Package com.google.cloud.aiplatform.v1beta1 (3.8.0)

Version 3.8.0

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Beta

This library is covered by the Pre-GA Offerings Terms of the Terms of Service. Pre-GA libraries might have limited support, and changes to pre-GA libraries might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

A client to Vertex AI API

The interfaces provided are listed below, along with usage samples.

DatasetServiceClient

Service Description: The service that handles the CRUD of Vertex AI Dataset and its child resources.

Sample for DatasetServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (DatasetServiceClient datasetServiceClient = DatasetServiceClient.create()) {
   DatasetName name = DatasetName.of("[PROJECT]", "[LOCATION]", "[DATASET]");
   Dataset response = datasetServiceClient.getDataset(name);
 }
 

DeploymentResourcePoolServiceClient

Service Description: A service that manages the DeploymentResourcePool resource.

Sample for DeploymentResourcePoolServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (DeploymentResourcePoolServiceClient deploymentResourcePoolServiceClient =
     DeploymentResourcePoolServiceClient.create()) {
   DeploymentResourcePoolName name =
       DeploymentResourcePoolName.of("[PROJECT]", "[LOCATION]", "[DEPLOYMENT_RESOURCE_POOL]");
   DeploymentResourcePool response =
       deploymentResourcePoolServiceClient.getDeploymentResourcePool(name);
 }
 

EndpointServiceClient

Service Description: A service for managing Vertex AI's Endpoints.

Sample for EndpointServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (EndpointServiceClient endpointServiceClient = EndpointServiceClient.create()) {
   EndpointName name = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   Endpoint response = endpointServiceClient.getEndpoint(name);
 }
 

FeaturestoreOnlineServingServiceClient

Service Description: A service for serving online feature values.

Sample for FeaturestoreOnlineServingServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (FeaturestoreOnlineServingServiceClient featurestoreOnlineServingServiceClient =
     FeaturestoreOnlineServingServiceClient.create()) {
   EntityTypeName entityType =
       EntityTypeName.of("[PROJECT]", "[LOCATION]", "[FEATURESTORE]", "[ENTITY_TYPE]");
   ReadFeatureValuesResponse response =
       featurestoreOnlineServingServiceClient.readFeatureValues(entityType);
 }
 

FeaturestoreServiceClient

Service Description: The service that handles CRUD and List for resources for Featurestore.

Sample for FeaturestoreServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (FeaturestoreServiceClient featurestoreServiceClient = FeaturestoreServiceClient.create()) {
   FeaturestoreName name = FeaturestoreName.of("[PROJECT]", "[LOCATION]", "[FEATURESTORE]");
   Featurestore response = featurestoreServiceClient.getFeaturestore(name);
 }
 

IndexEndpointServiceClient

Service Description: A service for managing Vertex AI's IndexEndpoints.

Sample for IndexEndpointServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (IndexEndpointServiceClient indexEndpointServiceClient =
     IndexEndpointServiceClient.create()) {
   IndexEndpointName name = IndexEndpointName.of("[PROJECT]", "[LOCATION]", "[INDEX_ENDPOINT]");
   IndexEndpoint response = indexEndpointServiceClient.getIndexEndpoint(name);
 }
 

IndexServiceClient

Service Description: A service for creating and managing Vertex AI's Index resources.

Sample for IndexServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (IndexServiceClient indexServiceClient = IndexServiceClient.create()) {
   IndexName name = IndexName.of("[PROJECT]", "[LOCATION]", "[INDEX]");
   Index response = indexServiceClient.getIndex(name);
 }
 

JobServiceClient

Service Description: A service for creating and managing Vertex AI's jobs.

Sample for JobServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (JobServiceClient jobServiceClient = JobServiceClient.create()) {
   LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
   CustomJob customJob = CustomJob.newBuilder().build();
   CustomJob response = jobServiceClient.createCustomJob(parent, customJob);
 }
 

MetadataServiceClient

Service Description: Service for reading and writing metadata entries.

Sample for MetadataServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (MetadataServiceClient metadataServiceClient = MetadataServiceClient.create()) {
   MetadataStoreName name = MetadataStoreName.of("[PROJECT]", "[LOCATION]", "[METADATA_STORE]");
   MetadataStore response = metadataServiceClient.getMetadataStore(name);
 }
 

MigrationServiceClient

Service Description: A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

Sample for MigrationServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (MigrationServiceClient migrationServiceClient = MigrationServiceClient.create()) {
   GetLocationRequest request = GetLocationRequest.newBuilder().setName("name3373707").build();
   Location response = migrationServiceClient.getLocation(request);
 }
 

ModelServiceClient

Service Description: A service for managing Vertex AI's machine learning Models.

Sample for ModelServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (ModelServiceClient modelServiceClient = ModelServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   Model response = modelServiceClient.getModel(name);
 }
 

PipelineServiceClient

Service Description: A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

Sample for PipelineServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PipelineServiceClient pipelineServiceClient = PipelineServiceClient.create()) {
   LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
   TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().build();
   TrainingPipeline response =
       pipelineServiceClient.createTrainingPipeline(parent, trainingPipeline);
 }
 

PredictionServiceClient

Service Description: A service for online predictions and explanations.

Sample for PredictionServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().setBoolValue(true).build();
   PredictResponse response = predictionServiceClient.predict(endpoint, instances, parameters);
 }
 

SpecialistPoolServiceClient

Service Description: A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

Sample for SpecialistPoolServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (SpecialistPoolServiceClient specialistPoolServiceClient =
     SpecialistPoolServiceClient.create()) {
   SpecialistPoolName name =
       SpecialistPoolName.of("[PROJECT]", "[LOCATION]", "[SPECIALIST_POOL]");
   SpecialistPool response = specialistPoolServiceClient.getSpecialistPool(name);
 }
 

TensorboardServiceClient

Service Description: TensorboardService

Sample for TensorboardServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (TensorboardServiceClient tensorboardServiceClient = TensorboardServiceClient.create()) {
   TensorboardName name = TensorboardName.of("[PROJECT]", "[LOCATION]", "[TENSORBOARD]");
   Tensorboard response = tensorboardServiceClient.getTensorboard(name);
 }
 

VizierServiceClient

Service Description: Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

Sample for VizierServiceClient:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (VizierServiceClient vizierServiceClient = VizierServiceClient.create()) {
   LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
   Study study = Study.newBuilder().build();
   Study response = vizierServiceClient.createStudy(parent, study);
 }
 

Classes

AcceleratorTypeProto

ActiveLearningConfig

Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.

Protobuf type google.cloud.aiplatform.v1beta1.ActiveLearningConfig

ActiveLearningConfig.Builder

Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.

Protobuf type google.cloud.aiplatform.v1beta1.ActiveLearningConfig

AddContextArtifactsAndExecutionsRequest

Request message for MetadataService.AddContextArtifactsAndExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextArtifactsAndExecutionsRequest

AddContextArtifactsAndExecutionsRequest.Builder

Request message for MetadataService.AddContextArtifactsAndExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextArtifactsAndExecutionsRequest

AddContextArtifactsAndExecutionsResponse

Response message for MetadataService.AddContextArtifactsAndExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextArtifactsAndExecutionsResponse

AddContextArtifactsAndExecutionsResponse.Builder

Response message for MetadataService.AddContextArtifactsAndExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextArtifactsAndExecutionsResponse

AddContextChildrenRequest

Request message for MetadataService.AddContextChildren.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextChildrenRequest

AddContextChildrenRequest.Builder

Request message for MetadataService.AddContextChildren.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextChildrenRequest

AddContextChildrenResponse

Response message for MetadataService.AddContextChildren.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextChildrenResponse

AddContextChildrenResponse.Builder

Response message for MetadataService.AddContextChildren.

Protobuf type google.cloud.aiplatform.v1beta1.AddContextChildrenResponse

AddExecutionEventsRequest

Request message for MetadataService.AddExecutionEvents.

Protobuf type google.cloud.aiplatform.v1beta1.AddExecutionEventsRequest

AddExecutionEventsRequest.Builder

Request message for MetadataService.AddExecutionEvents.

Protobuf type google.cloud.aiplatform.v1beta1.AddExecutionEventsRequest

AddExecutionEventsResponse

Response message for MetadataService.AddExecutionEvents.

Protobuf type google.cloud.aiplatform.v1beta1.AddExecutionEventsResponse

AddExecutionEventsResponse.Builder

Response message for MetadataService.AddExecutionEvents.

Protobuf type google.cloud.aiplatform.v1beta1.AddExecutionEventsResponse

AddTrialMeasurementRequest

Request message for VizierService.AddTrialMeasurement.

Protobuf type google.cloud.aiplatform.v1beta1.AddTrialMeasurementRequest

AddTrialMeasurementRequest.Builder

Request message for VizierService.AddTrialMeasurement.

Protobuf type google.cloud.aiplatform.v1beta1.AddTrialMeasurementRequest

Annotation

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

Protobuf type google.cloud.aiplatform.v1beta1.Annotation

Annotation.Builder

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

Protobuf type google.cloud.aiplatform.v1beta1.Annotation

AnnotationProto

AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

Protobuf type google.cloud.aiplatform.v1beta1.AnnotationSpec

AnnotationSpec.Builder

Identifies a concept with which DataItems may be annotated with.

Protobuf type google.cloud.aiplatform.v1beta1.AnnotationSpec

AnnotationSpecName

AnnotationSpecName.Builder

Builder for projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}.

AnnotationSpecProto

Artifact

Instance of a general artifact.

Protobuf type google.cloud.aiplatform.v1beta1.Artifact

Artifact.Builder

Instance of a general artifact.

Protobuf type google.cloud.aiplatform.v1beta1.Artifact

ArtifactName

ArtifactName.Builder

Builder for projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.

ArtifactProto

Attribution

Attribution that explains a particular prediction output.

Protobuf type google.cloud.aiplatform.v1beta1.Attribution

Attribution.Builder

Attribution that explains a particular prediction output.

Protobuf type google.cloud.aiplatform.v1beta1.Attribution

AutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

Protobuf type google.cloud.aiplatform.v1beta1.AutomaticResources

AutomaticResources.Builder

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

Protobuf type google.cloud.aiplatform.v1beta1.AutomaticResources

AutoscalingMetricSpec

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

Protobuf type google.cloud.aiplatform.v1beta1.AutoscalingMetricSpec

AutoscalingMetricSpec.Builder

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

Protobuf type google.cloud.aiplatform.v1beta1.AutoscalingMetricSpec

AvroSource

The storage details for Avro input content.

Protobuf type google.cloud.aiplatform.v1beta1.AvroSource

AvroSource.Builder

The storage details for Avro input content.

Protobuf type google.cloud.aiplatform.v1beta1.AvroSource

BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateFeaturesOperationMetadata

BatchCreateFeaturesOperationMetadata.Builder

Details of operations that perform batch create Features.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateFeaturesOperationMetadata

BatchCreateFeaturesRequest

Request message for FeaturestoreService.BatchCreateFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateFeaturesRequest

BatchCreateFeaturesRequest.Builder

Request message for FeaturestoreService.BatchCreateFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateFeaturesRequest

BatchCreateFeaturesResponse

Response message for FeaturestoreService.BatchCreateFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateFeaturesResponse

BatchCreateFeaturesResponse.Builder

Response message for FeaturestoreService.BatchCreateFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateFeaturesResponse

BatchCreateTensorboardRunsRequest

Request message for TensorboardService.BatchCreateTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardRunsRequest

BatchCreateTensorboardRunsRequest.Builder

Request message for TensorboardService.BatchCreateTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardRunsRequest

BatchCreateTensorboardRunsResponse

Response message for TensorboardService.BatchCreateTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardRunsResponse

BatchCreateTensorboardRunsResponse.Builder

Response message for TensorboardService.BatchCreateTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardRunsResponse

BatchCreateTensorboardTimeSeriesRequest

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardTimeSeriesRequest

BatchCreateTensorboardTimeSeriesRequest.Builder

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardTimeSeriesRequest

BatchCreateTensorboardTimeSeriesResponse

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardTimeSeriesResponse

BatchCreateTensorboardTimeSeriesResponse.Builder

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.BatchCreateTensorboardTimeSeriesResponse

BatchDedicatedResources

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

Protobuf type google.cloud.aiplatform.v1beta1.BatchDedicatedResources

BatchDedicatedResources.Builder

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

Protobuf type google.cloud.aiplatform.v1beta1.BatchDedicatedResources

BatchImportModelEvaluationSlicesRequest

Request message for ModelService.BatchImportModelEvaluationSlices

Protobuf type google.cloud.aiplatform.v1beta1.BatchImportModelEvaluationSlicesRequest

BatchImportModelEvaluationSlicesRequest.Builder

Request message for ModelService.BatchImportModelEvaluationSlices

Protobuf type google.cloud.aiplatform.v1beta1.BatchImportModelEvaluationSlicesRequest

BatchImportModelEvaluationSlicesResponse

Response message for ModelService.BatchImportModelEvaluationSlices

Protobuf type google.cloud.aiplatform.v1beta1.BatchImportModelEvaluationSlicesResponse

BatchImportModelEvaluationSlicesResponse.Builder

Response message for ModelService.BatchImportModelEvaluationSlices

Protobuf type google.cloud.aiplatform.v1beta1.BatchImportModelEvaluationSlicesResponse

BatchMigrateResourcesOperationMetadata

Runtime operation information for MigrationService.BatchMigrateResources.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesOperationMetadata

BatchMigrateResourcesOperationMetadata.Builder

Runtime operation information for MigrationService.BatchMigrateResources.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesOperationMetadata

BatchMigrateResourcesOperationMetadata.PartialResult

Represents a partial result in batch migration operation for one MigrateResourceRequest.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesOperationMetadata.PartialResult

BatchMigrateResourcesOperationMetadata.PartialResult.Builder

Represents a partial result in batch migration operation for one MigrateResourceRequest.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesOperationMetadata.PartialResult

BatchMigrateResourcesRequest

Request message for MigrationService.BatchMigrateResources.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesRequest

BatchMigrateResourcesRequest.Builder

Request message for MigrationService.BatchMigrateResources.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesRequest

BatchMigrateResourcesResponse

Response message for MigrationService.BatchMigrateResources.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesResponse

BatchMigrateResourcesResponse.Builder

Response message for MigrationService.BatchMigrateResources.

