "KernelName", # required
        display_name: "KernelDisplayName",
      },
    ],
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
  },
  jupyter_lab_app_image_config: {
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
    container_config: {
      container_arguments: ["NonEmptyString64"],
      container_entrypoint: ["NonEmptyString256"],
      container_environment_variables: {
        "NonEmptyString256" => "String256",
      },
    },
  },
  code_editor_app_image_config: {
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
    container_config: {
      container_arguments: ["NonEmptyString64"],
      container_entrypoint: ["NonEmptyString256"],
      container_environment_variables: {
        "NonEmptyString256" => "String256",
      },
    },
  },
})

Response structure


resp.app_image_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24667

def update_app_image_config(params = {}, options = {})
  req = build_request(:update_app_image_config, params)
  req.send_request(options)
end

#update_artifact(params = {}) ⇒ Types::UpdateArtifactResponse

Updates an artifact.

Examples:

Request syntax with placeholder values


resp = client.update_artifact({
  artifact_arn: "ArtifactArn", # required
  artifact_name: "ExperimentEntityName",
  properties: {
    "StringParameterValue" => "ArtifactPropertyValue",
  },
  properties_to_remove: ["StringParameterValue"],
})

Response structure


resp.artifact_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :artifact_arn (required, String) —

    The Amazon Resource Name (ARN) of the artifact to update.

  • :artifact_name (String) —

    The new name for the artifact.

  • :properties (Hash<String,String>) —

    The new list of properties. Overwrites the current property list.

  • :properties_to_remove (Array<String>) —

    A list of properties to remove.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24709

def update_artifact(params = {}, options = {})
  req = build_request(:update_artifact, params)
  req.send_request(options)
end

#update_cluster(params = {}) ⇒ Types::UpdateClusterResponse

Updates a SageMaker HyperPod cluster.

Examples:

Request syntax with placeholder values


resp = client.update_cluster({
  cluster_name: "ClusterNameOrArn", # required
  instance_groups: [ # required
    {
      instance_count: 1, # required
      instance_group_name: "ClusterInstanceGroupName", # required
      instance_type: "ml.p4d.24xlarge", # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge
      life_cycle_config: { # required
        source_s3_uri: "S3Uri", # required
        on_create: "ClusterLifeCycleConfigFileName", # required
      },
      execution_role: "IAMRoleArn", # required
      threads_per_core: 1,
      instance_storage_configs: [
        {
          ebs_volume_config: {
            volume_size_in_gb: 1, # required
          },
        },
      ],
    },
  ],
})

Response structure


resp.cluster_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    Specify the name of the SageMaker HyperPod cluster you want to update.

  • :instance_groups (required, Array<Types::ClusterInstanceGroupSpecification>) —

    Specify the instance groups to update.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24760

def update_cluster(params = {}, options = {})
  req = build_request(:update_cluster, params)
  req.send_request(options)
end

#update_cluster_software(params = {}) ⇒ Types::UpdateClusterSoftwareResponse

Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see [Update the SageMaker HyperPod platform software of a cluster].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-operate.html#sagemaker-hyperpod-operate-cli-command-update-cluster-software

Examples:

Request syntax with placeholder values


resp = client.update_cluster_software({
  cluster_name: "ClusterNameOrArn", # required
})

Response structure


resp.cluster_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    Specify the name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster you want to update for security patching.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24795

def update_cluster_software(params = {}, options = {})
  req = build_request(:update_cluster_software, params)
  req.send_request(options)
end

#update_code_repository(params = {}) ⇒ Types::UpdateCodeRepositoryOutput

Updates the specified Git repository with the specified values.

Examples:

Request syntax with placeholder values


resp = client.update_code_repository({
  code_repository_name: "EntityName", # required
  git_config: {
    secret_arn: "SecretArn",
  },
})

Response structure


resp.code_repository_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :code_repository_name (required, String) —

    The name of the Git repository to update.

  • :git_config (Types::GitConfigForUpdate) —

    The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of ‘AWSCURRENT` and must be in the following format:

    ‘UserName, “password”: Password`

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24835

def update_code_repository(params = {}, options = {})
  req = build_request(:update_code_repository, params)
  req.send_request(options)
end

#update_context(params = {}) ⇒ Types::UpdateContextResponse

Updates a context.

Examples:

Request syntax with placeholder values


resp = client.update_context({
  context_name: "ContextName", # required
  description: "ExperimentDescription",
  properties: {
    "StringParameterValue" => "StringParameterValue",
  },
  properties_to_remove: ["StringParameterValue"],
})

Response structure


resp.context_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :context_name (required, String) —

    The name of the context to update.

  • :description (String) —

    The new description for the context.

  • :properties (Hash<String,String>) —

    The new list of properties. Overwrites the current property list.

  • :properties_to_remove (Array<String>) —

    A list of properties to remove.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24877

def update_context(params = {}, options = {})
  req = build_request(:update_context, params)
  req.send_request(options)
end

#update_device_fleet(params = {}) ⇒ Struct

Updates a fleet of devices.

Examples:

Request syntax with placeholder values


resp = client.update_device_fleet({
  device_fleet_name: "EntityName", # required
  role_arn: "RoleArn",
  description: "DeviceFleetDescription",
  output_config: { # required
    s3_output_location: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component
    preset_deployment_config: "String",
  },
  enable_iot_role_alias: false,
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet.

  • :role_arn (String) —

    The Amazon Resource Name (ARN) of the device.

  • :description (String) —

    Description of the fleet.

  • :output_config (required, Types::EdgeOutputConfig) —

    Output configuration for storing sample data collected by the fleet.

  • :enable_iot_role_alias (Boolean) —

    Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: “SageMakerEdge-{DeviceFleetName\}”.

    For example, if your device fleet is called “demo-fleet”, the name of the role alias will be “SageMakerEdge-demo-fleet”.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24925

def update_device_fleet(params = {}, options = {})
  req = build_request(:update_device_fleet, params)
  req.send_request(options)
end

#update_devices(params = {}) ⇒ Struct

Updates one or more devices in a fleet.

Examples:

Request syntax with placeholder values


resp = client.update_devices({
  device_fleet_name: "EntityName", # required
  devices: [ # required
    {
      device_name: "DeviceName", # required
      description: "DeviceDescription",
      iot_thing_name: "ThingName",
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet the devices belong to.

  • :devices (required, Array<Types::Device>) —

    List of devices to register with Edge Manager agent.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 24957

def update_devices(params = {}, options = {})
  req = build_request(:update_devices, params)
  req.send_request(options)
end

#update_domain(params = {}) ⇒ Types::UpdateDomainResponse

Updates the default settings for new user profiles in the domain.

Examples:

