public static final class DedicatedResources.Builder extends GeneratedMessageV3.Builder<DedicatedResources.Builder> implements DedicatedResourcesOrBuilderA description of resources that are dedicated to a DeployedModel, and that need a higher degree of manual configuration.
Protobuf type google.cloud.vertexai.v1.DedicatedResources
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > DedicatedResources.BuilderImplements
DedicatedResourcesOrBuilderStatic 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.vertexai.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
| Parameter | |
|---|---|
| Name | Description |
values |
Iterable<? extends com.google.cloud.vertexai.api.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.vertexai.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.vertexai.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.vertexai.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.vertexai.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.vertexai.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.vertexai.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 |
|
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 |
|
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.vertexai.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 |
|
clearMachineSpec()
public DedicatedResources.Builder clearMachineSpec()Required. Immutable. The specification of a single machine used by the prediction.
.google.cloud.vertexai.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 |
|
clone()
public DedicatedResources.Builder clone()| Returns | |
|---|---|
| Type | Description |
DedicatedResources.Builder |
|
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.vertexai.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.vertexai.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.vertexai.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.vertexai.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.vertexai.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.vertexai.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.vertexai.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
| Returns | |
|---|---|
| Type | Description |
List<? extends com.google.cloud.vertexai.api.AutoscalingMetricSpecOrBuilder> |
|
getDefaultInstanceForType()
public DedicatedResources getDefaultInstanceForType()| Returns | |
|---|---|
| Type | Description |
DedicatedResources |
|
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Returns | |
|---|---|
| Type | Description |
Descriptor |
|
getMachineSpec()
public MachineSpec getMachineSpec()Required. Immutable. The specification of a single machine used by the prediction.
.google.cloud.vertexai.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.vertexai.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.vertexai.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. |
hasMachineSpec()
public boolean hasMachineSpec()Required. Immutable. The specification of a single machine used by the prediction.
.google.cloud.vertexai.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 |
|
isInitialized()
public final boolean isInitialized()| Returns | |
|---|---|
| Type | Description |
boolean |
|
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 |
|
| Exceptions | |
|---|---|
| Type | Description |
IOException |
|
mergeFrom(Message other)
public DedicatedResources.Builder mergeFrom(Message other)| Parameter | |
|---|---|
| Name | Description |
other |
Message |
| Returns | |
|---|---|
| Type | Description |
DedicatedResources.Builder |
|
mergeMachineSpec(MachineSpec value)
public DedicatedResources.Builder mergeMachineSpec(MachineSpec value)Required. Immutable. The specification of a single machine used by the prediction.
.google.cloud.vertexai.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 |
|
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.vertexai.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.vertexai.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.vertexai.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 |
|
setMachineSpec(MachineSpec value)
public DedicatedResources.Builder setMachineSpec(MachineSpec value)Required. Immutable. The specification of a single machine used by the prediction.
.google.cloud.vertexai.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.vertexai.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 |
intThe 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 |
intThe 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 |
|
setUnknownFields(UnknownFieldSet unknownFields)
public final DedicatedResources.Builder setUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields |
UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
DedicatedResources.Builder |
|