Protobuf type google.cloud.aiplatform.v1beta1.BatchMigrateResourcesResponse

BatchPredictionJob

A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob

BatchPredictionJob.Builder

A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob

BatchPredictionJob.InputConfig

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.InputConfig

BatchPredictionJob.InputConfig.Builder

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.InputConfig

BatchPredictionJob.InstanceConfig

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig

BatchPredictionJob.InstanceConfig.Builder

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig

BatchPredictionJob.OutputConfig

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.OutputConfig

BatchPredictionJob.OutputConfig.Builder

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.OutputConfig

BatchPredictionJob.OutputInfo

Further describes this job's output. Supplements output_config.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.OutputInfo

BatchPredictionJob.OutputInfo.Builder

Further describes this job's output. Supplements output_config.

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.OutputInfo

BatchPredictionJobName

BatchPredictionJobName.Builder

Builder for projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}.

BatchPredictionJobProto

BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesOperationMetadata

BatchReadFeatureValuesOperationMetadata.Builder

Details of operations that batch reads Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesOperationMetadata

BatchReadFeatureValuesRequest

Request message for FeaturestoreService.BatchReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesRequest

BatchReadFeatureValuesRequest.Builder

Request message for FeaturestoreService.BatchReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesRequest

BatchReadFeatureValuesRequest.EntityTypeSpec

Selects Features of an EntityType to read values of and specifies read settings.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesRequest.EntityTypeSpec

BatchReadFeatureValuesRequest.EntityTypeSpec.Builder

Selects Features of an EntityType to read values of and specifies read settings.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesRequest.EntityTypeSpec

BatchReadFeatureValuesRequest.PassThroughField

Describe pass-through fields in read_instance source.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesRequest.PassThroughField

BatchReadFeatureValuesRequest.PassThroughField.Builder

Describe pass-through fields in read_instance source.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesRequest.PassThroughField

BatchReadFeatureValuesResponse

Response message for FeaturestoreService.BatchReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesResponse

BatchReadFeatureValuesResponse.Builder

Response message for FeaturestoreService.BatchReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadFeatureValuesResponse

BatchReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadTensorboardTimeSeriesDataRequest

BatchReadTensorboardTimeSeriesDataRequest.Builder

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadTensorboardTimeSeriesDataRequest

BatchReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadTensorboardTimeSeriesDataResponse

BatchReadTensorboardTimeSeriesDataResponse.Builder

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.BatchReadTensorboardTimeSeriesDataResponse

BigQueryDestination

The BigQuery location for the output content.

Protobuf type google.cloud.aiplatform.v1beta1.BigQueryDestination

BigQueryDestination.Builder

The BigQuery location for the output content.

Protobuf type google.cloud.aiplatform.v1beta1.BigQueryDestination

BigQuerySource

The BigQuery location for the input content.

Protobuf type google.cloud.aiplatform.v1beta1.BigQuerySource

BigQuerySource.Builder

The BigQuery location for the input content.

Protobuf type google.cloud.aiplatform.v1beta1.BigQuerySource

BlurBaselineConfig

Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

Protobuf type google.cloud.aiplatform.v1beta1.BlurBaselineConfig

BlurBaselineConfig.Builder

Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

Protobuf type google.cloud.aiplatform.v1beta1.BlurBaselineConfig

BoolArray

A list of boolean values.

Protobuf type google.cloud.aiplatform.v1beta1.BoolArray

BoolArray.Builder

A list of boolean values.

Protobuf type google.cloud.aiplatform.v1beta1.BoolArray

CancelBatchPredictionJobRequest

Request message for JobService.CancelBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelBatchPredictionJobRequest

CancelBatchPredictionJobRequest.Builder

Request message for JobService.CancelBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelBatchPredictionJobRequest

CancelCustomJobRequest

Request message for JobService.CancelCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelCustomJobRequest

CancelCustomJobRequest.Builder

Request message for JobService.CancelCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelCustomJobRequest

CancelDataLabelingJobRequest

Request message for JobService.CancelDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelDataLabelingJobRequest

CancelDataLabelingJobRequest.Builder

Request message for JobService.CancelDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelDataLabelingJobRequest

CancelHyperparameterTuningJobRequest

Request message for JobService.CancelHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelHyperparameterTuningJobRequest

CancelHyperparameterTuningJobRequest.Builder

Request message for JobService.CancelHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelHyperparameterTuningJobRequest

CancelPipelineJobRequest

Request message for PipelineService.CancelPipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelPipelineJobRequest

CancelPipelineJobRequest.Builder

Request message for PipelineService.CancelPipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.CancelPipelineJobRequest

CancelTrainingPipelineRequest

Request message for PipelineService.CancelTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.CancelTrainingPipelineRequest

CancelTrainingPipelineRequest.Builder

Request message for PipelineService.CancelTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.CancelTrainingPipelineRequest

CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

Protobuf type google.cloud.aiplatform.v1beta1.CheckTrialEarlyStoppingStateMetatdata

CheckTrialEarlyStoppingStateMetatdata.Builder

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

Protobuf type google.cloud.aiplatform.v1beta1.CheckTrialEarlyStoppingStateMetatdata

CheckTrialEarlyStoppingStateRequest

Request message for VizierService.CheckTrialEarlyStoppingState.

Protobuf type google.cloud.aiplatform.v1beta1.CheckTrialEarlyStoppingStateRequest

CheckTrialEarlyStoppingStateRequest.Builder

Request message for VizierService.CheckTrialEarlyStoppingState.

Protobuf type google.cloud.aiplatform.v1beta1.CheckTrialEarlyStoppingStateRequest

CheckTrialEarlyStoppingStateResponse

Response message for VizierService.CheckTrialEarlyStoppingState.

Protobuf type google.cloud.aiplatform.v1beta1.CheckTrialEarlyStoppingStateResponse

CheckTrialEarlyStoppingStateResponse.Builder

Response message for VizierService.CheckTrialEarlyStoppingState.

Protobuf type google.cloud.aiplatform.v1beta1.CheckTrialEarlyStoppingStateResponse

CompleteTrialRequest

Request message for VizierService.CompleteTrial.

Protobuf type google.cloud.aiplatform.v1beta1.CompleteTrialRequest

CompleteTrialRequest.Builder

Request message for VizierService.CompleteTrial.

Protobuf type google.cloud.aiplatform.v1beta1.CompleteTrialRequest

CompletionStats

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

Protobuf type google.cloud.aiplatform.v1beta1.CompletionStats

CompletionStats.Builder

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

Protobuf type google.cloud.aiplatform.v1beta1.CompletionStats

CompletionStatsProto

ContainerRegistryDestination

The Container Registry location for the container image.

Protobuf type google.cloud.aiplatform.v1beta1.ContainerRegistryDestination

ContainerRegistryDestination.Builder

The Container Registry location for the container image.

Protobuf type google.cloud.aiplatform.v1beta1.ContainerRegistryDestination

ContainerSpec

The spec of a Container.

Protobuf type google.cloud.aiplatform.v1beta1.ContainerSpec

ContainerSpec.Builder

The spec of a Container.

Protobuf type google.cloud.aiplatform.v1beta1.ContainerSpec

Context

Instance of a general context.

Protobuf type google.cloud.aiplatform.v1beta1.Context

Context.Builder

Instance of a general context.

Protobuf type google.cloud.aiplatform.v1beta1.Context

ContextName

ContextName.Builder

Builder for projects/{project}/locations/{location}/metadataStores/{metadata_store}/contexts/{context}.

ContextProto

CreateArtifactRequest

Request message for MetadataService.CreateArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.CreateArtifactRequest

CreateArtifactRequest.Builder

Request message for MetadataService.CreateArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.CreateArtifactRequest

CreateBatchPredictionJobRequest

Request message for JobService.CreateBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateBatchPredictionJobRequest

CreateBatchPredictionJobRequest.Builder

Request message for JobService.CreateBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateBatchPredictionJobRequest

CreateContextRequest

Request message for MetadataService.CreateContext.

Protobuf type google.cloud.aiplatform.v1beta1.CreateContextRequest

CreateContextRequest.Builder

Request message for MetadataService.CreateContext.

Protobuf type google.cloud.aiplatform.v1beta1.CreateContextRequest

CreateCustomJobRequest

Request message for JobService.CreateCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateCustomJobRequest

CreateCustomJobRequest.Builder

Request message for JobService.CreateCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateCustomJobRequest

CreateDataLabelingJobRequest

Request message for JobService.CreateDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDataLabelingJobRequest

CreateDataLabelingJobRequest.Builder

Request message for JobService.CreateDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDataLabelingJobRequest

CreateDatasetOperationMetadata

Runtime operation information for DatasetService.CreateDataset.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDatasetOperationMetadata

CreateDatasetOperationMetadata.Builder

Runtime operation information for DatasetService.CreateDataset.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDatasetOperationMetadata

CreateDatasetRequest

Request message for DatasetService.CreateDataset.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDatasetRequest

CreateDatasetRequest.Builder

Request message for DatasetService.CreateDataset.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDatasetRequest

CreateDeploymentResourcePoolOperationMetadata

Runtime operation information for CreateDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDeploymentResourcePoolOperationMetadata

CreateDeploymentResourcePoolOperationMetadata.Builder

Runtime operation information for CreateDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDeploymentResourcePoolOperationMetadata

CreateDeploymentResourcePoolRequest

Request message for CreateDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDeploymentResourcePoolRequest

CreateDeploymentResourcePoolRequest.Builder

Request message for CreateDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.CreateDeploymentResourcePoolRequest

CreateEndpointOperationMetadata

Runtime operation information for EndpointService.CreateEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEndpointOperationMetadata

CreateEndpointOperationMetadata.Builder

Runtime operation information for EndpointService.CreateEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEndpointOperationMetadata

CreateEndpointRequest

Request message for EndpointService.CreateEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEndpointRequest

CreateEndpointRequest.Builder

Request message for EndpointService.CreateEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEndpointRequest

CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEntityTypeOperationMetadata

CreateEntityTypeOperationMetadata.Builder

Details of operations that perform create EntityType.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEntityTypeOperationMetadata

CreateEntityTypeRequest

Request message for FeaturestoreService.CreateEntityType.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEntityTypeRequest

CreateEntityTypeRequest.Builder

Request message for FeaturestoreService.CreateEntityType.

Protobuf type google.cloud.aiplatform.v1beta1.CreateEntityTypeRequest

CreateExecutionRequest

Request message for MetadataService.CreateExecution.

Protobuf type google.cloud.aiplatform.v1beta1.CreateExecutionRequest

CreateExecutionRequest.Builder

Request message for MetadataService.CreateExecution.

