Class DedicatedResources (1.133.0)

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DedicatedResources(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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

Attributes
Name	Description
machine_spec	google.cloud.aiplatform_v1beta1.types.MachineSpec Required. Immutable. The specification of a single machine being used.
min_replica_count	int Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
max_replica_count	int Immutable. The maximum number of replicas that 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 to that many replicas is guaranteed (barring service outages). If traffic 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).
required_replica_count	int Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the 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.
initial_replica_count	int Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count.
autoscaling_metric_specs	MutableSequence[google.cloud.aiplatform_v1beta1.types.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.
spot	bool Optional. If true, schedule the deployment workload on `spot VMs
flex_start	google.cloud.aiplatform_v1beta1.types.FlexStart Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
scale_to_zero_spec	google.cloud.aiplatform_v1beta1.types.DedicatedResources.ScaleToZeroSpec Optional. Specification for scale-to-zero feature.

Classes

ScaleToZeroSpec

ScaleToZeroSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Specification for scale-to-zero feature.

Methods

DedicatedResources

DedicatedResources(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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