Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1Model

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

A trained machine learning Model.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1Model

Returns a new instance of GoogleCloudAiplatformV1Model.



17960
17961
17962
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17960

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#artifact_uri ⇒ String

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models. Corresponds to the JSON property artifactUri

Returns:

  • (String) 


17715
17716
17717
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17715

def artifact_uri
  @artifact_uri
end

#base_model_source ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelBaseModelSource

User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models. Corresponds to the JSON property baseModelSource

Returns:



17721
17722
17723
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17721

def base_model_source
  @base_model_source
end

#checkpoints ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1Checkpoint>

Optional. Output only. The checkpoints of the model. Corresponds to the JSON property checkpoints

Returns:



17726
17727
17728
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17726

def checkpoints
  @checkpoints
end

#container_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelContainerSpec

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification. Corresponds to the JSON property containerSpec

Returns:



17734
17735
17736
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17734

def container_spec
  @container_spec
end

#create_time ⇒ String

Output only. Timestamp when this Model was uploaded into Vertex AI. Corresponds to the JSON property createTime

Returns:

  • (String) 


17739
17740
17741
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17739

def create_time
  @create_time
end

#data_stats ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDataStats

Stats of data used for train or evaluate the Model. Corresponds to the JSON property dataStats

Returns:



17744
17745
17746
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17744

def data_stats
  @data_stats
end

#default_checkpoint_id ⇒ String

The default checkpoint id of a model version. Corresponds to the JSON property defaultCheckpointId

Returns:

  • (String) 


17749
17750
17751
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17749

def default_checkpoint_id
  @default_checkpoint_id
end

#deployed_models ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1DeployedModelRef>

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations. Corresponds to the JSON property deployedModels

Returns:



17755
17756
17757
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17755

def deployed_models
  @deployed_models
end

#description ⇒ String

The description of the Model. Corresponds to the JSON property description

Returns:

  • (String) 


17760
17761
17762
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17760

def description
  @description
end

#display_name ⇒ String

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters. Corresponds to the JSON property displayName

Returns:

  • (String) 


17766
17767
17768
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17766

def display_name
  @display_name
end

#encryption_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top- level resource. Corresponds to the JSON property encryptionSpec

Returns:



17772
17773
17774
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17772

def encryption_spec
  @encryption_spec
end

#etag ⇒ String

Used to perform consistent read-modify-write updates. If not set, a blind " overwrite" update happens. Corresponds to the JSON property etag

Returns:

  • (String) 


17778
17779
17780
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17778

def etag
  @etag
end

#explanation_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ExplanationSpec

Specification of Model explanation. Corresponds to the JSON property explanationSpec

Returns:



17783
17784
17785
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17783

def explanation_spec
  @explanation_spec
end

#labels ⇒ Hash<String,String>

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Corresponds to the JSON property labels

Returns:

  • (Hash<String,String>) 


17792
17793
17794
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17792

def labels
  @labels
end

#metadata ⇒ Object

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information. Corresponds to the JSON property metadata

Returns:

  • (Object) 


17799
17800
17801
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17799

def metadata
  @metadata
end

#metadata_artifact ⇒ String

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/project/locations/location/metadataStores/metadata_store/ artifacts/artifact`. Corresponds to the JSON propertymetadataArtifact`

Returns:

  • (String) 


17807
17808
17809
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17807

def metadata_artifact
  @metadata_artifact
end

#metadata_schema_uri ⇒ String

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. Corresponds to the JSON property metadataSchemaUri

Returns:

  • (String) 


17820
17821
17822
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17820

def metadata_schema_uri
  @metadata_schema_uri
end

#model_source_info ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelSourceInfo

Detail description of the source information of the model. Corresponds to the JSON property modelSourceInfo

Returns:



17825
17826
17827
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17825

def model_source_info
  @model_source_info
end

#name ⇒ String

The resource name of the Model. Corresponds to the JSON property name

Returns:

  • (String) 


17830
17831
17832
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17830

def name
  @name
end

#original_model_info ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelOriginalModelInfo

Contains information about the original Model if this Model is a copy. Corresponds to the JSON property originalModelInfo

Returns:



17835
17836
17837
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17835

def original_model_info
  @original_model_info
end

#pipeline_job ⇒ String

Optional. This field is populated if the model is produced by a pipeline job. Corresponds to the JSON property pipelineJob

Returns:

  • (String) 


17840
17841
17842
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17840

def pipeline_job
  @pipeline_job
end

#predict_schemata ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1PredictSchemata

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob. Corresponds to the JSON property predictSchemata

Returns:



17846
17847
17848
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17846

def predict_schemata
  @predict_schemata
end

#satisfies_pzi ⇒ Boolean Also known as: satisfies_pzi?

Output only. Reserved for future use. Corresponds to the JSON property satisfiesPzi

Returns:

  • (Boolean) 


17851
17852
17853
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17851

def satisfies_pzi
  @satisfies_pzi
end

#satisfies_pzs ⇒ Boolean Also known as: satisfies_pzs?