Request syntax with placeholder values


resp = client.update_domain({
  domain_id: "DomainId", # required
  default_user_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    sharing_settings: {
      notebook_output_option: "Allowed", # accepts Allowed, Disabled
      s3_output_path: "S3Uri",
      s3_kms_key_id: "KmsKeyId",
    },
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    r_studio_server_pro_app_settings: {
      access_status: "ENABLED", # accepts ENABLED, DISABLED
      user_group: "R_STUDIO_ADMIN", # accepts R_STUDIO_ADMIN, R_STUDIO_USER
    },
    r_session_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
    },
    canvas_app_settings: {
      time_series_forecasting_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        amazon_forecast_role_arn: "RoleArn",
      },
      model_register_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        cross_account_model_register_role_arn: "RoleArn",
      },
      workspace_settings: {
        s3_artifact_path: "S3Uri",
        s3_kms_key_id: "KmsKeyId",
      },
      identity_provider_o_auth_settings: [
        {
          data_source_name: "SalesforceGenie", # accepts SalesforceGenie, Snowflake
          status: "ENABLED", # accepts ENABLED, DISABLED
          secret_arn: "SecretArn",
        },
      ],
      direct_deploy_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      kendra_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      generative_ai_settings: {
        amazon_bedrock_role_arn: "RoleArn",
      },
      emr_serverless_settings: {
        execution_role_arn: "RoleArn",
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    default_landing_uri: "LandingUri",
    studio_web_portal: "ENABLED", # accepts ENABLED, DISABLED
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
      },
    ],
    studio_web_portal_settings: {
      hidden_ml_tools: ["DataWrangler"], # accepts DataWrangler, FeatureStore, EmrClusters, AutoMl, Experiments, Training, ModelEvaluation, Pipelines, Models, JumpStart, InferenceRecommender, Endpoints, Projects, InferenceOptimization
      hidden_app_types: ["JupyterServer"], # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
    },
    auto_mount_home_efs: "Enabled", # accepts Enabled, Disabled, DefaultAsDomain
  },
  domain_settings_for_update: {
    r_studio_server_pro_domain_settings_for_update: {
      domain_execution_role_arn: "RoleArn", # required
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      r_studio_connect_url: "String",
      r_studio_package_manager_url: "String",
    },
    execution_role_identity_config: "USER_PROFILE_NAME", # accepts USER_PROFILE_NAME, DISABLED
    security_group_ids: ["SecurityGroupId"],
    docker_settings: {
      enable_docker_access: "ENABLED", # accepts ENABLED, DISABLED
      vpc_only_trusted_accounts: ["AccountId"],
    },
    amazon_q_settings: {
      status: "ENABLED", # accepts ENABLED, DISABLED
      q_profile_arn: "QProfileArn",
    },
  },
  app_security_group_management: "Service", # accepts Service, Customer
  default_space_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
      },
    ],
  },
  subnet_ids: ["SubnetId"],
  app_network_access_type: "PublicInternetOnly", # accepts PublicInternetOnly, VpcOnly
})

Response structure


resp.domain_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the domain to be updated.

  • :default_user_settings (Types::UserSettings) —

    A collection of settings.

  • :domain_settings_for_update (Types::DomainSettingsForUpdate) —

    A collection of ‘DomainSettings` configuration values to update.

  • :app_security_group_management (String) —

    The entity that creates and manages the required security groups for inter-app communication in ‘VPCOnly` mode. Required when `CreateDomain.AppNetworkAccessType` is `VPCOnly` and `DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn` is provided. If setting up the domain for use with RStudio, this value must be set to `Service`.

  • :default_space_settings (Types::DefaultSpaceSettings) —

    The default settings used to create a space within the domain.

  • :subnet_ids (Array<String>) —

    The VPC subnets that Studio uses for communication.

    If removing subnets, ensure there are no apps in the ‘InService`, `Pending`, or `Deleting` state.

  • :app_network_access_type (String) —

    Specifies the VPC used for non-EFS traffic.

    • ‘PublicInternetOnly` - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access.

    • ‘VpcOnly` - All Studio traffic is through the specified VPC and subnets.

    This configuration can only be modified if there are no apps in the ‘InService`, `Pending`, or `Deleting` state. The configuration cannot be updated if `DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn` is already set or `DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn` is provided as part of the same request.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25328

def update_domain(params = {}, options = {})
  req = build_request(:update_domain, params)
  req.send_request(options)
end

#update_endpoint(params = {}) ⇒ Types::UpdateEndpointOutput

Deploys the ‘EndpointConfig` specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous `EndpointConfig` (there is no availability loss). For more information about how to control the update and traffic shifting process, see [ Update models in production].

When SageMaker receives the request, it sets the endpoint status to ‘Updating`. After updating the endpoint, it sets the status to `InService`. To check the status of an endpoint, use the

DescribeEndpoint][2

API.

<note markdown=“1”> You must not delete an ‘EndpointConfig` in use by an endpoint that is live or while the `UpdateEndpoint` or `CreateEndpoint` operations are being performed on the endpoint. To update an endpoint, you must create a new `EndpointConfig`.

If you delete the `EndpointConfig` of an endpoint that is active or

being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/dg/deployment-guardrails.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpoint.html

Examples:

Request syntax with placeholder values


resp = client.update_endpoint({
  endpoint_name: "EndpointName", # required
  endpoint_config_name: "EndpointConfigName", # required
  retain_all_variant_properties: false,
  exclude_retained_variant_properties: [
    {
      variant_property_type: "DesiredInstanceCount", # required, accepts DesiredInstanceCount, DesiredWeight, DataCaptureConfig
    },
  ],
  deployment_config: {
    blue_green_update_policy: {
      traffic_routing_configuration: { # required
        type: "ALL_AT_ONCE", # required, accepts ALL_AT_ONCE, CANARY, LINEAR
        wait_interval_in_seconds: 1, # required
        canary_size: {
          type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
          value: 1, # required
        },
        linear_step_size: {
          type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
          value: 1, # required
        },
      },
      termination_wait_in_seconds: 1,
      maximum_execution_timeout_in_seconds: 1,
    },
    rolling_update_policy: {
      maximum_batch_size: { # required
        type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
        value: 1, # required
      },
      wait_interval_in_seconds: 1, # required
      maximum_execution_timeout_in_seconds: 1,
      rollback_maximum_batch_size: {
        type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
        value: 1, # required
      },
    },
    auto_rollback_configuration: {
      alarms: [
        {
          alarm_name: "AlarmName",
        },
      ],
    },
  },
  retain_deployment_config: false,
})

Response structure


resp.endpoint_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (required, String) —

    The name of the endpoint whose configuration you want to update.

  • :endpoint_config_name (required, String) —

    The name of the new endpoint configuration.

  • :retain_all_variant_properties (Boolean) —

    When updating endpoint resources, enables or disables the retention of [variant properties], such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set ‘RetainAllVariantProperties` to `true`. To use the variant properties specified in a new `EndpointConfig` call when updating an endpoint, set `RetainAllVariantProperties` to `false`. The default is `false`.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_VariantProperty.html

  • :exclude_retained_variant_properties (Array<Types::VariantProperty>) —

    When you are updating endpoint resources with ‘RetainAllVariantProperties`, whose value is set to `true`, `ExcludeRetainedVariantProperties` specifies the list of type

    VariantProperty][1

    to override with the values provided by

    ‘EndpointConfig`. If you don’t specify a value for ‘ExcludeRetainedVariantProperties`, no variant properties are overridden.

    [1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_VariantProperty.html

  • :deployment_config (Types::DeploymentConfig) —

    The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

  • :retain_deployment_config (Boolean) —

    Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25466

def update_endpoint(params = {}, options = {})
  req = build_request(:update_endpoint, params)
  req.send_request(options)
end

#update_endpoint_weights_and_capacities(params = {}) ⇒ Types::UpdateEndpointWeightsAndCapacitiesOutput

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to ‘Updating`. After updating the endpoint, it sets the status to `InService`. To check the status of an endpoint, use the

DescribeEndpoint][1

API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpoint.html

Examples:

Request syntax with placeholder values


resp = client.update_endpoint_weights_and_capacities({
  endpoint_name: "EndpointName", # required
  desired_weights_and_capacities: [ # required
    {
      variant_name: "VariantName", # required
      desired_weight: 1.0,
      desired_instance_count: 1,
      serverless_update_config: {
        max_concurrency: 1,
        provisioned_concurrency: 1,
      },
    },
  ],
})

Response structure


resp.endpoint_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (required, String) —

    The name of an existing SageMaker endpoint.

  • :desired_weights_and_capacities (required, Array<Types::DesiredWeightAndCapacity>) —

    An object that provides new capacity and weight values for a variant.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25517

def update_endpoint_weights_and_capacities(params = {}, options = {})
  req = build_request(:update_endpoint_weights_and_capacities, params)
  req.send_request(options)
end

#update_experiment(params = {}) ⇒ Types::UpdateExperimentResponse

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

Examples:

Request syntax with placeholder values


resp = client.update_experiment({
  experiment_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  description: "ExperimentDescription",
})

Response structure


resp.experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment to update.

  • :display_name (String) —

    The name of the experiment as displayed. The name doesn’t need to be unique. If ‘DisplayName` isn’t specified, ‘ExperimentName` is displayed.

  • :description (String) —

    The description of the experiment.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25556

def update_experiment(params = {}, options = {})
  req = build_request(:update_experiment, params)
  req.send_request(options)
end

#update_feature_group(params = {}) ⇒ Types::UpdateFeatureGroupResponse

Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the ‘UpdateFeatureGroup` API.

