public final class DedicatedResources extends GeneratedMessageV3 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
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
public static final int AUTOSCALING_METRIC_SPECS_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
public static final int MACHINE_SPEC_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
public static final int MAX_REPLICA_COUNT_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
public static final int MIN_REPLICA_COUNT_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
Static Methods
public static DedicatedResources getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static DedicatedResources.Builder newBuilder()
public static DedicatedResources.Builder newBuilder(DedicatedResources prototype)
public static DedicatedResources parseDelimitedFrom(InputStream input)
public static DedicatedResources parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static DedicatedResources parseFrom(byte[] data)
| Parameter |
|---|
| Name | Description |
data | byte[]
|
public static DedicatedResources parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static DedicatedResources parseFrom(ByteString data)
public static DedicatedResources parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static DedicatedResources parseFrom(CodedInputStream input)
public static DedicatedResources parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static DedicatedResources parseFrom(InputStream input)
public static DedicatedResources parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static DedicatedResources parseFrom(ByteBuffer data)
public static DedicatedResources parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<DedicatedResources> parser()
Methods
public boolean equals(Object obj)
| Parameter |
|---|
| Name | Description |
obj | Object
|
Overrides
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
|
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 | |
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];
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
|
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> | |
public DedicatedResources getDefaultInstanceForType()
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];
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];
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.
|
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.
|
public Parser<DedicatedResources> getParserForType()
Overrides
public int getSerializedSize()
| Returns |
|---|
| Type | Description |
int | |
Overrides
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.
|
| Returns |
|---|
| Type | Description |
int | |
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public DedicatedResources.Builder newBuilderForType()
protected DedicatedResources.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Overrides
public DedicatedResources.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