Protobuf type google.cloud.aiplatform.v1beta1.CreateExecutionRequest

CreateFeatureOperationMetadata

Details of operations that perform create Feature.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeatureOperationMetadata

CreateFeatureOperationMetadata.Builder

Details of operations that perform create Feature.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeatureOperationMetadata

CreateFeatureRequest

Request message for FeaturestoreService.CreateFeature.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeatureRequest

CreateFeatureRequest.Builder

Request message for FeaturestoreService.CreateFeature.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeatureRequest

CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeaturestoreOperationMetadata

CreateFeaturestoreOperationMetadata.Builder

Details of operations that perform create Featurestore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeaturestoreOperationMetadata

CreateFeaturestoreRequest

Request message for FeaturestoreService.CreateFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeaturestoreRequest

CreateFeaturestoreRequest.Builder

Request message for FeaturestoreService.CreateFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateFeaturestoreRequest

CreateHyperparameterTuningJobRequest

Request message for JobService.CreateHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateHyperparameterTuningJobRequest

CreateHyperparameterTuningJobRequest.Builder

Request message for JobService.CreateHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateHyperparameterTuningJobRequest

CreateIndexEndpointOperationMetadata

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexEndpointOperationMetadata

CreateIndexEndpointOperationMetadata.Builder

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexEndpointOperationMetadata

CreateIndexEndpointRequest

Request message for IndexEndpointService.CreateIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexEndpointRequest

CreateIndexEndpointRequest.Builder

Request message for IndexEndpointService.CreateIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexEndpointRequest

CreateIndexOperationMetadata

Runtime operation information for IndexService.CreateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexOperationMetadata

CreateIndexOperationMetadata.Builder

Runtime operation information for IndexService.CreateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexOperationMetadata

CreateIndexRequest

Request message for IndexService.CreateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexRequest

CreateIndexRequest.Builder

Request message for IndexService.CreateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.CreateIndexRequest

CreateMetadataSchemaRequest

Request message for MetadataService.CreateMetadataSchema.

Protobuf type google.cloud.aiplatform.v1beta1.CreateMetadataSchemaRequest

CreateMetadataSchemaRequest.Builder

Request message for MetadataService.CreateMetadataSchema.

Protobuf type google.cloud.aiplatform.v1beta1.CreateMetadataSchemaRequest

CreateMetadataStoreOperationMetadata

Details of operations that perform MetadataService.CreateMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateMetadataStoreOperationMetadata

CreateMetadataStoreOperationMetadata.Builder

Details of operations that perform MetadataService.CreateMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateMetadataStoreOperationMetadata

CreateMetadataStoreRequest

Request message for MetadataService.CreateMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateMetadataStoreRequest

CreateMetadataStoreRequest.Builder

Request message for MetadataService.CreateMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.CreateMetadataStoreRequest

CreateModelDeploymentMonitoringJobRequest

Request message for JobService.CreateModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateModelDeploymentMonitoringJobRequest

CreateModelDeploymentMonitoringJobRequest.Builder

Request message for JobService.CreateModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreateModelDeploymentMonitoringJobRequest

CreatePipelineJobRequest

Request message for PipelineService.CreatePipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreatePipelineJobRequest

CreatePipelineJobRequest.Builder

Request message for PipelineService.CreatePipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.CreatePipelineJobRequest

CreateSpecialistPoolOperationMetadata

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.CreateSpecialistPoolOperationMetadata

CreateSpecialistPoolOperationMetadata.Builder

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.CreateSpecialistPoolOperationMetadata

CreateSpecialistPoolRequest

Request message for SpecialistPoolService.CreateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.CreateSpecialistPoolRequest

CreateSpecialistPoolRequest.Builder

Request message for SpecialistPoolService.CreateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.CreateSpecialistPoolRequest

CreateStudyRequest

Request message for VizierService.CreateStudy.

Protobuf type google.cloud.aiplatform.v1beta1.CreateStudyRequest

CreateStudyRequest.Builder

Request message for VizierService.CreateStudy.

Protobuf type google.cloud.aiplatform.v1beta1.CreateStudyRequest

CreateTensorboardExperimentRequest

Request message for TensorboardService.CreateTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardExperimentRequest

CreateTensorboardExperimentRequest.Builder

Request message for TensorboardService.CreateTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardExperimentRequest

CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardOperationMetadata

CreateTensorboardOperationMetadata.Builder

Details of operations that perform create Tensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardOperationMetadata

CreateTensorboardRequest

Request message for TensorboardService.CreateTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardRequest

CreateTensorboardRequest.Builder

Request message for TensorboardService.CreateTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardRequest

CreateTensorboardRunRequest

Request message for TensorboardService.CreateTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardRunRequest

CreateTensorboardRunRequest.Builder

Request message for TensorboardService.CreateTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardRunRequest

CreateTensorboardTimeSeriesRequest

Request message for TensorboardService.CreateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardTimeSeriesRequest

CreateTensorboardTimeSeriesRequest.Builder

Request message for TensorboardService.CreateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTensorboardTimeSeriesRequest

CreateTrainingPipelineRequest

Request message for PipelineService.CreateTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTrainingPipelineRequest

CreateTrainingPipelineRequest.Builder

Request message for PipelineService.CreateTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTrainingPipelineRequest

CreateTrialRequest

Request message for VizierService.CreateTrial.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTrialRequest

CreateTrialRequest.Builder

Request message for VizierService.CreateTrial.

Protobuf type google.cloud.aiplatform.v1beta1.CreateTrialRequest

CsvDestination

The storage details for CSV output content.

Protobuf type google.cloud.aiplatform.v1beta1.CsvDestination

CsvDestination.Builder

The storage details for CSV output content.

Protobuf type google.cloud.aiplatform.v1beta1.CsvDestination

CsvSource

The storage details for CSV input content.

Protobuf type google.cloud.aiplatform.v1beta1.CsvSource

CsvSource.Builder

The storage details for CSV input content.

Protobuf type google.cloud.aiplatform.v1beta1.CsvSource

CustomJob

Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

Protobuf type google.cloud.aiplatform.v1beta1.CustomJob

CustomJob.Builder

Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

Protobuf type google.cloud.aiplatform.v1beta1.CustomJob

CustomJobName

CustomJobName.Builder

Builder for projects/{project}/locations/{location}/customJobs/{custom_job}.

CustomJobProto

CustomJobSpec

Represents the spec of a CustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.CustomJobSpec

CustomJobSpec.Builder

Represents the spec of a CustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.CustomJobSpec

DataItem

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

Protobuf type google.cloud.aiplatform.v1beta1.DataItem

DataItem.Builder

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

Protobuf type google.cloud.aiplatform.v1beta1.DataItem

DataItemName

DataItemName.Builder

Builder for projects/{project}/locations/{location}/datasets/{dataset}/dataItems/{data_item}.

DataItemProto

DataItemView

A container for a single DataItem and Annotations on it.

Protobuf type google.cloud.aiplatform.v1beta1.DataItemView

DataItemView.Builder

A container for a single DataItem and Annotations on it.

Protobuf type google.cloud.aiplatform.v1beta1.DataItemView

DataLabelingJob

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

Protobuf type google.cloud.aiplatform.v1beta1.DataLabelingJob

DataLabelingJob.Builder

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

Protobuf type google.cloud.aiplatform.v1beta1.DataLabelingJob

DataLabelingJobName

DataLabelingJobName.Builder

Builder for projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}.

DataLabelingJobProto

Dataset

A collection of DataItems and Annotations on them.

Protobuf type google.cloud.aiplatform.v1beta1.Dataset

Dataset.Builder

A collection of DataItems and Annotations on them.

Protobuf type google.cloud.aiplatform.v1beta1.Dataset

DatasetName

DatasetName.Builder

Builder for projects/{project}/locations/{location}/datasets/{dataset}.

DatasetProto

DatasetServiceClient

Service Description: The service that handles the CRUD of Vertex AI Dataset and its child resources.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (DatasetServiceClient datasetServiceClient = DatasetServiceClient.create()) {
   DatasetName name = DatasetName.of("[PROJECT]", "[LOCATION]", "[DATASET]");
   Dataset response = datasetServiceClient.getDataset(name);
 }
 

Note: close() needs to be called on the DatasetServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of DatasetServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 DatasetServiceSettings datasetServiceSettings =
     DatasetServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 DatasetServiceSettings datasetServiceSettings =
     DatasetServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

DatasetServiceClient.ListAnnotationsFixedSizeCollection

DatasetServiceClient.ListAnnotationsPage

DatasetServiceClient.ListAnnotationsPagedResponse

DatasetServiceClient.ListDataItemsFixedSizeCollection

DatasetServiceClient.ListDataItemsPage

DatasetServiceClient.ListDataItemsPagedResponse

DatasetServiceClient.ListDatasetsFixedSizeCollection

DatasetServiceClient.ListDatasetsPage

DatasetServiceClient.ListDatasetsPagedResponse

DatasetServiceClient.ListLocationsFixedSizeCollection

DatasetServiceClient.ListLocationsPage

DatasetServiceClient.ListLocationsPagedResponse

DatasetServiceClient.ListSavedQueriesFixedSizeCollection

DatasetServiceClient.ListSavedQueriesPage

DatasetServiceClient.ListSavedQueriesPagedResponse

DatasetServiceClient.SearchDataItemsFixedSizeCollection

DatasetServiceClient.SearchDataItemsPage

DatasetServiceClient.SearchDataItemsPagedResponse

DatasetServiceGrpc

The service that handles the CRUD of Vertex AI Dataset and its child resources.

DatasetServiceGrpc.DatasetServiceBlockingStub

The service that handles the CRUD of Vertex AI Dataset and its child resources.

DatasetServiceGrpc.DatasetServiceFutureStub

The service that handles the CRUD of Vertex AI Dataset and its child resources.

DatasetServiceGrpc.DatasetServiceImplBase

The service that handles the CRUD of Vertex AI Dataset and its child resources.

DatasetServiceGrpc.DatasetServiceStub

The service that handles the CRUD of Vertex AI Dataset and its child resources.

DatasetServiceProto

DatasetServiceSettings

Settings class to configure an instance of DatasetServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getDataset to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 DatasetServiceSettings.Builder datasetServiceSettingsBuilder =
     DatasetServiceSettings.newBuilder();
 datasetServiceSettingsBuilder
     .getDatasetSettings()
     .setRetrySettings(
         datasetServiceSettingsBuilder.getDatasetSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 DatasetServiceSettings datasetServiceSettings = datasetServiceSettingsBuilder.build();
 

DatasetServiceSettings.Builder

Builder for DatasetServiceSettings.

DedicatedResources

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

Protobuf type google.cloud.aiplatform.v1beta1.DedicatedResources

DedicatedResources.Builder

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

Protobuf type google.cloud.aiplatform.v1beta1.DedicatedResources

DeleteArtifactRequest

Request message for MetadataService.DeleteArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteArtifactRequest

DeleteArtifactRequest.Builder

Request message for MetadataService.DeleteArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteArtifactRequest

DeleteBatchPredictionJobRequest

Request message for JobService.DeleteBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteBatchPredictionJobRequest

DeleteBatchPredictionJobRequest.Builder

Request message for JobService.DeleteBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteBatchPredictionJobRequest

DeleteContextRequest

Request message for MetadataService.DeleteContext.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteContextRequest

DeleteContextRequest.Builder

Request message for MetadataService.DeleteContext.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteContextRequest

DeleteCustomJobRequest

Request message for JobService.DeleteCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteCustomJobRequest

DeleteCustomJobRequest.Builder

Request message for JobService.DeleteCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteCustomJobRequest

DeleteDataLabelingJobRequest

Request message for JobService.DeleteDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteDataLabelingJobRequest

DeleteDataLabelingJobRequest.Builder

Request message for JobService.DeleteDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteDataLabelingJobRequest

DeleteDatasetRequest

Request message for DatasetService.DeleteDataset.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteDatasetRequest

DeleteDatasetRequest.Builder

Request message for DatasetService.DeleteDataset.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteDatasetRequest

DeleteDeploymentResourcePoolRequest

Request message for DeleteDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteDeploymentResourcePoolRequest

DeleteDeploymentResourcePoolRequest.Builder

Request message for DeleteDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteDeploymentResourcePoolRequest

DeleteEndpointRequest

Request message for EndpointService.DeleteEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteEndpointRequest

DeleteEndpointRequest.Builder

Request message for EndpointService.DeleteEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteEndpointRequest

DeleteEntityTypeRequest

Request message for [FeaturestoreService.DeleteEntityTypes][].

Protobuf type google.cloud.aiplatform.v1beta1.DeleteEntityTypeRequest

DeleteEntityTypeRequest.Builder

Request message for [FeaturestoreService.DeleteEntityTypes][].

