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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>-
(::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/utilizationand autoscaling_metric_specs.target to80.
#autoscaling_metric_specs=
def autoscaling_metric_specs=(value) -> ::Array<::Google::Cloud::AIPlatform::V1::AutoscalingMetricSpec>-
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/utilizationand autoscaling_metric_specs.target to80.
-
(::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/utilizationand autoscaling_metric_specs.target to80.
#machine_spec
def machine_spec() -> ::Google::Cloud::AIPlatform::V1::MachineSpec- (::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- value (::Google::Cloud::AIPlatform::V1::MachineSpec) — Required. Immutable. The specification of a single machine used by the prediction.
- (::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-
(::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-
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).
-
(::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-
(::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-
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.
-
(::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.
#spot
def spot() -> ::Boolean- (::Boolean) — Optional. If true, schedule the deployment workload on spot VMs.
#spot=
def spot=(value) -> ::Boolean- value (::Boolean) — Optional. If true, schedule the deployment workload on spot VMs.
- (::Boolean) — Optional. If true, schedule the deployment workload on spot VMs.