You can add features for your feature group using the ‘FeatureAdditions` request parameter. Features cannot be removed from a feature group.

You can update the online store configuration by using the ‘OnlineStoreConfig` request parameter. If a `TtlDuration` is specified, the default `TtlDuration` applies for all records added to the feature group *after the feature group is updated*. If a record level `TtlDuration` exists from using the `PutRecord` API, the record level `TtlDuration` applies to that record instead of the default `TtlDuration`. To remove the default `TtlDuration` from an existing feature group, use the `UpdateFeatureGroup` API and set the `TtlDuration` `Unit` and `Value` to `null`.

Examples:

Request syntax with placeholder values


resp = client.update_feature_group({
  feature_group_name: "FeatureGroupNameOrArn", # required
  feature_additions: [
    {
      feature_name: "FeatureName", # required
      feature_type: "Integral", # required, accepts Integral, Fractional, String
      collection_type: "List", # accepts List, Set, Vector
      collection_config: {
        vector_config: {
          dimension: 1, # required
        },
      },
    },
  ],
  online_store_config: {
    ttl_duration: {
      unit: "Seconds", # accepts Seconds, Minutes, Hours, Days, Weeks
      value: 1,
    },
  },
  throughput_config: {
    throughput_mode: "OnDemand", # accepts OnDemand, Provisioned
    provisioned_read_capacity_units: 1,
    provisioned_write_capacity_units: 1,
  },
})

Response structure


resp.feature_group_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :feature_group_name (required, String) —

    The name or Amazon Resource Name (ARN) of the feature group that you’re updating.

  • :feature_additions (Array<Types::FeatureDefinition>) —

    Updates the feature group. Updating a feature group is an asynchronous operation. When you get an HTTP 200 response, you’ve made a valid request. It takes some time after you’ve made a valid request for Feature Store to update the feature group.

  • :online_store_config (Types::OnlineStoreConfigUpdate) —

    Updates the feature group online store configuration.

  • :throughput_config (Types::ThroughputConfigUpdate) —

    The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25639

def update_feature_group(params = {}, options = {})
  req = build_request(:update_feature_group, params)
  req.send_request(options)
end

#update_feature_metadata(params = {}) ⇒ Struct

Updates the description and parameters of the feature group.

Examples:

Request syntax with placeholder values


resp = client.update_feature_metadata({
  feature_group_name: "FeatureGroupNameOrArn", # required
  feature_name: "FeatureName", # required
  description: "FeatureDescription",
  parameter_additions: [
    {
      key: "FeatureParameterKey",
      value: "FeatureParameterValue",
    },
  ],
  parameter_removals: ["FeatureParameterKey"],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :feature_group_name (required, String) —

    The name or Amazon Resource Name (ARN) of the feature group containing the feature that you’re updating.

  • :feature_name (required, String) —

    The name of the feature that you’re updating.

  • :description (String) —

    A description that you can write to better describe the feature.

  • :parameter_additions (Array<Types::FeatureParameter>) —

    A list of key-value pairs that you can add to better describe the feature.

  • :parameter_removals (Array<String>) —

    A list of parameter keys that you can specify to remove parameters that describe your feature.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25685

def update_feature_metadata(params = {}, options = {})
  req = build_request(:update_feature_metadata, params)
  req.send_request(options)
end

#update_hub(params = {}) ⇒ Types::UpdateHubResponse

Update a hub.

Examples:

Request syntax with placeholder values


resp = client.update_hub({
  hub_name: "HubNameOrArn", # required
  hub_description: "HubDescription",
  hub_display_name: "HubDisplayName",
  hub_search_keywords: ["HubSearchKeyword"],
})

Response structure


resp.hub_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hub_name (required, String) —

    The name of the hub to update.

  • :hub_description (String) —

    A description of the updated hub.

  • :hub_display_name (String) —

    The display name of the hub.

  • :hub_search_keywords (Array<String>) —

    The searchable keywords for the hub.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25725

def update_hub(params = {}, options = {})
  req = build_request(:update_hub, params)
  req.send_request(options)
end

#update_image(params = {}) ⇒ Types::UpdateImageResponse

Updates the properties of a SageMaker image. To change the image’s tags, use the [AddTags] and [DeleteTags] APIs.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteTags.html

Examples:

Request syntax with placeholder values


resp = client.update_image({
  delete_properties: ["ImageDeleteProperty"],
  description: "ImageDescription",
  display_name: "ImageDisplayName",
  image_name: "ImageName", # required
  role_arn: "RoleArn",
})

Response structure


resp.image_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :delete_properties (Array<String>) —

    A list of properties to delete. Only the ‘Description` and `DisplayName` properties can be deleted.

  • :description (String) —

    The new description for the image.

  • :display_name (String) —

    The new display name for the image.

  • :image_name (required, String) —

    The name of the image to update.

  • :role_arn (String) —

    The new ARN for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25777

def update_image(params = {}, options = {})
  req = build_request(:update_image, params)
  req.send_request(options)
end

#update_image_version(params = {}) ⇒ Types::UpdateImageVersionResponse

Updates the properties of a SageMaker image version.

Examples:

Request syntax with placeholder values


resp = client.update_image_version({
  image_name: "ImageName", # required
  alias: "SageMakerImageVersionAlias",
  version: 1,
  aliases_to_add: ["SageMakerImageVersionAlias"],
  aliases_to_delete: ["SageMakerImageVersionAlias"],
  vendor_guidance: "NOT_PROVIDED", # accepts NOT_PROVIDED, STABLE, TO_BE_ARCHIVED, ARCHIVED
  job_type: "TRAINING", # accepts TRAINING, INFERENCE, NOTEBOOK_KERNEL
  ml_framework: "MLFramework",
  programming_lang: "ProgrammingLang",
  processor: "CPU", # accepts CPU, GPU
  horovod: false,
  release_notes: "ReleaseNotes",
})

Response structure


resp.image_version_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :image_name (required, String) —

    The name of the image.

  • :alias (String) —

    The alias of the image version.

  • :version (Integer) —

    The version of the image.

  • :aliases_to_add (Array<String>) —

    A list of aliases to add.

  • :aliases_to_delete (Array<String>) —

    A list of aliases to delete.

  • :vendor_guidance (String) —

    The availability of the image version specified by the maintainer.

    • ‘NOT_PROVIDED`: The maintainers did not provide a status for image version stability.

    • ‘STABLE`: The image version is stable.

    • ‘TO_BE_ARCHIVED`: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.

    • ‘ARCHIVED`: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

  • :job_type (String) —

    Indicates SageMaker job type compatibility.

    • ‘TRAINING`: The image version is compatible with SageMaker training jobs.

    • ‘INFERENCE`: The image version is compatible with SageMaker inference jobs.

    • ‘NOTEBOOK_KERNEL`: The image version is compatible with SageMaker notebook kernels.

  • :ml_framework (String) —

    The machine learning framework vended in the image version.

  • :programming_lang (String) —

    The supported programming language and its version.

  • :processor (String) —

    Indicates CPU or GPU compatibility.

    • ‘CPU`: The image version is compatible with CPU.

    • ‘GPU`: The image version is compatible with GPU.

  • :horovod (Boolean) —

    Indicates Horovod compatibility.

  • :release_notes (String) —

    The maintainer description of the image version.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25874

def update_image_version(params = {}, options = {})
  req = build_request(:update_image_version, params)
  req.send_request(options)
end

#update_inference_component(params = {}) ⇒ Types::UpdateInferenceComponentOutput

Updates an inference component.

Examples:

Request syntax with placeholder values


resp = client.update_inference_component({
  inference_component_name: "InferenceComponentName", # required
  specification: {
    model_name: "ModelName",
    container: {
      image: "ContainerImage",
      artifact_url: "Url",
      environment: {
        "EnvironmentKey" => "EnvironmentValue",
      },
    },
    startup_parameters: {
      model_data_download_timeout_in_seconds: 1,
      container_startup_health_check_timeout_in_seconds: 1,
    },
    compute_resource_requirements: { # required
      number_of_cpu_cores_required: 1.0,
      number_of_accelerator_devices_required: 1.0,
      min_memory_required_in_mb: 1, # required
      max_memory_required_in_mb: 1,
    },
  },
  runtime_config: {
    copy_count: 1, # required
  },
})

Response structure


resp.inference_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :inference_component_name (required, String) —

    The name of the inference component.