Protobuf type google.cloud.aiplatform.v1beta1.DeleteEntityTypeRequest

DeleteExecutionRequest

Request message for MetadataService.DeleteExecution.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteExecutionRequest

DeleteExecutionRequest.Builder

Request message for MetadataService.DeleteExecution.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteExecutionRequest

DeleteFeatureRequest

Request message for FeaturestoreService.DeleteFeature.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureRequest

DeleteFeatureRequest.Builder

Request message for FeaturestoreService.DeleteFeature.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureRequest

DeleteFeatureValuesOperationMetadata

Details of operations that delete Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesOperationMetadata

DeleteFeatureValuesOperationMetadata.Builder

Details of operations that delete Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesOperationMetadata

DeleteFeatureValuesRequest

Request message for FeaturestoreService.DeleteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesRequest

DeleteFeatureValuesRequest.Builder

Request message for FeaturestoreService.DeleteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesRequest

DeleteFeatureValuesRequest.SelectEntity

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesRequest.SelectEntity

DeleteFeatureValuesRequest.SelectEntity.Builder

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesRequest.SelectEntity

DeleteFeatureValuesRequest.SelectTimeRangeAndFeature

Message to select time range and feature. Values of the selected feature generated within an inclusive time range will be deleted. Using this option permanently deletes the feature values from the specified feature IDs within the specified time range. This might include data from the online storage. If you want to retain any deleted historical data in the online storage, you must re-ingest it.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesRequest.SelectTimeRangeAndFeature

DeleteFeatureValuesRequest.SelectTimeRangeAndFeature.Builder

Message to select time range and feature. Values of the selected feature generated within an inclusive time range will be deleted. Using this option permanently deletes the feature values from the specified feature IDs within the specified time range. This might include data from the online storage. If you want to retain any deleted historical data in the online storage, you must re-ingest it.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesRequest.SelectTimeRangeAndFeature

DeleteFeatureValuesResponse

Response message for FeaturestoreService.DeleteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesResponse

DeleteFeatureValuesResponse.Builder

Response message for FeaturestoreService.DeleteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeatureValuesResponse

DeleteFeaturestoreRequest

Request message for FeaturestoreService.DeleteFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeaturestoreRequest

DeleteFeaturestoreRequest.Builder

Request message for FeaturestoreService.DeleteFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteFeaturestoreRequest

DeleteHyperparameterTuningJobRequest

Request message for JobService.DeleteHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteHyperparameterTuningJobRequest

DeleteHyperparameterTuningJobRequest.Builder

Request message for JobService.DeleteHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteHyperparameterTuningJobRequest

DeleteIndexEndpointRequest

Request message for IndexEndpointService.DeleteIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteIndexEndpointRequest

DeleteIndexEndpointRequest.Builder

Request message for IndexEndpointService.DeleteIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteIndexEndpointRequest

DeleteIndexRequest

Request message for IndexService.DeleteIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteIndexRequest

DeleteIndexRequest.Builder

Request message for IndexService.DeleteIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteIndexRequest

DeleteMetadataStoreOperationMetadata

Details of operations that perform MetadataService.DeleteMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteMetadataStoreOperationMetadata

DeleteMetadataStoreOperationMetadata.Builder

Details of operations that perform MetadataService.DeleteMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteMetadataStoreOperationMetadata

DeleteMetadataStoreRequest

Request message for MetadataService.DeleteMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteMetadataStoreRequest

DeleteMetadataStoreRequest.Builder

Request message for MetadataService.DeleteMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteMetadataStoreRequest

DeleteModelDeploymentMonitoringJobRequest

Request message for JobService.DeleteModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteModelDeploymentMonitoringJobRequest

DeleteModelDeploymentMonitoringJobRequest.Builder

Request message for JobService.DeleteModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteModelDeploymentMonitoringJobRequest

DeleteModelRequest

Request message for ModelService.DeleteModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteModelRequest

DeleteModelRequest.Builder

Request message for ModelService.DeleteModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteModelRequest

DeleteModelVersionRequest

Request message for ModelService.DeleteModelVersion.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteModelVersionRequest

DeleteModelVersionRequest.Builder

Request message for ModelService.DeleteModelVersion.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteModelVersionRequest

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteOperationMetadata

DeleteOperationMetadata.Builder

Details of operations that perform deletes of any entities.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteOperationMetadata

DeletePipelineJobRequest

Request message for PipelineService.DeletePipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeletePipelineJobRequest

DeletePipelineJobRequest.Builder

Request message for PipelineService.DeletePipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.DeletePipelineJobRequest

DeleteSpecialistPoolRequest

Request message for SpecialistPoolService.DeleteSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteSpecialistPoolRequest

DeleteSpecialistPoolRequest.Builder

Request message for SpecialistPoolService.DeleteSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteSpecialistPoolRequest

DeleteStudyRequest

Request message for VizierService.DeleteStudy.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteStudyRequest

DeleteStudyRequest.Builder

Request message for VizierService.DeleteStudy.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteStudyRequest

DeleteTensorboardExperimentRequest

Request message for TensorboardService.DeleteTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardExperimentRequest

DeleteTensorboardExperimentRequest.Builder

Request message for TensorboardService.DeleteTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardExperimentRequest

DeleteTensorboardRequest

Request message for TensorboardService.DeleteTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardRequest

DeleteTensorboardRequest.Builder

Request message for TensorboardService.DeleteTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardRequest

DeleteTensorboardRunRequest

Request message for TensorboardService.DeleteTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardRunRequest

DeleteTensorboardRunRequest.Builder

Request message for TensorboardService.DeleteTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardRunRequest

DeleteTensorboardTimeSeriesRequest

Request message for TensorboardService.DeleteTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardTimeSeriesRequest

DeleteTensorboardTimeSeriesRequest.Builder

Request message for TensorboardService.DeleteTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTensorboardTimeSeriesRequest

DeleteTrainingPipelineRequest

Request message for PipelineService.DeleteTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTrainingPipelineRequest

DeleteTrainingPipelineRequest.Builder

Request message for PipelineService.DeleteTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTrainingPipelineRequest

DeleteTrialRequest

Request message for VizierService.DeleteTrial.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTrialRequest

DeleteTrialRequest.Builder

Request message for VizierService.DeleteTrial.

Protobuf type google.cloud.aiplatform.v1beta1.DeleteTrialRequest

DeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.DeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployIndexOperationMetadata

DeployIndexOperationMetadata.Builder

Runtime operation information for IndexEndpointService.DeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployIndexOperationMetadata

DeployIndexRequest

Request message for IndexEndpointService.DeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployIndexRequest

DeployIndexRequest.Builder

Request message for IndexEndpointService.DeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployIndexRequest

DeployIndexResponse

Response message for IndexEndpointService.DeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployIndexResponse

DeployIndexResponse.Builder

Response message for IndexEndpointService.DeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployIndexResponse

DeployModelOperationMetadata

Runtime operation information for EndpointService.DeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployModelOperationMetadata

DeployModelOperationMetadata.Builder

Runtime operation information for EndpointService.DeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployModelOperationMetadata

DeployModelRequest

Request message for EndpointService.DeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployModelRequest

DeployModelRequest.Builder

Request message for EndpointService.DeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployModelRequest

DeployModelResponse

Response message for EndpointService.DeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployModelResponse

DeployModelResponse.Builder

Response message for EndpointService.DeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployModelResponse

DeployedIndex

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndex

DeployedIndex.Builder

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndex

DeployedIndexAuthConfig

Used to set up the auth on the DeployedIndex's private endpoint.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndexAuthConfig

DeployedIndexAuthConfig.AuthProvider

Configuration for an authentication provider, including support for JSON Web Token (JWT).

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndexAuthConfig.AuthProvider

DeployedIndexAuthConfig.AuthProvider.Builder

Configuration for an authentication provider, including support for JSON Web Token (JWT).

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndexAuthConfig.AuthProvider

DeployedIndexAuthConfig.Builder

Used to set up the auth on the DeployedIndex's private endpoint.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndexAuthConfig

DeployedIndexRef

Points to a DeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndexRef

DeployedIndexRef.Builder

Points to a DeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedIndexRef

DeployedIndexRefProto

DeployedModel

A deployment of a Model. Endpoints contain one or more DeployedModels.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedModel

DeployedModel.Builder

A deployment of a Model. Endpoints contain one or more DeployedModels.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedModel

DeployedModelNameProto

DeployedModelRef

Points to a DeployedModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedModelRef

DeployedModelRef.Builder

Points to a DeployedModel.

Protobuf type google.cloud.aiplatform.v1beta1.DeployedModelRef

DeploymentResourcePool

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

Protobuf type google.cloud.aiplatform.v1beta1.DeploymentResourcePool

DeploymentResourcePool.Builder

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

Protobuf type google.cloud.aiplatform.v1beta1.DeploymentResourcePool

DeploymentResourcePoolName

DeploymentResourcePoolName.Builder

Builder for projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}.

DeploymentResourcePoolProto

DeploymentResourcePoolServiceClient

Service Description: A service that manages the DeploymentResourcePool resource.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (DeploymentResourcePoolServiceClient deploymentResourcePoolServiceClient =
     DeploymentResourcePoolServiceClient.create()) {
   DeploymentResourcePoolName name =
       DeploymentResourcePoolName.of("[PROJECT]", "[LOCATION]", "[DEPLOYMENT_RESOURCE_POOL]");
   DeploymentResourcePool response =
       deploymentResourcePoolServiceClient.getDeploymentResourcePool(name);
 }
 

Note: close() needs to be called on the DeploymentResourcePoolServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of DeploymentResourcePoolServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 DeploymentResourcePoolServiceSettings deploymentResourcePoolServiceSettings =
     DeploymentResourcePoolServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 DeploymentResourcePoolServiceClient deploymentResourcePoolServiceClient =
     DeploymentResourcePoolServiceClient.create(deploymentResourcePoolServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 DeploymentResourcePoolServiceSettings deploymentResourcePoolServiceSettings =
     DeploymentResourcePoolServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 DeploymentResourcePoolServiceClient deploymentResourcePoolServiceClient =
     DeploymentResourcePoolServiceClient.create(deploymentResourcePoolServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

DeploymentResourcePoolServiceClient.ListDeploymentResourcePoolsFixedSizeCollection

DeploymentResourcePoolServiceClient.ListDeploymentResourcePoolsPage

DeploymentResourcePoolServiceClient.ListDeploymentResourcePoolsPagedResponse

DeploymentResourcePoolServiceClient.ListLocationsFixedSizeCollection

DeploymentResourcePoolServiceClient.ListLocationsPage

DeploymentResourcePoolServiceClient.ListLocationsPagedResponse

DeploymentResourcePoolServiceClient.QueryDeployedModelsFixedSizeCollection

DeploymentResourcePoolServiceClient.QueryDeployedModelsPage

DeploymentResourcePoolServiceClient.QueryDeployedModelsPagedResponse

DeploymentResourcePoolServiceGrpc

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceGrpc.DeploymentResourcePoolServiceBlockingStub

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceGrpc.DeploymentResourcePoolServiceFutureStub

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceGrpc.DeploymentResourcePoolServiceImplBase

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceGrpc.DeploymentResourcePoolServiceStub

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceProto

DeploymentResourcePoolServiceSettings

Settings class to configure an instance of DeploymentResourcePoolServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getDeploymentResourcePool to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 DeploymentResourcePoolServiceSettings.Builder deploymentResourcePoolServiceSettingsBuilder =
     DeploymentResourcePoolServiceSettings.newBuilder();
 deploymentResourcePoolServiceSettingsBuilder
     .getDeploymentResourcePoolSettings()
     .setRetrySettings(
         deploymentResourcePoolServiceSettingsBuilder
             .getDeploymentResourcePoolSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 DeploymentResourcePoolServiceSettings deploymentResourcePoolServiceSettings =
     deploymentResourcePoolServiceSettingsBuilder.build();
 

DeploymentResourcePoolServiceSettings.Builder

Builder for DeploymentResourcePoolServiceSettings.

DestinationFeatureSetting

Protobuf type google.cloud.aiplatform.v1beta1.DestinationFeatureSetting

DestinationFeatureSetting.Builder

Protobuf type google.cloud.aiplatform.v1beta1.DestinationFeatureSetting

DiskSpec

Represents the spec of disk options.

Protobuf type google.cloud.aiplatform.v1beta1.DiskSpec

DiskSpec.Builder

Represents the spec of disk options.

Protobuf type google.cloud.aiplatform.v1beta1.DiskSpec

DoubleArray

A list of double values.

Protobuf type google.cloud.aiplatform.v1beta1.DoubleArray

DoubleArray.Builder

A list of double values.

