Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::DedicatedResources (v0.60.0)

Version 0.60.0

• 0.61.0 (latest)
• 0.60.0
• 0.59.0
• 0.58.0
• 0.57.0
• 0.56.0
• 0.55.0
• 0.54.0
• 0.53.0
• 0.52.0
• 0.51.0
• 0.50.0
• 0.49.0
• 0.48.0
• 0.47.0
• 0.46.0
• 0.45.0
• 0.44.0
• 0.43.0
• 0.42.0
• 0.41.0
• 0.40.0
• 0.39.0
• 0.38.0
• 0.37.0
• 0.36.0
• 0.35.0
• 0.34.0
• 0.33.0
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.0
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.1
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0
• 0.1.0

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::DedicatedResources.

A description of resources that are dedicated to a DeployedModel, and that need a higher degree of manual configuration.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#autoscaling_metric_specs

def autoscaling_metric_specs() -> ::Array<::Google::Cloud::AIPlatform::V1::AutoscalingMetricSpec>

Returns

• (::Array<::Google::Cloud::AIPlatform::V1::AutoscalingMetricSpec>) — Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric.

  If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics.

  If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set.

  For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.

#autoscaling_metric_specs=

def autoscaling_metric_specs=(value) -> ::Array<::Google::Cloud::AIPlatform::V1::AutoscalingMetricSpec>

Parameter

• value (::Array<::Google::Cloud::AIPlatform::V1::AutoscalingMetricSpec>) — Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric.

  If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics.

  If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set.

  For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.

Returns

• (::Array<::Google::Cloud::AIPlatform::V1::AutoscalingMetricSpec>) — Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric.

  If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics.

  If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set.

  For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.

#machine_spec

def machine_spec() -> ::Google::Cloud::AIPlatform::V1::MachineSpec

Returns

• (::Google::Cloud::AIPlatform::V1::MachineSpec) — Required. Immutable. The specification of a single machine used by the prediction.

#machine_spec=

def machine_spec=(value) -> ::Google::Cloud::AIPlatform::V1::MachineSpec

Parameter

• value (::Google::Cloud::AIPlatform::V1::MachineSpec) — Required. Immutable. The specification of a single machine used by the prediction.

Returns

• (::Google::Cloud::AIPlatform::V1::MachineSpec) — Required. Immutable. The specification of a single machine used by the prediction.

#max_replica_count

def max_replica_count() -> ::Integer

Returns

• (::Integer) — Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value.

  The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).

#max_replica_count=

def max_replica_count=(value) -> ::Integer

Parameter

• value (::Integer) — Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value.

  The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).

Returns

• (::Integer) — Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value.

  The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).

#min_replica_count

def min_replica_count() -> ::Integer

Returns

• (::Integer) — Required. Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1.

  If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.

#min_replica_count=

def min_replica_count=(value) -> ::Integer

Parameter

• value (::Integer) — Required. Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1.

  If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.

Returns

• (::Integer) — Required. Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1.

  If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.

#required_replica_count

def required_replica_count() -> ::Integer

Returns

• (::Integer) — Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial model deployment/mutation is desired. If set, the model deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.

#required_replica_count=

def required_replica_count=(value) -> ::Integer

Parameter

• value (::Integer) — Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial model deployment/mutation is desired. If set, the model deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.

Returns

• (::Integer) — Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial model deployment/mutation is desired. If set, the model deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.

#spot

def spot() -> ::Boolean

Returns

• (::Boolean) — Optional. If true, schedule the deployment workload on spot VMs.

#spot=

def spot=(value) -> ::Boolean

Parameter

• value (::Boolean) — Optional. If true, schedule the deployment workload on spot VMs.

Returns

• (::Boolean) — Optional. If true, schedule the deployment workload on spot VMs.