  • :specification (Types::InferenceComponentSpecification) —

    Details about the resources to deploy with this inference component, including the model, container, and compute resources.

  • :runtime_config (Types::InferenceComponentRuntimeConfig) —

    Runtime settings for a model that is deployed with an inference component.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25933

def update_inference_component(params = {}, options = {})
  req = build_request(:update_inference_component, params)
  req.send_request(options)
end

#update_inference_component_runtime_config(params = {}) ⇒ Types::UpdateInferenceComponentRuntimeConfigOutput

Runtime settings for a model that is deployed with an inference component.

Examples:

Request syntax with placeholder values


resp = client.update_inference_component_runtime_config({
  inference_component_name: "InferenceComponentName", # required
  desired_runtime_config: { # required
    copy_count: 1, # required
  },
})

Response structure


resp.inference_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :inference_component_name (required, String) —

    The name of the inference component to update.

  • :desired_runtime_config (required, Types::InferenceComponentRuntimeConfig) —

    Runtime settings for a model that is deployed with an inference component.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 25969

def update_inference_component_runtime_config(params = {}, options = {})
  req = build_request(:update_inference_component_runtime_config, params)
  req.send_request(options)
end

#update_inference_experiment(params = {}) ⇒ Types::UpdateInferenceExperimentResponse

Updates an inference experiment that you created. The status of the inference experiment has to be either ‘Created`, `Running`. For more information on the status of an inference experiment, see [DescribeInferenceExperiment].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeInferenceExperiment.html

Examples:

Request syntax with placeholder values


resp = client.update_inference_experiment({
  name: "InferenceExperimentName", # required
  schedule: {
    start_time: Time.now,
    end_time: Time.now,
  },
  description: "InferenceExperimentDescription",
  model_variants: [
    {
      model_name: "ModelName", # required
      variant_name: "ModelVariantName", # required
      infrastructure_config: { # required
        infrastructure_type: "RealTimeInference", # required, accepts RealTimeInference
        real_time_inference_config: { # required
          instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
          instance_count: 1, # required
        },
      },
    },
  ],
  data_storage_config: {
    destination: "DestinationS3Uri", # required
    kms_key: "KmsKeyId",
    content_type: {
      csv_content_types: ["CsvContentType"],
      json_content_types: ["JsonContentType"],
    },
  },
  shadow_mode_config: {
    source_model_variant_name: "ModelVariantName", # required
    shadow_model_variants: [ # required
      {
        shadow_model_variant_name: "ModelVariantName", # required
        sampling_percentage: 1, # required
      },
    ],
  },
})

Response structure


resp.inference_experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    The name of the inference experiment to be updated.

  • :schedule (Types::InferenceExperimentSchedule) —

    The duration for which the inference experiment will run. If the status of the inference experiment is ‘Created`, then you can update both the start and end dates. If the status of the inference experiment is `Running`, then you can update only the end date.

  • :description (String) —

    The description of the inference experiment.

  • :model_variants (Array<Types::ModelVariantConfig>) —

    An array of ‘ModelVariantConfig` objects. There is one for each variant, whose infrastructure configuration you want to update.

  • :data_storage_config (Types::InferenceExperimentDataStorageConfig) —

    The Amazon S3 location and configuration for storing inference request and response data.

  • :shadow_mode_config (Types::ShadowModeConfig) —

    The configuration of ‘ShadowMode` inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26063

def update_inference_experiment(params = {}, options = {})
  req = build_request(:update_inference_experiment, params)
  req.send_request(options)
end

#update_mlflow_tracking_server(params = {}) ⇒ Types::UpdateMlflowTrackingServerResponse

Updates properties of an existing MLflow Tracking Server.

Examples:

Request syntax with placeholder values


resp = client.update_mlflow_tracking_server({
  tracking_server_name: "TrackingServerName", # required
  artifact_store_uri: "S3Uri",
  tracking_server_size: "Small", # accepts Small, Medium, Large
  automatic_model_registration: false,
  weekly_maintenance_window_start: "WeeklyMaintenanceWindowStart",
})

Response structure


resp.tracking_server_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :tracking_server_name (required, String) —

    The name of the MLflow Tracking Server to update.

  • :artifact_store_uri (String) —

    The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow Tracking Server.

  • :tracking_server_size (String) —

    The new size for the MLflow Tracking Server.

  • :automatic_model_registration (Boolean) —

    Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to ‘True`. To disable automatic model registration, set this value to `False`. If not specified, `AutomaticModelRegistration` defaults to `False`

  • :weekly_maintenance_window_start (String) —

    The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26114

def update_mlflow_tracking_server(params = {}, options = {})
  req = build_request(:update_mlflow_tracking_server, params)
  req.send_request(options)
end

#update_model_card(params = {}) ⇒ Types::UpdateModelCardResponse

Update an Amazon SageMaker Model Card.

You cannot update both model card content and model card status in a single call.

Examples:

Request syntax with placeholder values


resp = client.update_model_card({
  model_card_name: "ModelCardNameOrArn", # required
  content: "ModelCardContent",
  model_card_status: "Draft", # accepts Draft, PendingReview, Approved, Archived
})

Response structure


resp.model_card_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_card_name (required, String) —

    The name or Amazon Resource Name (ARN) of the model card to update.

  • :content (String) —

    The updated model card content. Content must be in [model card JSON schema] and provided as a string.

    When updating model card content, be sure to include the full content and not just updated content.

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html#model-cards-json-schema

  • :model_card_status (String) —

    The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.

    • ‘Draft`: The model card is a work in progress.

    • ‘PendingReview`: The model card is pending review.

    • ‘Approved`: The model card is approved.

    • ‘Archived`: The model card is archived. No more updates should be made to the model card, but it can still be exported.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26172

def update_model_card(params = {}, options = {})
  req = build_request(:update_model_card, params)
  req.send_request(options)
end

#update_model_package(params = {}) ⇒ Types::UpdateModelPackageOutput

Updates a versioned model.

Examples:

Request syntax with placeholder values


resp = client.update_model_package({
  model_package_arn: "ModelPackageArn", # required
  model_approval_status: "Approved", # accepts Approved, Rejected, PendingManualApproval
  approval_description: "ApprovalDescription",
  customer_metadata_properties: {
    "CustomerMetadataKey" => "CustomerMetadataValue",
  },
  customer_metadata_properties_to_remove: ["CustomerMetadataKey"],
  additional_inference_specifications_to_add: [
    {
      name: "EntityName", # required
      description: "EntityDescription",
      containers: [ # required
        {
          container_hostname: "ContainerHostname",
          image: "ContainerImage", # required
          image_digest: "ImageDigest",
          model_data_url: "Url",
          model_data_source: {
            s3_data_source: {
              s3_uri: "S3ModelUri", # required
              s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
              compression_type: "None", # required, accepts None, Gzip
              model_access_config: {
                accept_eula: false, # required
              },
              hub_access_config: {
                hub_content_arn: "HubContentArn", # required
              },
            },
          },
          product_id: "ProductId",
          environment: {
            "EnvironmentKey" => "EnvironmentValue",
          },
          model_input: {
            data_input_config: "DataInputConfig", # required
          },
          framework: "String",
          framework_version: "ModelPackageFrameworkVersion",
          nearest_model_name: "String",
          additional_s3_data_source: {
            s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
            s3_uri: "S3Uri", # required
            compression_type: "None", # accepts None, Gzip
          },
        },
      ],
      supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
      supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
      supported_content_types: ["ContentType"],
      supported_response_mime_types: ["ResponseMIMEType"],
    },
  ],
  inference_specification: {
    containers: [ # required
      {
        container_hostname: "ContainerHostname",
        image: "ContainerImage", # required
        image_digest: "ImageDigest",
        model_data_url: "Url",
        model_data_source: {
          s3_data_source: {
            s3_uri: "S3ModelUri", # required
            s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
            compression_type: "None", # required, accepts None, Gzip
            model_access_config: {
              accept_eula: false, # required
            },
            hub_access_config: {
              hub_content_arn: "HubContentArn", # required
            },
          },
        },
        product_id: "ProductId",
        environment: {
          "EnvironmentKey" => "EnvironmentValue",
        },
        model_input: {
          data_input_config: "DataInputConfig", # required
        },
        framework: "String",
        framework_version: "ModelPackageFrameworkVersion",
        nearest_model_name: "String",
        additional_s3_data_source: {
          s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
          s3_uri: "S3Uri", # required
          compression_type: "None", # accepts None, Gzip
        },
      },
    ],
    supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge
    supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge
    supported_content_types: ["ContentType"],
    supported_response_mime_types: ["ResponseMIMEType"],
  },
  source_uri: "ModelPackageSourceUri",
  model_card: {
    model_card_content: "ModelCardContent",
    model_card_status: "Draft", # accepts Draft, PendingReview, Approved, Archived
  },
})

Response structure


resp.model_package_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_package_arn (required, String) —

    The Amazon Resource Name (ARN) of the model package.