Protobuf type google.cloud.aiplatform.v1beta1.DoubleArray

EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Protobuf type google.cloud.aiplatform.v1beta1.EncryptionSpec

EncryptionSpec.Builder

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Protobuf type google.cloud.aiplatform.v1beta1.EncryptionSpec

EncryptionSpecProto

Endpoint

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Protobuf type google.cloud.aiplatform.v1beta1.Endpoint

Endpoint.Builder

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Protobuf type google.cloud.aiplatform.v1beta1.Endpoint

EndpointName

EndpointName.Builder

Builder for projects/{project}/locations/{location}/endpoints/{endpoint}.

EndpointProto

EndpointServiceClient

Service Description: A service for managing Vertex AI's Endpoints.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (EndpointServiceClient endpointServiceClient = EndpointServiceClient.create()) {
   EndpointName name = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   Endpoint response = endpointServiceClient.getEndpoint(name);
 }
 

Note: close() needs to be called on the EndpointServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of EndpointServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 EndpointServiceSettings endpointServiceSettings =
     EndpointServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 EndpointServiceClient endpointServiceClient =
     EndpointServiceClient.create(endpointServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 EndpointServiceSettings endpointServiceSettings =
     EndpointServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 EndpointServiceClient endpointServiceClient =
     EndpointServiceClient.create(endpointServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

EndpointServiceClient.ListEndpointsFixedSizeCollection

EndpointServiceClient.ListEndpointsPage

EndpointServiceClient.ListEndpointsPagedResponse

EndpointServiceClient.ListLocationsFixedSizeCollection

EndpointServiceClient.ListLocationsPage

EndpointServiceClient.ListLocationsPagedResponse

EndpointServiceGrpc

A service for managing Vertex AI's Endpoints.

EndpointServiceGrpc.EndpointServiceBlockingStub

A service for managing Vertex AI's Endpoints.

EndpointServiceGrpc.EndpointServiceFutureStub

A service for managing Vertex AI's Endpoints.

EndpointServiceGrpc.EndpointServiceImplBase

A service for managing Vertex AI's Endpoints.

EndpointServiceGrpc.EndpointServiceStub

A service for managing Vertex AI's Endpoints.

EndpointServiceProto

EndpointServiceSettings

Settings class to configure an instance of EndpointServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getEndpoint to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 EndpointServiceSettings.Builder endpointServiceSettingsBuilder =
     EndpointServiceSettings.newBuilder();
 endpointServiceSettingsBuilder
     .getEndpointSettings()
     .setRetrySettings(
         endpointServiceSettingsBuilder.getEndpointSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 EndpointServiceSettings endpointServiceSettings = endpointServiceSettingsBuilder.build();
 

EndpointServiceSettings.Builder

Builder for EndpointServiceSettings.

EntityIdSelector

Selector for entityId. Getting ids from the given source.

Protobuf type google.cloud.aiplatform.v1beta1.EntityIdSelector

EntityIdSelector.Builder

Selector for entityId. Getting ids from the given source.

Protobuf type google.cloud.aiplatform.v1beta1.EntityIdSelector

EntityType

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Protobuf type google.cloud.aiplatform.v1beta1.EntityType

EntityType.Builder

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Protobuf type google.cloud.aiplatform.v1beta1.EntityType

EntityTypeName

EntityTypeName.Builder

Builder for projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}.

EntityTypeProto

EnvVar

Represents an environment variable present in a Container or Python Module.

Protobuf type google.cloud.aiplatform.v1beta1.EnvVar

EnvVar.Builder

Represents an environment variable present in a Container or Python Module.

Protobuf type google.cloud.aiplatform.v1beta1.EnvVar

EnvVarProto

Event

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

Protobuf type google.cloud.aiplatform.v1beta1.Event

Event.Builder

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

Protobuf type google.cloud.aiplatform.v1beta1.Event

EventProto

Examples

Example-based explainability that returns the nearest neighbors from the provided dataset.

Protobuf type google.cloud.aiplatform.v1beta1.Examples

Examples.Builder

Example-based explainability that returns the nearest neighbors from the provided dataset.

Protobuf type google.cloud.aiplatform.v1beta1.Examples

ExamplesOverride

Overrides for example-based explanations.

Protobuf type google.cloud.aiplatform.v1beta1.ExamplesOverride

ExamplesOverride.Builder

Overrides for example-based explanations.

Protobuf type google.cloud.aiplatform.v1beta1.ExamplesOverride

ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

Protobuf type google.cloud.aiplatform.v1beta1.ExamplesRestrictionsNamespace

ExamplesRestrictionsNamespace.Builder

Restrictions namespace for example-based explanations overrides.

Protobuf type google.cloud.aiplatform.v1beta1.ExamplesRestrictionsNamespace

Execution

Instance of a general execution.

Protobuf type google.cloud.aiplatform.v1beta1.Execution

Execution.Builder

Instance of a general execution.

Protobuf type google.cloud.aiplatform.v1beta1.Execution

ExecutionName

ExecutionName.Builder

Builder for projects/{project}/locations/{location}/metadataStores/{metadata_store}/executions/{execution}.

ExecutionProto

ExplainRequest

Request message for PredictionService.Explain.

Protobuf type google.cloud.aiplatform.v1beta1.ExplainRequest

ExplainRequest.Builder

Request message for PredictionService.Explain.

Protobuf type google.cloud.aiplatform.v1beta1.ExplainRequest

ExplainResponse

Response message for PredictionService.Explain.

Protobuf type google.cloud.aiplatform.v1beta1.ExplainResponse

ExplainResponse.Builder

Response message for PredictionService.Explain.

Protobuf type google.cloud.aiplatform.v1beta1.ExplainResponse

Explanation

Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.

Protobuf type google.cloud.aiplatform.v1beta1.Explanation

Explanation.Builder

Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.

Protobuf type google.cloud.aiplatform.v1beta1.Explanation

ExplanationMetadata

Metadata describing the Model's input and output for explanation.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata

ExplanationMetadata.Builder

Metadata describing the Model's input and output for explanation.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata

ExplanationMetadata.InputMetadata

Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata

ExplanationMetadata.InputMetadata.Builder

Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata

ExplanationMetadata.InputMetadata.FeatureValueDomain

Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain

ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder

Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain

ExplanationMetadata.InputMetadata.Visualization

Visualization configurations for image explanation.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization

ExplanationMetadata.InputMetadata.Visualization.Builder

Visualization configurations for image explanation.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization

ExplanationMetadata.OutputMetadata

Metadata of the prediction output to be explained.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata

ExplanationMetadata.OutputMetadata.Builder

Metadata of the prediction output to be explained.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata

ExplanationMetadataOverride

The ExplanationMetadata entries that can be overridden at online explanation time.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride

ExplanationMetadataOverride.Builder

The ExplanationMetadata entries that can be overridden at online explanation time.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride

ExplanationMetadataOverride.InputMetadataOverride

The input metadata entries to be overridden.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride

ExplanationMetadataOverride.InputMetadataOverride.Builder

The input metadata entries to be overridden.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride

ExplanationMetadataProto

ExplanationParameters

Parameters to configure explaining for Model's predictions.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationParameters

ExplanationParameters.Builder

Parameters to configure explaining for Model's predictions.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationParameters

ExplanationProto

ExplanationSpec

Specification of Model explanation.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationSpec

ExplanationSpec.Builder

Specification of Model explanation.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationSpec

ExplanationSpecOverride

The ExplanationSpec entries that can be overridden at online explanation time.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationSpecOverride

ExplanationSpecOverride.Builder

The ExplanationSpec entries that can be overridden at online explanation time.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationSpecOverride

ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataConfig

ExportDataConfig.Builder

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataConfig

ExportDataOperationMetadata

Runtime operation information for DatasetService.ExportData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataOperationMetadata

ExportDataOperationMetadata.Builder

Runtime operation information for DatasetService.ExportData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataOperationMetadata

ExportDataRequest

Request message for DatasetService.ExportData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataRequest

ExportDataRequest.Builder

Request message for DatasetService.ExportData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataRequest

ExportDataResponse

Response message for DatasetService.ExportData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataResponse

ExportDataResponse.Builder

Response message for DatasetService.ExportData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportDataResponse

ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesOperationMetadata

ExportFeatureValuesOperationMetadata.Builder

Details of operations that exports Features values.

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesOperationMetadata

ExportFeatureValuesRequest

Request message for FeaturestoreService.ExportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesRequest

ExportFeatureValuesRequest.Builder

Request message for FeaturestoreService.ExportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesRequest

ExportFeatureValuesRequest.FullExport

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesRequest.FullExport

ExportFeatureValuesRequest.FullExport.Builder

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesRequest.FullExport

ExportFeatureValuesRequest.SnapshotExport

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesRequest.SnapshotExport

ExportFeatureValuesRequest.SnapshotExport.Builder

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesRequest.SnapshotExport

ExportFeatureValuesResponse

Response message for FeaturestoreService.ExportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesResponse

ExportFeatureValuesResponse.Builder

Response message for FeaturestoreService.ExportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ExportFeatureValuesResponse

ExportModelOperationMetadata

Details of ModelService.ExportModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelOperationMetadata

ExportModelOperationMetadata.Builder

Details of ModelService.ExportModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelOperationMetadata

ExportModelOperationMetadata.OutputInfo

Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelOperationMetadata.OutputInfo

ExportModelOperationMetadata.OutputInfo.Builder

Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelOperationMetadata.OutputInfo

ExportModelRequest

Request message for ModelService.ExportModel.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelRequest

ExportModelRequest.Builder

Request message for ModelService.ExportModel.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelRequest

ExportModelRequest.OutputConfig

Output configuration for the Model export.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelRequest.OutputConfig

ExportModelRequest.OutputConfig.Builder

Output configuration for the Model export.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelRequest.OutputConfig

ExportModelResponse

Response message of ModelService.ExportModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelResponse

ExportModelResponse.Builder

Response message of ModelService.ExportModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.ExportModelResponse

ExportTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportTensorboardTimeSeriesDataRequest

ExportTensorboardTimeSeriesDataRequest.Builder

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportTensorboardTimeSeriesDataRequest

ExportTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportTensorboardTimeSeriesDataResponse

ExportTensorboardTimeSeriesDataResponse.Builder

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ExportTensorboardTimeSeriesDataResponse

Feature

Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple.

Protobuf type google.cloud.aiplatform.v1beta1.Feature

Feature.Builder

Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple.

Protobuf type google.cloud.aiplatform.v1beta1.Feature

Feature.MonitoringStatsAnomaly

A list of historical Snapshot Analysis or [Import Feature Analysis] [FeaturestoreMonitoringConfig.ImportFeatureAnalysis] stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Protobuf type google.cloud.aiplatform.v1beta1.Feature.MonitoringStatsAnomaly

Feature.MonitoringStatsAnomaly.Builder

A list of historical Snapshot Analysis or [Import Feature Analysis] [FeaturestoreMonitoringConfig.ImportFeatureAnalysis] stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Protobuf type google.cloud.aiplatform.v1beta1.Feature.MonitoringStatsAnomaly

FeatureMonitoringStatsProto

FeatureName

FeatureName.Builder

Builder for projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature}.

FeatureNoiseSigma

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureNoiseSigma

FeatureNoiseSigma.Builder

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureNoiseSigma

FeatureNoiseSigma.NoiseSigmaForFeature

Noise sigma for a single feature.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature

FeatureNoiseSigma.NoiseSigmaForFeature.Builder

Noise sigma for a single feature.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature

FeatureProto

FeatureSelector

Selector for Features of an EntityType.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureSelector

FeatureSelector.Builder

Selector for Features of an EntityType.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureSelector

FeatureSelectorProto

FeatureStatsAnomaly

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureStatsAnomaly

FeatureStatsAnomaly.Builder

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureStatsAnomaly

FeatureValue

Value for a feature.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValue

FeatureValue.Builder

Value for a feature.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValue

FeatureValue.Metadata

Metadata of feature value.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValue.Metadata

FeatureValue.Metadata.Builder

Metadata of feature value.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValue.Metadata

FeatureValueDestination

A destination location for Feature values and format.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValueDestination

FeatureValueDestination.Builder

A destination location for Feature values and format.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValueDestination

FeatureValueList

Container for list of values.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValueList

FeatureValueList.Builder

Container for list of values.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureValueList

Featurestore

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Protobuf type google.cloud.aiplatform.v1beta1.Featurestore

Featurestore.Builder

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Protobuf type google.cloud.aiplatform.v1beta1.Featurestore

Featurestore.OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Protobuf type google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig

Featurestore.OnlineServingConfig.Builder

OnlineServingConfig specifies the details for provisioning online serving resources.

