Class DedicatedResources.Builder (3.56.0) 

public static final class DedicatedResources.Builder extends GeneratedMessageV3.Builder<DedicatedResources.Builder> implements DedicatedResourcesOrBuilder

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

Protobuf type google.cloud.aiplatform.v1.DedicatedResources

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > DedicatedResources.Builder

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
Type Description
Descriptor

Methods

addAllAutoscalingMetricSpecs(Iterable<? extends AutoscalingMetricSpec> values)

public DedicatedResources.Builder addAllAutoscalingMetricSpecs(Iterable<? extends AutoscalingMetricSpec> values)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
values Iterable<? extends com.google.cloud.aiplatform.v1.AutoscalingMetricSpec>
Returns
Type Description
DedicatedResources.Builder

addAutoscalingMetricSpecs(AutoscalingMetricSpec value)

public DedicatedResources.Builder addAutoscalingMetricSpecs(AutoscalingMetricSpec value)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
value AutoscalingMetricSpec
Returns
Type Description
DedicatedResources.Builder

addAutoscalingMetricSpecs(AutoscalingMetricSpec.Builder builderForValue)

public DedicatedResources.Builder addAutoscalingMetricSpecs(AutoscalingMetricSpec.Builder builderForValue)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
builderForValue AutoscalingMetricSpec.Builder
Returns
Type Description
DedicatedResources.Builder

addAutoscalingMetricSpecs(int index, AutoscalingMetricSpec value)

public DedicatedResources.Builder addAutoscalingMetricSpecs(int index, AutoscalingMetricSpec value)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameters
Name Description
index int
value AutoscalingMetricSpec
Returns
Type Description
DedicatedResources.Builder

addAutoscalingMetricSpecs(int index, AutoscalingMetricSpec.Builder builderForValue)

public DedicatedResources.Builder addAutoscalingMetricSpecs(int index, AutoscalingMetricSpec.Builder builderForValue)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameters
Name Description
index int
builderForValue AutoscalingMetricSpec.Builder
Returns
Type Description
DedicatedResources.Builder

addAutoscalingMetricSpecsBuilder()

public AutoscalingMetricSpec.Builder addAutoscalingMetricSpecsBuilder()

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
AutoscalingMetricSpec.Builder

addAutoscalingMetricSpecsBuilder(int index)

public AutoscalingMetricSpec.Builder addAutoscalingMetricSpecsBuilder(int index)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
index int
Returns
Type Description
AutoscalingMetricSpec.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public DedicatedResources.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
Name Description
field FieldDescriptor
value Object
Returns
Type Description
DedicatedResources.Builder
Overrides

build()

public DedicatedResources build()
Returns
Type Description
DedicatedResources

buildPartial()

public DedicatedResources buildPartial()
Returns
Type Description
DedicatedResources

clear()

public DedicatedResources.Builder clear()
Returns
Type Description
DedicatedResources.Builder
Overrides

clearAutoscalingMetricSpecs()

public DedicatedResources.Builder clearAutoscalingMetricSpecs()

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
DedicatedResources.Builder

clearField(Descriptors.FieldDescriptor field)

public DedicatedResources.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
Name Description
field FieldDescriptor
Returns
Type Description
DedicatedResources.Builder
Overrides

clearMachineSpec()

public DedicatedResources.Builder clearMachineSpec()

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
DedicatedResources.Builder

clearMaxReplicaCount()

public DedicatedResources.Builder clearMaxReplicaCount()

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).

int32 max_replica_count = 3 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

clearMinReplicaCount()

public DedicatedResources.Builder clearMinReplicaCount()

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.

int32 min_replica_count = 2 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public DedicatedResources.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Name Description
oneof OneofDescriptor
Returns
Type Description
DedicatedResources.Builder
Overrides

clearRequiredReplicaCount()

public DedicatedResources.Builder clearRequiredReplicaCount()

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.

int32 required_replica_count = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

clearSpot()

public DedicatedResources.Builder clearSpot()

Optional. If true, schedule the deployment workload on spot VMs.

bool spot = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

clone()

public DedicatedResources.Builder clone()
Returns
Type Description
DedicatedResources.Builder
Overrides

getAutoscalingMetricSpecs(int index)

public AutoscalingMetricSpec getAutoscalingMetricSpecs(int index)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
index int
Returns
Type Description
AutoscalingMetricSpec

getAutoscalingMetricSpecsBuilder(int index)

public AutoscalingMetricSpec.Builder getAutoscalingMetricSpecsBuilder(int index)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
index int
Returns
Type Description
AutoscalingMetricSpec.Builder

getAutoscalingMetricSpecsBuilderList()

public List<AutoscalingMetricSpec.Builder> getAutoscalingMetricSpecsBuilderList()

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
List<Builder>

getAutoscalingMetricSpecsCount()

public int getAutoscalingMetricSpecsCount()

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
int

getAutoscalingMetricSpecsList()

public List<AutoscalingMetricSpec> getAutoscalingMetricSpecsList()

