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.



30200
30201
30202
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30200

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) 


30142
30143
30144
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30142

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) 


30148
30149
30150
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30148

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) 


30161
30162
30163
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30161

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) 


30172
30173
30174
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30172

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) 


30179
30180
30181
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30179

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) 


30185
30186
30187
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30185

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:



30192
30193
30194
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30192

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) 


30198
30199
30200
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30198

def tpu_topology
  @tpu_topology
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



30205
30206
30207
30208
30209
30210
30211
30212
30213
30214
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30205

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