Protobuf type google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig

Featurestore.OnlineServingConfig.Scaling

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

Protobuf type google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig.Scaling

Featurestore.OnlineServingConfig.Scaling.Builder

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

Protobuf type google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig.Scaling

FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig

FeaturestoreMonitoringConfig.Builder

Configuration of how features in Featurestore are monitored.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig

FeaturestoreMonitoringConfig.ImportFeaturesAnalysis

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every [ImportFeatureValues][] operation.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis

FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Builder

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every [ImportFeatureValues][] operation.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis

FeaturestoreMonitoringConfig.SnapshotAnalysis

Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.SnapshotAnalysis

FeaturestoreMonitoringConfig.SnapshotAnalysis.Builder

Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.SnapshotAnalysis

FeaturestoreMonitoringConfig.ThresholdConfig

The config for Featurestore Monitoring threshold.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ThresholdConfig

FeaturestoreMonitoringConfig.ThresholdConfig.Builder

The config for Featurestore Monitoring threshold.

Protobuf type google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ThresholdConfig

FeaturestoreMonitoringProto

FeaturestoreName

FeaturestoreName.Builder

Builder for projects/{project}/locations/{location}/featurestores/{featurestore}.

FeaturestoreOnlineServiceProto

FeaturestoreOnlineServingServiceClient

Service Description: A service for serving online feature values.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (FeaturestoreOnlineServingServiceClient featurestoreOnlineServingServiceClient =
     FeaturestoreOnlineServingServiceClient.create()) {
   EntityTypeName entityType =
       EntityTypeName.of("[PROJECT]", "[LOCATION]", "[FEATURESTORE]", "[ENTITY_TYPE]");
   ReadFeatureValuesResponse response =
       featurestoreOnlineServingServiceClient.readFeatureValues(entityType);
 }
 

Note: close() needs to be called on the FeaturestoreOnlineServingServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of FeaturestoreOnlineServingServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 FeaturestoreOnlineServingServiceSettings featurestoreOnlineServingServiceSettings =
     FeaturestoreOnlineServingServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 FeaturestoreOnlineServingServiceClient featurestoreOnlineServingServiceClient =
     FeaturestoreOnlineServingServiceClient.create(featurestoreOnlineServingServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 FeaturestoreOnlineServingServiceSettings featurestoreOnlineServingServiceSettings =
     FeaturestoreOnlineServingServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 FeaturestoreOnlineServingServiceClient featurestoreOnlineServingServiceClient =
     FeaturestoreOnlineServingServiceClient.create(featurestoreOnlineServingServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

FeaturestoreOnlineServingServiceClient.ListLocationsFixedSizeCollection

FeaturestoreOnlineServingServiceClient.ListLocationsPage

FeaturestoreOnlineServingServiceClient.ListLocationsPagedResponse

FeaturestoreOnlineServingServiceGrpc

A service for serving online feature values.

FeaturestoreOnlineServingServiceGrpc.FeaturestoreOnlineServingServiceBlockingStub

A service for serving online feature values.

FeaturestoreOnlineServingServiceGrpc.FeaturestoreOnlineServingServiceFutureStub

A service for serving online feature values.

FeaturestoreOnlineServingServiceGrpc.FeaturestoreOnlineServingServiceImplBase

A service for serving online feature values.

FeaturestoreOnlineServingServiceGrpc.FeaturestoreOnlineServingServiceStub

A service for serving online feature values.

FeaturestoreOnlineServingServiceSettings

Settings class to configure an instance of FeaturestoreOnlineServingServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of readFeatureValues to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 FeaturestoreOnlineServingServiceSettings.Builder
     featurestoreOnlineServingServiceSettingsBuilder =
         FeaturestoreOnlineServingServiceSettings.newBuilder();
 featurestoreOnlineServingServiceSettingsBuilder
     .readFeatureValuesSettings()
     .setRetrySettings(
         featurestoreOnlineServingServiceSettingsBuilder
             .readFeatureValuesSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 FeaturestoreOnlineServingServiceSettings featurestoreOnlineServingServiceSettings =
     featurestoreOnlineServingServiceSettingsBuilder.build();
 

FeaturestoreOnlineServingServiceSettings.Builder

Builder for FeaturestoreOnlineServingServiceSettings.

FeaturestoreProto

FeaturestoreServiceClient

Service Description: The service that handles CRUD and List for resources for Featurestore.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (FeaturestoreServiceClient featurestoreServiceClient = FeaturestoreServiceClient.create()) {
   FeaturestoreName name = FeaturestoreName.of("[PROJECT]", "[LOCATION]", "[FEATURESTORE]");
   Featurestore response = featurestoreServiceClient.getFeaturestore(name);
 }
 

Note: close() needs to be called on the FeaturestoreServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of FeaturestoreServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 FeaturestoreServiceSettings featurestoreServiceSettings =
     FeaturestoreServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 FeaturestoreServiceClient featurestoreServiceClient =
     FeaturestoreServiceClient.create(featurestoreServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 FeaturestoreServiceSettings featurestoreServiceSettings =
     FeaturestoreServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 FeaturestoreServiceClient featurestoreServiceClient =
     FeaturestoreServiceClient.create(featurestoreServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

FeaturestoreServiceClient.ListEntityTypesFixedSizeCollection

FeaturestoreServiceClient.ListEntityTypesPage

FeaturestoreServiceClient.ListEntityTypesPagedResponse

FeaturestoreServiceClient.ListFeaturesFixedSizeCollection

FeaturestoreServiceClient.ListFeaturesPage

FeaturestoreServiceClient.ListFeaturesPagedResponse

FeaturestoreServiceClient.ListFeaturestoresFixedSizeCollection

FeaturestoreServiceClient.ListFeaturestoresPage

FeaturestoreServiceClient.ListFeaturestoresPagedResponse

FeaturestoreServiceClient.ListLocationsFixedSizeCollection

FeaturestoreServiceClient.ListLocationsPage

FeaturestoreServiceClient.ListLocationsPagedResponse

FeaturestoreServiceClient.SearchFeaturesFixedSizeCollection

FeaturestoreServiceClient.SearchFeaturesPage

FeaturestoreServiceClient.SearchFeaturesPagedResponse

FeaturestoreServiceGrpc

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceGrpc.FeaturestoreServiceBlockingStub

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceGrpc.FeaturestoreServiceFutureStub

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceGrpc.FeaturestoreServiceImplBase

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceGrpc.FeaturestoreServiceStub

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceProto

FeaturestoreServiceSettings

Settings class to configure an instance of FeaturestoreServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getFeaturestore to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 FeaturestoreServiceSettings.Builder featurestoreServiceSettingsBuilder =
     FeaturestoreServiceSettings.newBuilder();
 featurestoreServiceSettingsBuilder
     .getFeaturestoreSettings()
     .setRetrySettings(
         featurestoreServiceSettingsBuilder
             .getFeaturestoreSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 FeaturestoreServiceSettings featurestoreServiceSettings =
     featurestoreServiceSettingsBuilder.build();
 

FeaturestoreServiceSettings.Builder

Builder for FeaturestoreServiceSettings.

FilterSplit

Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign). Supported only for unstructured Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.FilterSplit

FilterSplit.Builder

Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign). Supported only for unstructured Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.FilterSplit

FractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

Protobuf type google.cloud.aiplatform.v1beta1.FractionSplit

FractionSplit.Builder

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

Protobuf type google.cloud.aiplatform.v1beta1.FractionSplit

GcsDestination

The Google Cloud Storage location where the output is to be written to.

Protobuf type google.cloud.aiplatform.v1beta1.GcsDestination

GcsDestination.Builder

The Google Cloud Storage location where the output is to be written to.

Protobuf type google.cloud.aiplatform.v1beta1.GcsDestination

GcsSource

The Google Cloud Storage location for the input content.

Protobuf type google.cloud.aiplatform.v1beta1.GcsSource

GcsSource.Builder

The Google Cloud Storage location for the input content.

Protobuf type google.cloud.aiplatform.v1beta1.GcsSource

GenericOperationMetadata

Generic Metadata shared by all operations.

Protobuf type google.cloud.aiplatform.v1beta1.GenericOperationMetadata

GenericOperationMetadata.Builder

Generic Metadata shared by all operations.

Protobuf type google.cloud.aiplatform.v1beta1.GenericOperationMetadata

GetAnnotationSpecRequest

Request message for DatasetService.GetAnnotationSpec.

Protobuf type google.cloud.aiplatform.v1beta1.GetAnnotationSpecRequest

GetAnnotationSpecRequest.Builder

Request message for DatasetService.GetAnnotationSpec.

Protobuf type google.cloud.aiplatform.v1beta1.GetAnnotationSpecRequest

GetArtifactRequest

Request message for MetadataService.GetArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.GetArtifactRequest

GetArtifactRequest.Builder

Request message for MetadataService.GetArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.GetArtifactRequest

GetBatchPredictionJobRequest

Request message for JobService.GetBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetBatchPredictionJobRequest

GetBatchPredictionJobRequest.Builder

Request message for JobService.GetBatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetBatchPredictionJobRequest

GetContextRequest

Request message for MetadataService.GetContext.

Protobuf type google.cloud.aiplatform.v1beta1.GetContextRequest

GetContextRequest.Builder

Request message for MetadataService.GetContext.

Protobuf type google.cloud.aiplatform.v1beta1.GetContextRequest

GetCustomJobRequest

Request message for JobService.GetCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetCustomJobRequest

GetCustomJobRequest.Builder

Request message for JobService.GetCustomJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetCustomJobRequest

GetDataLabelingJobRequest

Request message for JobService.GetDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetDataLabelingJobRequest

GetDataLabelingJobRequest.Builder

Request message for JobService.GetDataLabelingJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetDataLabelingJobRequest

GetDatasetRequest

Request message for DatasetService.GetDataset.

Protobuf type google.cloud.aiplatform.v1beta1.GetDatasetRequest

GetDatasetRequest.Builder

Request message for DatasetService.GetDataset.

Protobuf type google.cloud.aiplatform.v1beta1.GetDatasetRequest

GetDeploymentResourcePoolRequest

Request message for GetDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.GetDeploymentResourcePoolRequest

GetDeploymentResourcePoolRequest.Builder

Request message for GetDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.GetDeploymentResourcePoolRequest

GetEndpointRequest

Request message for EndpointService.GetEndpoint

Protobuf type google.cloud.aiplatform.v1beta1.GetEndpointRequest

GetEndpointRequest.Builder

Request message for EndpointService.GetEndpoint

Protobuf type google.cloud.aiplatform.v1beta1.GetEndpointRequest

GetEntityTypeRequest

Request message for FeaturestoreService.GetEntityType.

Protobuf type google.cloud.aiplatform.v1beta1.GetEntityTypeRequest

GetEntityTypeRequest.Builder

Request message for FeaturestoreService.GetEntityType.

Protobuf type google.cloud.aiplatform.v1beta1.GetEntityTypeRequest

GetExecutionRequest

Request message for MetadataService.GetExecution.

Protobuf type google.cloud.aiplatform.v1beta1.GetExecutionRequest

GetExecutionRequest.Builder

Request message for MetadataService.GetExecution.

Protobuf type google.cloud.aiplatform.v1beta1.GetExecutionRequest

GetFeatureRequest

Request message for FeaturestoreService.GetFeature.

Protobuf type google.cloud.aiplatform.v1beta1.GetFeatureRequest

GetFeatureRequest.Builder

Request message for FeaturestoreService.GetFeature.

Protobuf type google.cloud.aiplatform.v1beta1.GetFeatureRequest

GetFeaturestoreRequest

Request message for FeaturestoreService.GetFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.GetFeaturestoreRequest

GetFeaturestoreRequest.Builder

Request message for FeaturestoreService.GetFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.GetFeaturestoreRequest

GetHyperparameterTuningJobRequest

Request message for JobService.GetHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetHyperparameterTuningJobRequest

GetHyperparameterTuningJobRequest.Builder

Request message for JobService.GetHyperparameterTuningJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetHyperparameterTuningJobRequest

GetIndexEndpointRequest

Request message for IndexEndpointService.GetIndexEndpoint

Protobuf type google.cloud.aiplatform.v1beta1.GetIndexEndpointRequest

GetIndexEndpointRequest.Builder

Request message for IndexEndpointService.GetIndexEndpoint

Protobuf type google.cloud.aiplatform.v1beta1.GetIndexEndpointRequest

GetIndexRequest

Request message for IndexService.GetIndex

Protobuf type google.cloud.aiplatform.v1beta1.GetIndexRequest

GetIndexRequest.Builder

Request message for IndexService.GetIndex

Protobuf type google.cloud.aiplatform.v1beta1.GetIndexRequest

GetMetadataSchemaRequest

Request message for MetadataService.GetMetadataSchema.