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
List<AutoscalingMetricSpec>

getAutoscalingMetricSpecsOrBuilder(int index)

public AutoscalingMetricSpecOrBuilder getAutoscalingMetricSpecsOrBuilder(int index)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
index int
Returns
Type Description
AutoscalingMetricSpecOrBuilder

getAutoscalingMetricSpecsOrBuilderList()

public List<? extends AutoscalingMetricSpecOrBuilder> getAutoscalingMetricSpecsOrBuilderList()

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
List<? extends com.google.cloud.aiplatform.v1.AutoscalingMetricSpecOrBuilder>

getDefaultInstanceForType()

public DedicatedResources getDefaultInstanceForType()
Returns
Type Description
DedicatedResources

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
Type Description
Descriptor
Overrides

getMachineSpec()

public MachineSpec getMachineSpec()

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
MachineSpec

The machineSpec.

getMachineSpecBuilder()

public MachineSpec.Builder getMachineSpecBuilder()

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
MachineSpec.Builder

getMachineSpecOrBuilder()

public MachineSpecOrBuilder getMachineSpecOrBuilder()

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
MachineSpecOrBuilder

getMaxReplicaCount()

public int getMaxReplicaCount()

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).

int32 max_replica_count = 3 [(.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
int

The maxReplicaCount.

getMinReplicaCount()

public int getMinReplicaCount()

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.

int32 min_replica_count = 2 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
int

The minReplicaCount.

getRequiredReplicaCount()

public int getRequiredReplicaCount()

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.

int32 required_replica_count = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
int

The requiredReplicaCount.

getSpot()

public boolean getSpot()

Optional. If true, schedule the deployment workload on spot VMs.

bool spot = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
boolean

The spot.

hasMachineSpec()

public boolean hasMachineSpec()

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Returns
Type Description
boolean

Whether the machineSpec field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Type Description
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

mergeFrom(DedicatedResources other)

public DedicatedResources.Builder mergeFrom(DedicatedResources other)
Parameter
Name Description
other DedicatedResources
Returns
Type Description
DedicatedResources.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public DedicatedResources.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
DedicatedResources.Builder
Overrides
Exceptions
Type Description
IOException

mergeFrom(Message other)

public DedicatedResources.Builder mergeFrom(Message other)
Parameter
Name Description
other Message
Returns
Type Description
DedicatedResources.Builder
Overrides

mergeMachineSpec(MachineSpec value)

public DedicatedResources.Builder mergeMachineSpec(MachineSpec value)

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
value MachineSpec
Returns
Type Description
DedicatedResources.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final DedicatedResources.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
DedicatedResources.Builder
Overrides

removeAutoscalingMetricSpecs(int index)

public DedicatedResources.Builder removeAutoscalingMetricSpecs(int index)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
index int
Returns
Type Description
DedicatedResources.Builder

setAutoscalingMetricSpecs(int index, AutoscalingMetricSpec value)

public DedicatedResources.Builder setAutoscalingMetricSpecs(int index, AutoscalingMetricSpec value)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameters
Name Description
index int
value AutoscalingMetricSpec
Returns
Type Description
DedicatedResources.Builder

setAutoscalingMetricSpecs(int index, AutoscalingMetricSpec.Builder builderForValue)

public DedicatedResources.Builder setAutoscalingMetricSpecs(int index, AutoscalingMetricSpec.Builder builderForValue)

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.

repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];

Parameters
Name Description
index int
builderForValue AutoscalingMetricSpec.Builder
Returns
Type Description
DedicatedResources.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public DedicatedResources.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
Name Description
field FieldDescriptor
value Object
Returns
Type Description
DedicatedResources.Builder
Overrides

setMachineSpec(MachineSpec value)

public DedicatedResources.Builder setMachineSpec(MachineSpec value)

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
value MachineSpec
Returns
Type Description
DedicatedResources.Builder

setMachineSpec(MachineSpec.Builder builderForValue)

public DedicatedResources.Builder setMachineSpec(MachineSpec.Builder builderForValue)

Required. Immutable. The specification of a single machine used by the prediction.

.google.cloud.aiplatform.v1.MachineSpec machine_spec = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
builderForValue MachineSpec.Builder
Returns
Type Description
DedicatedResources.Builder

setMaxReplicaCount(int value)

public DedicatedResources.Builder setMaxReplicaCount(int value)

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).

int32 max_replica_count = 3 [(.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
value int

The maxReplicaCount to set.
Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

setMinReplicaCount(int value)

public DedicatedResources.Builder setMinReplicaCount(int value)

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.

int32 min_replica_count = 2 [(.google.api.field_behavior) = REQUIRED, (.google.api.field_behavior) = IMMUTABLE];

Parameter
Name Description
value int

The minReplicaCount to set.
Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public DedicatedResources.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
Name Description
field FieldDescriptor
index int
value Object
Returns
Type Description
DedicatedResources.Builder
Overrides

setRequiredReplicaCount(int value)

public DedicatedResources.Builder setRequiredReplicaCount(int value)

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.

int32 required_replica_count = 9 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name Description
value int

The requiredReplicaCount to set.
Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

setSpot(boolean value)

public DedicatedResources.Builder setSpot(boolean value)

Optional. If true, schedule the deployment workload on spot VMs.

bool spot = 5 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name Description
value boolean

The spot to set.
Returns
Type Description
DedicatedResources.Builder

This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final DedicatedResources.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
DedicatedResources.Builder
Overrides