Output only. Reserved for future use. Corresponds to the JSON property satisfiesPzs

Returns:

  • (Boolean) 


17857
17858
17859
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17857

def satisfies_pzs
  @satisfies_pzs
end

#supported_deployment_resources_types ⇒ Array<String>

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService. Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats. Corresponds to the JSON property supportedDeploymentResourcesTypes

Returns:

  • (Array<String>) 


17871
17872
17873
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17871

def supported_deployment_resources_types
  @supported_deployment_resources_types
end

#supported_export_formats ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelExportFormat>

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export. Corresponds to the JSON property supportedExportFormats

Returns:



17877
17878
17879
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17877

def supported_export_formats
  @supported_export_formats
end

#supported_input_storage_formats ⇒ Array<String>

Output only. The formats this Model supports in BatchPredictionJob. input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain. Corresponds to the JSON property supportedInputStorageFormats

Returns:

  • (Array<String>) 


17896
17897
17898
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17896

def supported_input_storage_formats
  @supported_input_storage_formats
end

#supported_output_storage_formats ⇒ Array<String>

Output only. The formats this Model supports in BatchPredictionJob. output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata. prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain. Corresponds to the JSON property supportedOutputStorageFormats

Returns:

  • (Array<String>) 


17914
17915
17916
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17914

def supported_output_storage_formats
  @supported_output_storage_formats
end

#training_pipeline ⇒ String

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any. Corresponds to the JSON property trainingPipeline

Returns:

  • (String) 


17920
17921
17922
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17920

def training_pipeline
  @training_pipeline
end

#update_time ⇒ String

Output only. Timestamp when this Model was most recently updated. Corresponds to the JSON property updateTime

Returns:

  • (String) 


17925
17926
17927
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17925

def update_time
  @update_time
end

#version_aliases ⇒ Array<String>

User provided version aliases so that a model version can be referenced via alias (i.e. projects/project/locations/location/models/model_id@ version_alias`instead of auto-generated version id (i.e.projects/project/ locations/location/models/model_id@version_id). The format is a-z0,126 [a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model. Corresponds to the JSON propertyversionAliases`

Returns:

  • (Array<String>) 


17936
17937
17938
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17936

def version_aliases
  @version_aliases
end

#version_create_time ⇒ String

Output only. Timestamp when this version was created. Corresponds to the JSON property versionCreateTime

Returns:

  • (String) 


17941
17942
17943
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17941

def version_create_time
  @version_create_time
end

#version_description ⇒ String

The description of this version. Corresponds to the JSON property versionDescription

Returns:

  • (String) 


17946
17947
17948
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17946

def version_description
  @version_description
end

#version_id ⇒ String

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation. Corresponds to the JSON property versionId

Returns:

  • (String) 


17953
17954
17955
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17953

def version_id
  @version_id
end

#version_update_time ⇒ String

Output only. Timestamp when this version was most recently updated. Corresponds to the JSON property versionUpdateTime

Returns:

  • (String) 


17958
17959
17960
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17958

def version_update_time
  @version_update_time
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



17965
17966
17967
17968
17969
17970
17971
17972
17973
17974
17975
17976
17977
17978
17979
17980
17981
17982
17983
17984
17985
17986
17987
17988
17989
17990
17991
17992
17993
17994
17995
17996
17997
17998
17999
18000
18001
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17965

def update!(**args)
  @artifact_uri = args[:artifact_uri] if args.key?(:artifact_uri)
  @base_model_source = args[:base_model_source] if args.key?(:base_model_source)
  @checkpoints = args[:checkpoints] if args.key?(:checkpoints)
  @container_spec = args[:container_spec] if args.key?(:container_spec)
  @create_time = args[:create_time] if args.key?(:create_time)
  @data_stats = args[:data_stats] if args.key?(:data_stats)
  @default_checkpoint_id = args[:default_checkpoint_id] if args.key?(:default_checkpoint_id)
  @deployed_models = args[:deployed_models] if args.key?(:deployed_models)
  @description = args[:description] if args.key?(:description)
  @display_name = args[:display_name] if args.key?(:display_name)
  @encryption_spec = args[:encryption_spec] if args.key?(:encryption_spec)
  @etag = args[:etag] if args.key?(:etag)
  @explanation_spec = args[:explanation_spec] if args.key?(:explanation_spec)
  @labels = args[:labels] if args.key?(:labels)
  @metadata = args[:metadata] if args.key?(:metadata)
  @metadata_artifact = args[:metadata_artifact] if args.key?(:metadata_artifact)
  @metadata_schema_uri = args[:metadata_schema_uri] if args.key?(:metadata_schema_uri)
  @model_source_info = args[:model_source_info] if args.key?(:model_source_info)
  @name = args[:name] if args.key?(:name)
  @original_model_info = args[:original_model_info] if args.key?(:original_model_info)
  @pipeline_job = args[:pipeline_job] if args.key?(:pipeline_job)
  @predict_schemata = args[:predict_schemata] if args.key?(:predict_schemata)
  @satisfies_pzi = args[:satisfies_pzi] if args.key?(:satisfies_pzi)
  @satisfies_pzs = args[:satisfies_pzs] if args.key?(:satisfies_pzs)
  @supported_deployment_resources_types = args[:supported_deployment_resources_types] if args.key?(:supported_deployment_resources_types)
  @supported_export_formats = args[:supported_export_formats] if args.key?(:supported_export_formats)
  @supported_input_storage_formats = args[:supported_input_storage_formats] if args.key?(:supported_input_storage_formats)
  @supported_output_storage_formats = args[:supported_output_storage_formats] if args.key?(:supported_output_storage_formats)
  @training_pipeline = args[:training_pipeline] if args.key?(:training_pipeline)
  @update_time = args[:update_time] if args.key?(:update_time)
  @version_aliases = args[:version_aliases] if args.key?(:version_aliases)
  @version_create_time = args[:version_create_time] if args.key?(:version_create_time)
  @version_description = args[:version_description] if args.key?(:version_description)
  @version_id = args[:version_id] if args.key?(:version_id)
  @version_update_time = args[:version_update_time] if args.key?(:version_update_time)
end