  • :model_approval_status (String) —

    The approval status of the model.

  • :approval_description (String) —

    A description for the approval status of the model.

  • :customer_metadata_properties (Hash<String,String>) —

    The metadata properties associated with the model package versions.

  • :customer_metadata_properties_to_remove (Array<String>) —

    The metadata properties associated with the model package versions to remove.

  • :additional_inference_specifications_to_add (Array<Types::AdditionalInferenceSpecificationDefinition>) —

    An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

  • :inference_specification (Types::InferenceSpecification) —

    Specifies details about inference jobs that you can run with models based on this model package, including the following information:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the model package supports for inference.

  • :source_uri (String) —

    The URI of the source for the model package.

  • :model_card (Types::ModelPackageModelCard) —

    The model card associated with the model package. Since ‘ModelPackageModelCard` is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of `ModelCard`. The `ModelPackageModelCard` schema does not include `model_package_details`, and `model_overview` is composed of the `model_creator` and `model_artifact` properties. For more information about the model package model card schema, see [Model package model card schema]. For more information about the model card associated with the model package, see [View the Details of a Model Version].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/model-registry-details.html#model-card-schema [2]: docs.aws.amazon.com/sagemaker/latest/dg/model-registry-details.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26353

def update_model_package(params = {}, options = {})
  req = build_request(:update_model_package, params)
  req.send_request(options)
end

#update_monitoring_alert(params = {}) ⇒ Types::UpdateMonitoringAlertResponse

Update the parameters of a model monitor alert.

Examples:

Request syntax with placeholder values


resp = client.update_monitoring_alert({
  monitoring_schedule_name: "MonitoringScheduleName", # required
  monitoring_alert_name: "MonitoringAlertName", # required
  datapoints_to_alert: 1, # required
  evaluation_period: 1, # required
})

Response structure


resp.monitoring_schedule_arn #=> String
resp.monitoring_alert_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of a monitoring schedule.

  • :monitoring_alert_name (required, String) —

    The name of a monitoring alert.

  • :datapoints_to_alert (required, Integer) —

    Within ‘EvaluationPeriod`, how many execution failures will raise an alert.

  • :evaluation_period (required, Integer) —

    The number of most recent monitoring executions to consider when evaluating alert status.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26397

def update_monitoring_alert(params = {}, options = {})
  req = build_request(:update_monitoring_alert, params)
  req.send_request(options)
end

#update_monitoring_schedule(params = {}) ⇒ Types::UpdateMonitoringScheduleResponse

Updates a previously created schedule.

Examples:

Request syntax with placeholder values


resp = client.update_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
  monitoring_schedule_config: { # required
    schedule_config: {
      schedule_expression: "ScheduleExpression", # required
      data_analysis_start_time: "String",
      data_analysis_end_time: "String",
    },
    monitoring_job_definition: {
      baseline_config: {
        baselining_job_name: "ProcessingJobName",
        constraints_resource: {
          s3_uri: "S3Uri",
        },
        statistics_resource: {
          s3_uri: "S3Uri",
        },
      },
      monitoring_inputs: [ # required
        {
          endpoint_input: {
            endpoint_name: "EndpointName", # required
            local_path: "ProcessingLocalPath", # required
            s3_input_mode: "Pipe", # accepts Pipe, File
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
            features_attribute: "String",
            inference_attribute: "String",
            probability_attribute: "String",
            probability_threshold_attribute: 1.0,
            start_time_offset: "MonitoringTimeOffsetString",
            end_time_offset: "MonitoringTimeOffsetString",
            exclude_features_attribute: "ExcludeFeaturesAttribute",
          },
          batch_transform_input: {
            data_captured_destination_s3_uri: "DestinationS3Uri", # required
            dataset_format: { # required
              csv: {
                header: false,
              },
              json: {
                line: false,
              },
              parquet: {
              },
            },
            local_path: "ProcessingLocalPath", # required
            s3_input_mode: "Pipe", # accepts Pipe, File
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
            features_attribute: "String",
            inference_attribute: "String",
            probability_attribute: "String",
            probability_threshold_attribute: 1.0,
            start_time_offset: "MonitoringTimeOffsetString",
            end_time_offset: "MonitoringTimeOffsetString",
            exclude_features_attribute: "ExcludeFeaturesAttribute",
          },
        },
      ],
      monitoring_output_config: { # required
        monitoring_outputs: [ # required
          {
            s3_output: { # required
              s3_uri: "MonitoringS3Uri", # required
              local_path: "ProcessingLocalPath", # required
              s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
            },
          },
        ],
        kms_key_id: "KmsKeyId",
      },
      monitoring_resources: { # required
        cluster_config: { # required
          instance_count: 1, # required
          instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
          volume_size_in_gb: 1, # required
          volume_kms_key_id: "KmsKeyId",
        },
      },
      monitoring_app_specification: { # required
        image_uri: "ImageUri", # required
        container_entrypoint: ["ContainerEntrypointString"],
        container_arguments: ["ContainerArgument"],
        record_preprocessor_source_uri: "S3Uri",
        post_analytics_processor_source_uri: "S3Uri",
      },
      stopping_condition: {
        max_runtime_in_seconds: 1, # required
      },
      environment: {
        "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
      },
      network_config: {
        enable_inter_container_traffic_encryption: false,
        enable_network_isolation: false,
        vpc_config: {
          security_group_ids: ["SecurityGroupId"], # required
          subnets: ["SubnetId"], # required
        },
      },
      role_arn: "RoleArn", # required
    },
    monitoring_job_definition_name: "MonitoringJobDefinitionName",
    monitoring_type: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability
  },
})

Response structure


resp.monitoring_schedule_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

  • :monitoring_schedule_config (required, Types::MonitoringScheduleConfig) —

    The configuration object that specifies the monitoring schedule and defines the monitoring job.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26532

def update_monitoring_schedule(params = {}, options = {})
  req = build_request(:update_monitoring_schedule, params)
  req.send_request(options)
end

#update_notebook_instance(params = {}) ⇒ Struct

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.

Examples:

Request syntax with placeholder values


resp = client.update_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
  instance_type: "ml.t2.medium", # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
  role_arn: "RoleArn",
  lifecycle_config_name: "NotebookInstanceLifecycleConfigName",
  disassociate_lifecycle_config: false,
  volume_size_in_gb: 1,
  default_code_repository: "CodeRepositoryNameOrUrl",
  additional_code_repositories: ["CodeRepositoryNameOrUrl"],
  accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
  disassociate_accelerator_types: false,
  disassociate_default_code_repository: false,
  disassociate_additional_code_repositories: false,
  root_access: "Enabled", # accepts Enabled, Disabled
  instance_metadata_service_configuration: {
    minimum_instance_metadata_service_version: "MinimumInstanceMetadataServiceVersion", # required
  },
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the notebook instance to update.

  • :instance_type (String) —

    The Amazon ML compute instance type.

  • :role_arn (String) —

    The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access the notebook instance. For more information, see [SageMaker Roles].

