Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1MachineSpec

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb

Overview

Specification of a single machine.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1MachineSpec

Returns a new instance of GoogleCloudAiplatformV1beta1MachineSpec.



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25328

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

Instance Attribute Details

#accelerator_count ⇒ Fixnum

The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized- machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co- schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co- scheduling will not be enabled. Corresponds to the JSON property acceleratorCount

Returns:

  • (Fixnum) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25270

def accelerator_count
  @accelerator_count
end

#accelerator_type ⇒ String

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. Corresponds to the JSON property acceleratorType

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25276

def accelerator_type
  @accelerator_type
end

#gpu_partition_size ⇒ String

Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to Nvidia GPU Partitioning for the available partition sizes. If set, the accelerator_count should be set to 1. Corresponds to the JSON property gpuPartitionSize

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25289

def gpu_partition_size
  @gpu_partition_size
end

#machine_type ⇒ String

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. Corresponds to the JSON property machineType

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25300

def machine_type
  @machine_type
end

#min_gpu_driver_version ⇒ String

Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. Corresponds to the JSON property minGpuDriverVersion

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25307

def min_gpu_driver_version
  @min_gpu_driver_version
end

#multihost_gpu_node_count ⇒ Fixnum

Optional. Immutable. The number of nodes per replica for multihost GPU deployments. Corresponds to the JSON property multihostGpuNodeCount

Returns:

  • (Fixnum) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25313

def multihost_gpu_node_count
  @multihost_gpu_node_count
end

#reservation_affinity ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ReservationAffinity

A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. Corresponds to the JSON property reservationAffinity

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25320

def reservation_affinity
  @reservation_affinity
end

#tpu_topology ⇒ String

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). Corresponds to the JSON property tpuTopology

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25326

def tpu_topology
  @tpu_topology
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 25333

def update!(**args)
  @accelerator_count = args[:accelerator_count] if args.key?(:accelerator_count)
  @accelerator_type = args[:accelerator_type] if args.key?(:accelerator_type)
  @gpu_partition_size = args[:gpu_partition_size] if args.key?(:gpu_partition_size)
  @machine_type = args[:machine_type] if args.key?(:machine_type)
  @min_gpu_driver_version = args[:min_gpu_driver_version] if args.key?(:min_gpu_driver_version)
  @multihost_gpu_node_count = args[:multihost_gpu_node_count] if args.key?(:multihost_gpu_node_count)
  @reservation_affinity = args[:reservation_affinity] if args.key?(:reservation_affinity)
  @tpu_topology = args[:tpu_topology] if args.key?(:tpu_topology)
end