Protobuf type google.cloud.aiplatform.v1beta1.GetMetadataSchemaRequest

GetMetadataSchemaRequest.Builder

Request message for MetadataService.GetMetadataSchema.

Protobuf type google.cloud.aiplatform.v1beta1.GetMetadataSchemaRequest

GetMetadataStoreRequest

Request message for MetadataService.GetMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.GetMetadataStoreRequest

GetMetadataStoreRequest.Builder

Request message for MetadataService.GetMetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.GetMetadataStoreRequest

GetModelDeploymentMonitoringJobRequest

Request message for JobService.GetModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelDeploymentMonitoringJobRequest

GetModelDeploymentMonitoringJobRequest.Builder

Request message for JobService.GetModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelDeploymentMonitoringJobRequest

GetModelEvaluationRequest

Request message for ModelService.GetModelEvaluation.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelEvaluationRequest

GetModelEvaluationRequest.Builder

Request message for ModelService.GetModelEvaluation.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelEvaluationRequest

GetModelEvaluationSliceRequest

Request message for ModelService.GetModelEvaluationSlice.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelEvaluationSliceRequest

GetModelEvaluationSliceRequest.Builder

Request message for ModelService.GetModelEvaluationSlice.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelEvaluationSliceRequest

GetModelRequest

Request message for ModelService.GetModel.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelRequest

GetModelRequest.Builder

Request message for ModelService.GetModel.

Protobuf type google.cloud.aiplatform.v1beta1.GetModelRequest

GetPipelineJobRequest

Request message for PipelineService.GetPipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetPipelineJobRequest

GetPipelineJobRequest.Builder

Request message for PipelineService.GetPipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.GetPipelineJobRequest

GetSpecialistPoolRequest

Request message for SpecialistPoolService.GetSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.GetSpecialistPoolRequest

GetSpecialistPoolRequest.Builder

Request message for SpecialistPoolService.GetSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.GetSpecialistPoolRequest

GetStudyRequest

Request message for VizierService.GetStudy.

Protobuf type google.cloud.aiplatform.v1beta1.GetStudyRequest

GetStudyRequest.Builder

Request message for VizierService.GetStudy.

Protobuf type google.cloud.aiplatform.v1beta1.GetStudyRequest

GetTensorboardExperimentRequest

Request message for TensorboardService.GetTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardExperimentRequest

GetTensorboardExperimentRequest.Builder

Request message for TensorboardService.GetTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardExperimentRequest

GetTensorboardRequest

Request message for TensorboardService.GetTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardRequest

GetTensorboardRequest.Builder

Request message for TensorboardService.GetTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardRequest

GetTensorboardRunRequest

Request message for TensorboardService.GetTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardRunRequest

GetTensorboardRunRequest.Builder

Request message for TensorboardService.GetTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardRunRequest

GetTensorboardTimeSeriesRequest

Request message for TensorboardService.GetTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardTimeSeriesRequest

GetTensorboardTimeSeriesRequest.Builder

Request message for TensorboardService.GetTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.GetTensorboardTimeSeriesRequest

GetTrainingPipelineRequest

Request message for PipelineService.GetTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.GetTrainingPipelineRequest

GetTrainingPipelineRequest.Builder

Request message for PipelineService.GetTrainingPipeline.

Protobuf type google.cloud.aiplatform.v1beta1.GetTrainingPipelineRequest

GetTrialRequest

Request message for VizierService.GetTrial.

Protobuf type google.cloud.aiplatform.v1beta1.GetTrialRequest

GetTrialRequest.Builder

Request message for VizierService.GetTrial.

Protobuf type google.cloud.aiplatform.v1beta1.GetTrialRequest

HyperparameterTuningJob

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

Protobuf type google.cloud.aiplatform.v1beta1.HyperparameterTuningJob

HyperparameterTuningJob.Builder

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

Protobuf type google.cloud.aiplatform.v1beta1.HyperparameterTuningJob

HyperparameterTuningJobName

HyperparameterTuningJobName.Builder

Builder for projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}.

HyperparameterTuningJobProto

IdMatcher

Matcher for Features of an EntityType by Feature ID.

Protobuf type google.cloud.aiplatform.v1beta1.IdMatcher

IdMatcher.Builder

Matcher for Features of an EntityType by Feature ID.

Protobuf type google.cloud.aiplatform.v1beta1.IdMatcher

ImportDataConfig

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataConfig

ImportDataConfig.Builder

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataConfig

ImportDataOperationMetadata

Runtime operation information for DatasetService.ImportData.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataOperationMetadata

ImportDataOperationMetadata.Builder

Runtime operation information for DatasetService.ImportData.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataOperationMetadata

ImportDataRequest

Request message for DatasetService.ImportData.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataRequest

ImportDataRequest.Builder

Request message for DatasetService.ImportData.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataRequest

ImportDataResponse

Response message for DatasetService.ImportData.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataResponse

ImportDataResponse.Builder

Response message for DatasetService.ImportData.

Protobuf type google.cloud.aiplatform.v1beta1.ImportDataResponse

ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesOperationMetadata

ImportFeatureValuesOperationMetadata.Builder

Details of operations that perform import Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesOperationMetadata

ImportFeatureValuesRequest

Request message for FeaturestoreService.ImportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesRequest

ImportFeatureValuesRequest.Builder

Request message for FeaturestoreService.ImportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesRequest

ImportFeatureValuesRequest.FeatureSpec

Defines the Feature value(s) to import.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesRequest.FeatureSpec

ImportFeatureValuesRequest.FeatureSpec.Builder

Defines the Feature value(s) to import.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesRequest.FeatureSpec

ImportFeatureValuesResponse

Response message for FeaturestoreService.ImportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesResponse

ImportFeatureValuesResponse.Builder

Response message for FeaturestoreService.ImportFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ImportFeatureValuesResponse

ImportModelEvaluationRequest

Request message for ModelService.ImportModelEvaluation

Protobuf type google.cloud.aiplatform.v1beta1.ImportModelEvaluationRequest

ImportModelEvaluationRequest.Builder

Request message for ModelService.ImportModelEvaluation

Protobuf type google.cloud.aiplatform.v1beta1.ImportModelEvaluationRequest

Index

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

Protobuf type google.cloud.aiplatform.v1beta1.Index

Index.Builder

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

Protobuf type google.cloud.aiplatform.v1beta1.Index

IndexDatapoint

A datapoint of Index.

Protobuf type google.cloud.aiplatform.v1beta1.IndexDatapoint

IndexDatapoint.Builder

A datapoint of Index.

Protobuf type google.cloud.aiplatform.v1beta1.IndexDatapoint

IndexDatapoint.CrowdingTag

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

Protobuf type google.cloud.aiplatform.v1beta1.IndexDatapoint.CrowdingTag

IndexDatapoint.CrowdingTag.Builder

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

Protobuf type google.cloud.aiplatform.v1beta1.IndexDatapoint.CrowdingTag

IndexDatapoint.Restriction

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

Protobuf type google.cloud.aiplatform.v1beta1.IndexDatapoint.Restriction

IndexDatapoint.Restriction.Builder

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

Protobuf type google.cloud.aiplatform.v1beta1.IndexDatapoint.Restriction

IndexEndpoint

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.IndexEndpoint

IndexEndpoint.Builder

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.IndexEndpoint

IndexEndpointName

IndexEndpointName.Builder

Builder for projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}.

IndexEndpointProto

IndexEndpointServiceClient

Service Description: A service for managing Vertex AI's IndexEndpoints.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (IndexEndpointServiceClient indexEndpointServiceClient =
     IndexEndpointServiceClient.create()) {
   IndexEndpointName name = IndexEndpointName.of("[PROJECT]", "[LOCATION]", "[INDEX_ENDPOINT]");
   IndexEndpoint response = indexEndpointServiceClient.getIndexEndpoint(name);
 }
 

Note: close() needs to be called on the IndexEndpointServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of IndexEndpointServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 IndexEndpointServiceSettings indexEndpointServiceSettings =
     IndexEndpointServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 IndexEndpointServiceClient indexEndpointServiceClient =
     IndexEndpointServiceClient.create(indexEndpointServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 IndexEndpointServiceSettings indexEndpointServiceSettings =
     IndexEndpointServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 IndexEndpointServiceClient indexEndpointServiceClient =
     IndexEndpointServiceClient.create(indexEndpointServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

IndexEndpointServiceClient.ListIndexEndpointsFixedSizeCollection

IndexEndpointServiceClient.ListIndexEndpointsPage

IndexEndpointServiceClient.ListIndexEndpointsPagedResponse

IndexEndpointServiceClient.ListLocationsFixedSizeCollection

IndexEndpointServiceClient.ListLocationsPage

IndexEndpointServiceClient.ListLocationsPagedResponse

IndexEndpointServiceGrpc

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceGrpc.IndexEndpointServiceBlockingStub

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceGrpc.IndexEndpointServiceFutureStub

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceGrpc.IndexEndpointServiceImplBase

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceGrpc.IndexEndpointServiceStub

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceProto

IndexEndpointServiceSettings

Settings class to configure an instance of IndexEndpointServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getIndexEndpoint to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 IndexEndpointServiceSettings.Builder indexEndpointServiceSettingsBuilder =
     IndexEndpointServiceSettings.newBuilder();
 indexEndpointServiceSettingsBuilder
     .getIndexEndpointSettings()
     .setRetrySettings(
         indexEndpointServiceSettingsBuilder
             .getIndexEndpointSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 IndexEndpointServiceSettings indexEndpointServiceSettings =
     indexEndpointServiceSettingsBuilder.build();
 

IndexEndpointServiceSettings.Builder

Builder for IndexEndpointServiceSettings.

IndexName

IndexName.Builder

Builder for projects/{project}/locations/{location}/indexes/{index}.

IndexPrivateEndpoints

IndexPrivateEndpoints proto is used to provide paths for users to send requests via private endpoints (e.g. private service access, private service connect). To send request via private service access, use match_grpc_address. To send request via private service connect, use service_attachment.

Protobuf type google.cloud.aiplatform.v1beta1.IndexPrivateEndpoints

IndexPrivateEndpoints.Builder

IndexPrivateEndpoints proto is used to provide paths for users to send requests via private endpoints (e.g. private service access, private service connect). To send request via private service access, use match_grpc_address. To send request via private service connect, use service_attachment.

Protobuf type google.cloud.aiplatform.v1beta1.IndexPrivateEndpoints

IndexProto

IndexServiceClient

Service Description: A service for creating and managing Vertex AI's Index resources.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (IndexServiceClient indexServiceClient = IndexServiceClient.create()) {
   IndexName name = IndexName.of("[PROJECT]", "[LOCATION]", "[INDEX]");
   Index response = indexServiceClient.getIndex(name);
 }
 

Note: close() needs to be called on the IndexServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of IndexServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 IndexServiceSettings indexServiceSettings =
     IndexServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 IndexServiceClient indexServiceClient = IndexServiceClient.create(indexServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 IndexServiceSettings indexServiceSettings =
     IndexServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 IndexServiceClient indexServiceClient = IndexServiceClient.create(indexServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

IndexServiceClient.ListIndexesFixedSizeCollection

IndexServiceClient.ListIndexesPage

IndexServiceClient.ListIndexesPagedResponse

IndexServiceClient.ListLocationsFixedSizeCollection

IndexServiceClient.ListLocationsPage

IndexServiceClient.ListLocationsPagedResponse

IndexServiceGrpc

A service for creating and managing Vertex AI's Index resources.

IndexServiceGrpc.IndexServiceBlockingStub

A service for creating and managing Vertex AI's Index resources.

IndexServiceGrpc.IndexServiceFutureStub

A service for creating and managing Vertex AI's Index resources.

IndexServiceGrpc.IndexServiceImplBase

A service for creating and managing Vertex AI's Index resources.

IndexServiceGrpc.IndexServiceStub

A service for creating and managing Vertex AI's Index resources.

IndexServiceProto

IndexServiceSettings

Settings class to configure an instance of IndexServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getIndex to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 IndexServiceSettings.Builder indexServiceSettingsBuilder = IndexServiceSettings.newBuilder();
 indexServiceSettingsBuilder
     .getIndexSettings()
     .setRetrySettings(
         indexServiceSettingsBuilder.getIndexSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 IndexServiceSettings indexServiceSettings = indexServiceSettingsBuilder.build();
 

IndexServiceSettings.Builder

Builder for IndexServiceSettings.

IndexStats

Stats of the Index.

Protobuf type google.cloud.aiplatform.v1beta1.IndexStats

IndexStats.Builder

Stats of the Index.