    <note markdown=“1”> To be able to pass this role to SageMaker, the caller of this API must have the ‘iam:PassRole` permission.

    </note>

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

  • :lifecycle_config_name (String) —

    The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html

  • :disassociate_lifecycle_config (Boolean) —

    Set to ‘true` to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :volume_size_in_gb (Integer) —

    The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so SageMaker can’t determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can’t decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.

  • :default_code_repository (String) —

    The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in [Amazon Web Services CodeCommit] or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see [Associating Git Repositories with SageMaker Notebook Instances].

    [1]: docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html

  • :additional_code_repositories (Array<String>) —

    An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in

    Amazon Web Services CodeCommit][1

    or in any other Git repository.

    These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see [Associating Git Repositories with SageMaker Notebook Instances].

    [1]: docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html

  • :accelerator_types (Array<String>) —

    A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see [Using Elastic Inference in Amazon SageMaker].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/ei.html

  • :disassociate_accelerator_types (Boolean) —

    A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :disassociate_default_code_repository (Boolean) —

    The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :disassociate_additional_code_repositories (Boolean) —

    A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :root_access (String) —

    Whether root access is enabled or disabled for users of the notebook instance. The default value is ‘Enabled`.

    <note markdown=“1”> If you set this to ‘Disabled`, users don’t have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.

    </note>
  • :instance_metadata_service_configuration (Types::InstanceMetadataServiceConfiguration) —

    Information on the IMDS configuration of the notebook instance

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26683

def update_notebook_instance(params = {}, options = {})
  req = build_request(:update_notebook_instance, params)
  req.send_request(options)
end

#update_notebook_instance_lifecycle_config(params = {}) ⇒ Struct

Updates a notebook instance lifecycle configuration created with the

CreateNotebookInstanceLifecycleConfig][1

API.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateNotebookInstanceLifecycleConfig.html

Examples:

Request syntax with placeholder values


resp = client.update_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
  on_create: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
  on_start: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_lifecycle_config_name (required, String) —

    The name of the lifecycle configuration.

  • :on_create (Array<Types::NotebookInstanceLifecycleHook>) —

    The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

  • :on_start (Array<Types::NotebookInstanceLifecycleHook>) —

    The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26729

def update_notebook_instance_lifecycle_config(params = {}, options = {})
  req = build_request(:update_notebook_instance_lifecycle_config, params)
  req.send_request(options)
end

#update_pipeline(params = {}) ⇒ Types::UpdatePipelineResponse

Updates a pipeline.

Examples:

Request syntax with placeholder values


resp = client.update_pipeline({
  pipeline_name: "PipelineName", # required
  pipeline_display_name: "PipelineName",
  pipeline_definition: "PipelineDefinition",
  pipeline_definition_s3_location: {
    bucket: "BucketName", # required
    object_key: "Key", # required
    version_id: "VersionId",
  },
  pipeline_description: "PipelineDescription",
  role_arn: "RoleArn",
  parallelism_configuration: {
    max_parallel_execution_steps: 1, # required
  },
})

Response structure


resp.pipeline_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_name (required, String) —

    The name of the pipeline to update.

  • :pipeline_display_name (String) —

    The display name of the pipeline.

  • :pipeline_definition (String) —

    The JSON pipeline definition.

  • :pipeline_definition_s3_location (Types::PipelineDefinitionS3Location) —

    The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

  • :pipeline_description (String) —

    The description of the pipeline.

  • :role_arn (String) —

    The Amazon Resource Name (ARN) that the pipeline uses to execute.

  • :parallelism_configuration (Types::ParallelismConfiguration) —

    If specified, it applies to all executions of this pipeline by default.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26790

def update_pipeline(params = {}, options = {})
  req = build_request(:update_pipeline, params)
  req.send_request(options)
end

#update_pipeline_execution(params = {}) ⇒ Types::UpdatePipelineExecutionResponse

Updates a pipeline execution.

Examples:

Request syntax with placeholder values


resp = client.update_pipeline_execution({
  pipeline_execution_arn: "PipelineExecutionArn", # required
  pipeline_execution_description: "PipelineExecutionDescription",
  pipeline_execution_display_name: "PipelineExecutionName",
  parallelism_configuration: {
    max_parallel_execution_steps: 1, # required
  },
})

Response structure


resp.pipeline_execution_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :pipeline_execution_arn (required, String) —

    The Amazon Resource Name (ARN) of the pipeline execution.

  • :pipeline_execution_description (String) —

    The description of the pipeline execution.

  • :pipeline_execution_display_name (String) —

    The display name of the pipeline execution.

  • :parallelism_configuration (Types::ParallelismConfiguration) —

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26833

def update_pipeline_execution(params = {}, options = {})
  req = build_request(:update_pipeline_execution, params)
  req.send_request(options)
end

#update_project(params = {}) ⇒ Types::UpdateProjectOutput

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.

<note markdown=“1”> You must not update a project that is in use. If you update the ‘ServiceCatalogProvisioningUpdateDetails` of a project that is active or being created, or updated, you may lose resources already created by the project.

</note>

Examples:

Request syntax with placeholder values


resp = client.update_project({
  project_name: "ProjectEntityName", # required
  project_description: "EntityDescription",
  service_catalog_provisioning_update_details: {
    provisioning_artifact_id: "ServiceCatalogEntityId",
    provisioning_parameters: [
      {
        key: "ProvisioningParameterKey",
        value: "ProvisioningParameterValue",
      },
    ],
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.project_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 26914

def update_project(params = {}, options = {})
  req = build_request(:update_project, params)
  req.send_request(options)
end

#update_space(params = {}) ⇒ Types::UpdateSpaceResponse

Updates the settings of a space.

Examples:

Request syntax with placeholder values


resp = client.update_space({
  domain_id: "DomainId", # required
  space_name: "SpaceName", # required
  space_settings: {
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      app_lifecycle_management: {
        idle_settings: {
          idle_timeout_in_minutes: 1,
        },
      },
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          idle_timeout_in_minutes: 1,
        },
      },
    },
    app_type: "JupyterServer", # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
    space_storage_settings: {
      ebs_storage_settings: {
        ebs_volume_size_in_gb: 1, # required
      },
    },
    custom_file_systems: [
      {
        efs_file_system: {
          file_system_id: "FileSystemId", # required
        },
      },
    ],
  },
  space_display_name: "NonEmptyString64",
})

Response structure


resp.space_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the associated domain.

  • :space_name (required, String) —

    The name of the space.

  • :space_settings (Types::SpaceSettings) —

    A collection of space settings.

  • :space_display_name (String) —

    The name of the space that appears in the Amazon SageMaker Studio UI.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27033

def update_space(params = {}, options = {})
  req = build_request(:update_space, params)
  req.send_request(options)
end

#update_training_job(params = {}) ⇒ Types::UpdateTrainingJobResponse

Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.

Examples:

Request syntax with placeholder values


resp = client.update_training_job({
  training_job_name: "TrainingJobName", # required
  profiler_config: {
    s3_output_path: "S3Uri",
    profiling_interval_in_milliseconds: 1,
    profiling_parameters: {
      "ConfigKey" => "ConfigValue",
    },
    disable_profiler: false,
  },
  profiler_rule_configurations: [
    {
      rule_configuration_name: "RuleConfigurationName", # required
      local_path: "DirectoryPath",
      s3_output_path: "S3Uri",
      rule_evaluator_image: "AlgorithmImage", # required
      instance_type: "ml.t3.medium", # accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge
      volume_size_in_gb: 1,
      rule_parameters: {
        "ConfigKey" => "ConfigValue",
      },
    },
  ],
  resource_config: {
    keep_alive_period_in_seconds: 1, # required
  },
  remote_debug_config: {
    enable_remote_debug: false,
  },
})

Response structure


resp.training_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :training_job_name (required, String) —

    The name of a training job to update the Debugger profiling configuration.

  • :profiler_config (Types::ProfilerConfigForUpdate) —

    Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

  • :profiler_rule_configurations (Array<Types::ProfilerRuleConfiguration>) —

    Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

  • :resource_config (Types::ResourceConfigForUpdate) —

    The training job ‘ResourceConfig` to update warm pool retention length.

