public static final class DedicatedResources.Builder extends com.google.protobuf.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.DedicatedResourcesgetAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setUnknownFieldSetBuilder, setUnknownFieldsProto3findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toStringaddAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageExceptionequals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitpublic static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder clear()
clear in interface com.google.protobuf.Message.Builderclear in interface com.google.protobuf.MessageLite.Builderclear in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
getDescriptorForType in interface com.google.protobuf.Message.BuildergetDescriptorForType in interface com.google.protobuf.MessageOrBuildergetDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources getDefaultInstanceForType()
getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuildergetDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilderpublic DedicatedResources build()
build in interface com.google.protobuf.Message.Builderbuild in interface com.google.protobuf.MessageLite.Builderpublic DedicatedResources buildPartial()
buildPartial in interface com.google.protobuf.Message.BuilderbuildPartial in interface com.google.protobuf.MessageLite.Builderpublic DedicatedResources.Builder clone()
clone in interface com.google.protobuf.Message.Builderclone in interface com.google.protobuf.MessageLite.Builderclone in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
setField in interface com.google.protobuf.Message.BuildersetField in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
clearField in interface com.google.protobuf.Message.BuilderclearField in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
clearOneof in interface com.google.protobuf.Message.BuilderclearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
setRepeatedField in interface com.google.protobuf.Message.BuildersetRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
addRepeatedField in interface com.google.protobuf.Message.BuilderaddRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder mergeFrom(com.google.protobuf.Message other)
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<DedicatedResources.Builder>public DedicatedResources.Builder mergeFrom(DedicatedResources other)
public final boolean isInitialized()
isInitialized in interface com.google.protobuf.MessageLiteOrBuilderisInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public DedicatedResources.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in interface com.google.protobuf.MessageLite.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<DedicatedResources.Builder>IOExceptionpublic 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];
hasMachineSpec in interface DedicatedResourcesOrBuilderpublic 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];
getMachineSpec in interface DedicatedResourcesOrBuilderpublic 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];
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];
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];
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];
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];
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];
getMachineSpecOrBuilder in interface DedicatedResourcesOrBuilderpublic 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];
getMinReplicaCount in interface DedicatedResourcesOrBuilderpublic 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];
value - The minReplicaCount to set.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];
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][google.cloud.aiplatform.v1.DedicatedResources.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];getMaxReplicaCount in interface DedicatedResourcesOrBuilderpublic 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][google.cloud.aiplatform.v1.DedicatedResources.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];value - The maxReplicaCount to set.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][google.cloud.aiplatform.v1.DedicatedResources.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];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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
getAutoscalingMetricSpecsList in interface DedicatedResourcesOrBuilderpublic 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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
getAutoscalingMetricSpecsCount in interface DedicatedResourcesOrBuilderpublic 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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
getAutoscalingMetricSpecs in interface DedicatedResourcesOrBuilderpublic 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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
getAutoscalingMetricSpecsOrBuilder in interface DedicatedResourcesOrBuilderpublic 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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
getAutoscalingMetricSpecsOrBuilderList in interface DedicatedResourcesOrBuilderpublic 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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to `80`.
repeated .google.cloud.aiplatform.v1.AutoscalingMetricSpec autoscaling_metric_specs = 4 [(.google.api.field_behavior) = IMMUTABLE];
public final DedicatedResources.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
setUnknownFields in interface com.google.protobuf.Message.BuildersetUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>public final DedicatedResources.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
mergeUnknownFields in interface com.google.protobuf.Message.BuildermergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<DedicatedResources.Builder>Copyright © 2024 Google LLC. All rights reserved.