  • :remote_debug_config (Types::RemoteDebugConfigForUpdate) —

    Configuration for remote debugging while the training job is running. You can update the remote debugging configuration when the ‘SecondaryStatus` of the job is `Downloading` or `Training`.To learn more about the remote debugging functionality of SageMaker, see [Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging].

    [1]: docs.aws.amazon.com/sagemaker/latest/dg/train-remote-debugging.html

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27114

def update_training_job(params = {}, options = {})
  req = build_request(:update_training_job, params)
  req.send_request(options)
end

#update_trial(params = {}) ⇒ Types::UpdateTrialResponse

Updates the display name of a trial.

Examples:

Request syntax with placeholder values


resp = client.update_trial({
  trial_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
})

Response structure


resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_name (required, String) —

    The name of the trial to update.

  • :display_name (String) —

    The name of the trial as displayed. The name doesn’t need to be unique. If ‘DisplayName` isn’t specified, ‘TrialName` is displayed.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27147

def update_trial(params = {}, options = {})
  req = build_request(:update_trial, params)
  req.send_request(options)
end

#update_trial_component(params = {}) ⇒ Types::UpdateTrialComponentResponse

Updates one or more properties of a trial component.

Examples:

Request syntax with placeholder values


resp = client.update_trial_component({
  trial_component_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  status: {
    primary_status: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
    message: "TrialComponentStatusMessage",
  },
  start_time: Time.now,
  end_time: Time.now,
  parameters: {
    "TrialComponentKey320" => {
      string_value: "StringParameterValue",
      number_value: 1.0,
    },
  },
  parameters_to_remove: ["TrialComponentKey256"],
  input_artifacts: {
    "TrialComponentKey128" => {
      media_type: "MediaType",
      value: "TrialComponentArtifactValue", # required
    },
  },
  input_artifacts_to_remove: ["TrialComponentKey256"],
  output_artifacts: {
    "TrialComponentKey128" => {
      media_type: "MediaType",
      value: "TrialComponentArtifactValue", # required
    },
  },
  output_artifacts_to_remove: ["TrialComponentKey256"],
})

Response structure


resp.trial_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component to update.

  • :display_name (String) —

    The name of the component as displayed. The name doesn’t need to be unique. If ‘DisplayName` isn’t specified, ‘TrialComponentName` is displayed.

  • :status (Types::TrialComponentStatus) —

    The new status of the component.

  • :start_time (Time, DateTime, Date, Integer, String) —

    When the component started.

  • :end_time (Time, DateTime, Date, Integer, String) —

    When the component ended.

  • :parameters (Hash<String,Types::TrialComponentParameterValue>) —

    Replaces all of the component’s hyperparameters with the specified hyperparameters or add new hyperparameters. Existing hyperparameters are replaced if the trial component is updated with an identical hyperparameter key.

  • :parameters_to_remove (Array<String>) —

    The hyperparameters to remove from the component.

  • :input_artifacts (Hash<String,Types::TrialComponentArtifact>) —

    Replaces all of the component’s input artifacts with the specified artifacts or adds new input artifacts. Existing input artifacts are replaced if the trial component is updated with an identical input artifact key.

  • :input_artifacts_to_remove (Array<String>) —

    The input artifacts to remove from the component.

  • :output_artifacts (Hash<String,Types::TrialComponentArtifact>) —

    Replaces all of the component’s output artifacts with the specified artifacts or adds new output artifacts. Existing output artifacts are replaced if the trial component is updated with an identical output artifact key.

  • :output_artifacts_to_remove (Array<String>) —

    The output artifacts to remove from the component.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27244

def update_trial_component(params = {}, options = {})
  req = build_request(:update_trial_component, params)
  req.send_request(options)
end

#update_user_profile(params = {}) ⇒ Types::UpdateUserProfileResponse

Updates a user profile.

Examples:

Request syntax with placeholder values


resp = client.({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  user_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    sharing_settings: {
      notebook_output_option: "Allowed", # accepts Allowed, Disabled
      s3_output_path: "S3Uri",
      s3_kms_key_id: "KmsKeyId",
    },
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    r_studio_server_pro_app_settings: {
      access_status: "ENABLED", # accepts ENABLED, DISABLED
      user_group: "R_STUDIO_ADMIN", # accepts R_STUDIO_ADMIN, R_STUDIO_USER
    },
    r_session_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
    },
    canvas_app_settings: {
      time_series_forecasting_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        amazon_forecast_role_arn: "RoleArn",
      },
      model_register_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        cross_account_model_register_role_arn: "RoleArn",
      },
      workspace_settings: {
        s3_artifact_path: "S3Uri",
        s3_kms_key_id: "KmsKeyId",
      },
      identity_provider_o_auth_settings: [
        {
          data_source_name: "SalesforceGenie", # accepts SalesforceGenie, Snowflake
          status: "ENABLED", # accepts ENABLED, DISABLED
          secret_arn: "SecretArn",
        },
      ],
      direct_deploy_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      kendra_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      generative_ai_settings: {
        amazon_bedrock_role_arn: "RoleArn",
      },
      emr_serverless_settings: {
        execution_role_arn: "RoleArn",
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    default_landing_uri: "LandingUri",
    studio_web_portal: "ENABLED", # accepts ENABLED, DISABLED
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
      },
    ],
    studio_web_portal_settings: {
      hidden_ml_tools: ["DataWrangler"], # accepts DataWrangler, FeatureStore, EmrClusters, AutoMl, Experiments, Training, ModelEvaluation, Pipelines, Models, JumpStart, InferenceRecommender, Endpoints, Projects, InferenceOptimization
      hidden_app_types: ["JupyterServer"], # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
    },
    auto_mount_home_efs: "Enabled", # accepts Enabled, Disabled, DefaultAsDomain
  },
})

Response structure


resp. #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name.

  • :user_settings (Types::UserSettings) —

    A collection of settings.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27467

def (params = {}, options = {})
  req = build_request(:update_user_profile, params)
  req.send_request(options)
end

#update_workforce(params = {}) ⇒ Types::UpdateWorkforceResponse

Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.

The worker portal is now supported in VPC and public internet.

Use ‘SourceIpConfig` to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten [CIDRs]. By default, a workforce isn’t restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a ‘Not Found` error message on the worker portal.

To restrict access to all the workers in public internet, add the ‘SourceIpConfig` CIDR value as “10.0.0.0/16”.

Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.

Use ‘OidcConfig` to update the configuration of a workforce created using your own OIDC IdP.

You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the [DeleteWorkteam] operation.

After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the [DescribeWorkforce] operation.

This operation only applies to private workforces.

[1]: docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteWorkteam.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeWorkforce.html

Examples:

Request syntax with placeholder values


resp = client.update_workforce({
  workforce_name: "WorkforceName", # required
  source_ip_config: {
    cidrs: ["Cidr"], # required
  },
  oidc_config: {
    client_id: "ClientId", # required
    client_secret: "ClientSecret", # required
    issuer: "OidcEndpoint", # required
    authorization_endpoint: "OidcEndpoint", # required
    token_endpoint: "OidcEndpoint", # required
    user_info_endpoint: "OidcEndpoint", # required
    logout_endpoint: "OidcEndpoint", # required
    jwks_uri: "OidcEndpoint", # required
    scope: "Scope",
    authentication_request_extra_params: {
      "AuthenticationRequestExtraParamsKey" => "AuthenticationRequestExtraParamsValue",
    },
  },
  workforce_vpc_config: {
    vpc_id: "WorkforceVpcId",
    security_group_ids: ["WorkforceSecurityGroupId"],
    subnets: ["WorkforceSubnetId"],
  },
})

Response structure


resp.workforce.workforce_name #=> String
resp.workforce.workforce_arn #=> String
resp.workforce.last_updated_date #=> Time
resp.workforce.source_ip_config.cidrs #=> Array
resp.workforce.source_ip_config.cidrs[0] #=> String
resp.workforce.sub_domain #=> String
resp.workforce.cognito_config.user_pool #=> String
resp.workforce.cognito_config.client_id #=> String
resp.workforce.oidc_config.client_id #=> String
resp.workforce.oidc_config.issuer #=> String
resp.workforce.oidc_config.authorization_endpoint #=> String
resp.workforce.oidc_config.token_endpoint #=> String
resp.workforce.oidc_config. #=> String
resp.workforce.oidc_config.logout_endpoint #=> String
resp.workforce.oidc_config.jwks_uri #=> String
resp.workforce.oidc_config.scope #=> String
resp.workforce.oidc_config.authentication_request_extra_params #=> Hash
resp.workforce.oidc_config.authentication_request_extra_params["AuthenticationRequestExtraParamsKey"] #=> String
resp.workforce.create_date #=> Time
resp.workforce.workforce_vpc_config.vpc_id #=> String
resp.workforce.workforce_vpc_config.security_group_ids #=> Array
resp.workforce.workforce_vpc_config.security_group_ids[0] #=> String
resp.workforce.workforce_vpc_config.subnets #=> Array
resp.workforce.workforce_vpc_config.subnets[0] #=> String
resp.workforce.workforce_vpc_config.vpc_endpoint_id #=> String
resp.workforce.status #=> String, one of "Initializing", "Updating", "Deleting", "Failed", "Active"
resp.workforce.failure_reason #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27603

def update_workforce(params = {}, options = {})
  req = build_request(:update_workforce, params)
  req.send_request(options)
end

#update_workteam(params = {}) ⇒ Types::UpdateWorkteamResponse

Updates an existing work team with new member definitions or description.

Examples:

Request syntax with placeholder values


resp = client.update_workteam({
  workteam_name: "WorkteamName", # required
  member_definitions: [
    {
      cognito_member_definition: {
        user_pool: "CognitoUserPool", # required
        user_group: "CognitoUserGroup", # required
        client_id: "ClientId", # required
      },
      oidc_member_definition: {
        groups: ["Group"],
      },
    },
  ],
  description: "String200",
  notification_configuration: {
    notification_topic_arn: "NotificationTopicArn",
  },
  worker_access_configuration: {
    s3_presign: {
      iam_policy_constraints: {
        source_ip: "Enabled", # accepts Enabled, Disabled
        vpc_source_ip: "Enabled", # accepts Enabled, Disabled
      },
    },
  },
})

Response structure


resp.workteam.workteam_name #=> String
resp.workteam.member_definitions #=> Array
resp.workteam.member_definitions[0].cognito_member_definition.user_pool #=> String
resp.workteam.member_definitions[0].cognito_member_definition.user_group #=> String
resp.workteam.member_definitions[0].cognito_member_definition.client_id #=> String
resp.workteam.member_definitions[0].oidc_member_definition.groups #=> Array
resp.workteam.member_definitions[0].oidc_member_definition.groups[0] #=> String
resp.workteam.workteam_arn #=> String
resp.workteam.workforce_arn #=> String
resp.workteam.product_listing_ids #=> Array
resp.workteam.product_listing_ids[0] #=> String
resp.workteam.description #=> String
resp.workteam.sub_domain #=> String
resp.workteam.create_date #=> Time
resp.workteam.last_updated_date #=> Time
resp.workteam.notification_configuration.notification_topic_arn #=> String
resp.workteam.worker_access_configuration.s3_presign.iam_policy_constraints.source_ip #=> String, one of "Enabled", "Disabled"
resp.workteam.worker_access_configuration.s3_presign.iam_policy_constraints.vpc_source_ip #=> String, one of "Enabled", "Disabled"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_name (required, String) —

    The name of the work team to update.

  • :member_definitions (Array<Types::MemberDefinition>) —

    A list of ‘MemberDefinition` objects that contains objects that identify the workers that make up the work team.

    Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use ‘CognitoMemberDefinition`. For workforces created using your own OIDC identity provider (IdP) use `OidcMemberDefinition`. You should not provide input for both of these parameters in a single request.

    For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito *user groups* within the user pool used to create a workforce. All of the ‘CognitoMemberDefinition` objects that make up the member definition must have the same `ClientId` and `UserPool` values. To add a Amazon Cognito user group to an existing worker pool, see [Adding groups to a User Pool](). For more information about user pools, see [Amazon Cognito User Pools].

    For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in ‘OidcMemberDefinition` by listing those groups in `Groups`. Be aware that user groups that are already in the work team must also be listed in `Groups` when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.

    [1]: docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html

  • :description (String) —

    An updated description for the work team.

  • :notification_configuration (Types::NotificationConfiguration) —

    Configures SNS topic notifications for available or expiring work items

  • :worker_access_configuration (Types::WorkerAccessConfiguration) —

    Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

Returns:

See Also:



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27717

def update_workteam(params = {}, options = {})
  req = build_request(:update_workteam, params)
  req.send_request(options)
end

#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean

Polls an API operation until a resource enters a desired state.

## Basic Usage

A waiter will call an API operation until:

  • It is successful

  • It enters a terminal state

  • It makes the maximum number of attempts

In between attempts, the waiter will sleep.

# polls in a loop, sleeping between attempts
client.wait_until(waiter_name, params)

## Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You can pass configuration as the final arguments hash.

# poll for ~25 seconds
client.wait_until(waiter_name, params, {
  max_attempts: 5,
  delay: 5,
})

## Callbacks

You can be notified before each polling attempt and before each delay. If you throw ‘:success` or `:failure` from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(waiter_name, params, {

  # disable max attempts
  max_attempts: nil,

  # poll for 1 hour, instead of a number of attempts
  before_wait: -> (attempts, response) do
    throw :failure if Time.now - started_at > 3600
  end
})

## Handling Errors

When a waiter is unsuccessful, it will raise an error. All of the failure errors extend from Waiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end

## Valid Waiters

The following table lists the valid waiter names, the operations they call, and the default ‘:delay` and `:max_attempts` values.

| waiter_name | params | :delay | :max_attempts | | ———————————– | ———————————– | ——– | ————- | | endpoint_deleted | #describe_endpoint | 30 | 60 | | endpoint_in_service | #describe_endpoint | 30 | 120 | | image_created | #describe_image | 60 | 60 | | image_deleted | #describe_image | 60 | 60 | | image_updated | #describe_image | 60 | 60 | | image_version_created | #describe_image_version | 60 | 60 | | image_version_deleted | #describe_image_version | 60 | 60 | | notebook_instance_deleted | #describe_notebook_instance | 30 | 60 | | notebook_instance_in_service | #describe_notebook_instance | 30 | 60 | | notebook_instance_stopped | #describe_notebook_instance | 30 | 60 | | processing_job_completed_or_stopped | #describe_processing_job | 60 | 60 | | training_job_completed_or_stopped | #describe_training_job | 120 | 180 | | transform_job_completed_or_stopped | #describe_transform_job | 60 | 60 |

Parameters:

  • waiter_name (Symbol)
  • params (Hash) (defaults to: {}) —

    ({})

  • options (Hash) (defaults to: {}) —

    ({})

Options Hash (options):

  • :max_attempts (Integer)
  • :delay (Integer)
  • :before_attempt (Proc)
  • :before_wait (Proc)

Yields:

  • (w.waiter)

Returns:

  • (Boolean) —

    Returns ‘true` if the waiter was successful.

Raises:

  • (Errors::FailureStateError) —

    Raised when the waiter terminates because the waiter has entered a state that it will not transition out of, preventing success.

  • (Errors::TooManyAttemptsError) —

    Raised when the configured maximum number of attempts have been made, and the waiter is not yet successful.

  • (Errors::UnexpectedError) —

    Raised when an error is encounted while polling for a resource that is not expected.

  • (Errors::NoSuchWaiterError) —

    Raised when you request to wait for an unknown state.



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# File 'lib/aws-sdk-sagemaker/client.rb', line 27844

def wait_until(waiter_name, params = {}, options = {})
  w = waiter(waiter_name, options)
  yield(w.waiter) if block_given? # deprecated
  w.wait(params)
end

#waiter_names ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Deprecated. 


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# File 'lib/aws-sdk-sagemaker/client.rb', line 27852

def waiter_names
  waiters.keys
end