Protobuf type google.cloud.aiplatform.v1beta1.IndexStats

InputDataConfig

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

Protobuf type google.cloud.aiplatform.v1beta1.InputDataConfig

InputDataConfig.Builder

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

Protobuf type google.cloud.aiplatform.v1beta1.InputDataConfig

Int64Array

A list of int64 values.

Protobuf type google.cloud.aiplatform.v1beta1.Int64Array

Int64Array.Builder

A list of int64 values.

Protobuf type google.cloud.aiplatform.v1beta1.Int64Array

IntegratedGradientsAttribution

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

Protobuf type google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution

IntegratedGradientsAttribution.Builder

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

Protobuf type google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution

IoProto

JobServiceClient

Service Description: A service for creating and managing Vertex AI's jobs.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (JobServiceClient jobServiceClient = JobServiceClient.create()) {
   LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
   CustomJob customJob = CustomJob.newBuilder().build();
   CustomJob response = jobServiceClient.createCustomJob(parent, customJob);
 }
 

Note: close() needs to be called on the JobServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of JobServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 JobServiceSettings jobServiceSettings =
     JobServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 JobServiceSettings jobServiceSettings =
     JobServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

JobServiceClient.ListBatchPredictionJobsFixedSizeCollection

JobServiceClient.ListBatchPredictionJobsPage

JobServiceClient.ListBatchPredictionJobsPagedResponse

JobServiceClient.ListCustomJobsFixedSizeCollection

JobServiceClient.ListCustomJobsPage

JobServiceClient.ListCustomJobsPagedResponse

JobServiceClient.ListDataLabelingJobsFixedSizeCollection

JobServiceClient.ListDataLabelingJobsPage

JobServiceClient.ListDataLabelingJobsPagedResponse

JobServiceClient.ListHyperparameterTuningJobsFixedSizeCollection

JobServiceClient.ListHyperparameterTuningJobsPage

JobServiceClient.ListHyperparameterTuningJobsPagedResponse

JobServiceClient.ListLocationsFixedSizeCollection

JobServiceClient.ListLocationsPage

JobServiceClient.ListLocationsPagedResponse

JobServiceClient.ListModelDeploymentMonitoringJobsFixedSizeCollection

JobServiceClient.ListModelDeploymentMonitoringJobsPage

JobServiceClient.ListModelDeploymentMonitoringJobsPagedResponse

JobServiceClient.SearchModelDeploymentMonitoringStatsAnomaliesFixedSizeCollection

JobServiceClient.SearchModelDeploymentMonitoringStatsAnomaliesPage

JobServiceClient.SearchModelDeploymentMonitoringStatsAnomaliesPagedResponse

JobServiceGrpc

A service for creating and managing Vertex AI's jobs.

JobServiceGrpc.JobServiceBlockingStub

A service for creating and managing Vertex AI's jobs.

JobServiceGrpc.JobServiceFutureStub

A service for creating and managing Vertex AI's jobs.

JobServiceGrpc.JobServiceImplBase

A service for creating and managing Vertex AI's jobs.

JobServiceGrpc.JobServiceStub

A service for creating and managing Vertex AI's jobs.

JobServiceProto

JobServiceSettings

Settings class to configure an instance of JobServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of createCustomJob to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 JobServiceSettings.Builder jobServiceSettingsBuilder = JobServiceSettings.newBuilder();
 jobServiceSettingsBuilder
     .createCustomJobSettings()
     .setRetrySettings(
         jobServiceSettingsBuilder.createCustomJobSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 JobServiceSettings jobServiceSettings = jobServiceSettingsBuilder.build();
 

JobServiceSettings.Builder

Builder for JobServiceSettings.

JobStateProto

LineageSubgraph

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

Protobuf type google.cloud.aiplatform.v1beta1.LineageSubgraph

LineageSubgraph.Builder

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

Protobuf type google.cloud.aiplatform.v1beta1.LineageSubgraph

LineageSubgraphProto

ListAnnotationsRequest

Request message for DatasetService.ListAnnotations.

Protobuf type google.cloud.aiplatform.v1beta1.ListAnnotationsRequest

ListAnnotationsRequest.Builder

Request message for DatasetService.ListAnnotations.

Protobuf type google.cloud.aiplatform.v1beta1.ListAnnotationsRequest

ListAnnotationsResponse

Response message for DatasetService.ListAnnotations.

Protobuf type google.cloud.aiplatform.v1beta1.ListAnnotationsResponse

ListAnnotationsResponse.Builder

Response message for DatasetService.ListAnnotations.

Protobuf type google.cloud.aiplatform.v1beta1.ListAnnotationsResponse

ListArtifactsRequest

Request message for MetadataService.ListArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.ListArtifactsRequest

ListArtifactsRequest.Builder

Request message for MetadataService.ListArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.ListArtifactsRequest

ListArtifactsResponse

Response message for MetadataService.ListArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.ListArtifactsResponse

ListArtifactsResponse.Builder

Response message for MetadataService.ListArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.ListArtifactsResponse

ListBatchPredictionJobsRequest

Request message for JobService.ListBatchPredictionJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListBatchPredictionJobsRequest

ListBatchPredictionJobsRequest.Builder

Request message for JobService.ListBatchPredictionJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListBatchPredictionJobsRequest

ListBatchPredictionJobsResponse

Response message for JobService.ListBatchPredictionJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListBatchPredictionJobsResponse

ListBatchPredictionJobsResponse.Builder

Response message for JobService.ListBatchPredictionJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListBatchPredictionJobsResponse

ListContextsRequest

Request message for MetadataService.ListContexts

Protobuf type google.cloud.aiplatform.v1beta1.ListContextsRequest

ListContextsRequest.Builder

Request message for MetadataService.ListContexts

Protobuf type google.cloud.aiplatform.v1beta1.ListContextsRequest

ListContextsResponse

Response message for MetadataService.ListContexts.

Protobuf type google.cloud.aiplatform.v1beta1.ListContextsResponse

ListContextsResponse.Builder

Response message for MetadataService.ListContexts.

Protobuf type google.cloud.aiplatform.v1beta1.ListContextsResponse

ListCustomJobsRequest

Request message for JobService.ListCustomJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListCustomJobsRequest

ListCustomJobsRequest.Builder

Request message for JobService.ListCustomJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListCustomJobsRequest

ListCustomJobsResponse

Response message for JobService.ListCustomJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListCustomJobsResponse

ListCustomJobsResponse.Builder

Response message for JobService.ListCustomJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListCustomJobsResponse

ListDataItemsRequest

Request message for DatasetService.ListDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataItemsRequest

ListDataItemsRequest.Builder

Request message for DatasetService.ListDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataItemsRequest

ListDataItemsResponse

Response message for DatasetService.ListDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataItemsResponse

ListDataItemsResponse.Builder

Response message for DatasetService.ListDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataItemsResponse

ListDataLabelingJobsRequest

Request message for JobService.ListDataLabelingJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataLabelingJobsRequest

ListDataLabelingJobsRequest.Builder

Request message for JobService.ListDataLabelingJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataLabelingJobsRequest

ListDataLabelingJobsResponse

Response message for JobService.ListDataLabelingJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataLabelingJobsResponse

ListDataLabelingJobsResponse.Builder

Response message for JobService.ListDataLabelingJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListDataLabelingJobsResponse

ListDatasetsRequest

Request message for DatasetService.ListDatasets.

Protobuf type google.cloud.aiplatform.v1beta1.ListDatasetsRequest

ListDatasetsRequest.Builder

Request message for DatasetService.ListDatasets.

Protobuf type google.cloud.aiplatform.v1beta1.ListDatasetsRequest

ListDatasetsResponse

Response message for DatasetService.ListDatasets.

Protobuf type google.cloud.aiplatform.v1beta1.ListDatasetsResponse

ListDatasetsResponse.Builder

Response message for DatasetService.ListDatasets.

Protobuf type google.cloud.aiplatform.v1beta1.ListDatasetsResponse

ListDeploymentResourcePoolsRequest

Request message for ListDeploymentResourcePools method.

Protobuf type google.cloud.aiplatform.v1beta1.ListDeploymentResourcePoolsRequest

ListDeploymentResourcePoolsRequest.Builder

Request message for ListDeploymentResourcePools method.

Protobuf type google.cloud.aiplatform.v1beta1.ListDeploymentResourcePoolsRequest

ListDeploymentResourcePoolsResponse

Response message for ListDeploymentResourcePools method.

Protobuf type google.cloud.aiplatform.v1beta1.ListDeploymentResourcePoolsResponse

ListDeploymentResourcePoolsResponse.Builder

Response message for ListDeploymentResourcePools method.

Protobuf type google.cloud.aiplatform.v1beta1.ListDeploymentResourcePoolsResponse

ListEndpointsRequest

Request message for EndpointService.ListEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListEndpointsRequest

ListEndpointsRequest.Builder

Request message for EndpointService.ListEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListEndpointsRequest

ListEndpointsResponse

Response message for EndpointService.ListEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListEndpointsResponse

ListEndpointsResponse.Builder

Response message for EndpointService.ListEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListEndpointsResponse

ListEntityTypesRequest

Request message for FeaturestoreService.ListEntityTypes.

Protobuf type google.cloud.aiplatform.v1beta1.ListEntityTypesRequest

ListEntityTypesRequest.Builder

Request message for FeaturestoreService.ListEntityTypes.

Protobuf type google.cloud.aiplatform.v1beta1.ListEntityTypesRequest

ListEntityTypesResponse

Response message for FeaturestoreService.ListEntityTypes.

Protobuf type google.cloud.aiplatform.v1beta1.ListEntityTypesResponse

ListEntityTypesResponse.Builder

Response message for FeaturestoreService.ListEntityTypes.

Protobuf type google.cloud.aiplatform.v1beta1.ListEntityTypesResponse

ListExecutionsRequest

Request message for MetadataService.ListExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.ListExecutionsRequest

ListExecutionsRequest.Builder

Request message for MetadataService.ListExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.ListExecutionsRequest

ListExecutionsResponse

Response message for MetadataService.ListExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.ListExecutionsResponse

ListExecutionsResponse.Builder

Response message for MetadataService.ListExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.ListExecutionsResponse

ListFeaturesRequest

Request message for FeaturestoreService.ListFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturesRequest

ListFeaturesRequest.Builder

Request message for FeaturestoreService.ListFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturesRequest

ListFeaturesResponse

Response message for FeaturestoreService.ListFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturesResponse

ListFeaturesResponse.Builder

Response message for FeaturestoreService.ListFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturesResponse

ListFeaturestoresRequest

Request message for FeaturestoreService.ListFeaturestores.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturestoresRequest

ListFeaturestoresRequest.Builder

Request message for FeaturestoreService.ListFeaturestores.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturestoresRequest

ListFeaturestoresResponse

Response message for FeaturestoreService.ListFeaturestores.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturestoresResponse

ListFeaturestoresResponse.Builder

Response message for FeaturestoreService.ListFeaturestores.

Protobuf type google.cloud.aiplatform.v1beta1.ListFeaturestoresResponse

ListHyperparameterTuningJobsRequest

Request message for JobService.ListHyperparameterTuningJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListHyperparameterTuningJobsRequest

ListHyperparameterTuningJobsRequest.Builder

Request message for JobService.ListHyperparameterTuningJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListHyperparameterTuningJobsRequest

ListHyperparameterTuningJobsResponse

Response message for JobService.ListHyperparameterTuningJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListHyperparameterTuningJobsResponse

ListHyperparameterTuningJobsResponse.Builder

Response message for JobService.ListHyperparameterTuningJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListHyperparameterTuningJobsResponse

ListIndexEndpointsRequest

Request message for IndexEndpointService.ListIndexEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexEndpointsRequest

ListIndexEndpointsRequest.Builder

Request message for IndexEndpointService.ListIndexEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexEndpointsRequest

ListIndexEndpointsResponse

Response message for IndexEndpointService.ListIndexEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexEndpointsResponse

ListIndexEndpointsResponse.Builder

Response message for IndexEndpointService.ListIndexEndpoints.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexEndpointsResponse

ListIndexesRequest

Request message for IndexService.ListIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexesRequest

ListIndexesRequest.Builder

Request message for IndexService.ListIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexesRequest

ListIndexesResponse

Response message for IndexService.ListIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexesResponse

ListIndexesResponse.Builder

Response message for IndexService.ListIndexes.

Protobuf type google.cloud.aiplatform.v1beta1.ListIndexesResponse

ListMetadataSchemasRequest

Request message for MetadataService.ListMetadataSchemas.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataSchemasRequest

ListMetadataSchemasRequest.Builder

Request message for MetadataService.ListMetadataSchemas.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataSchemasRequest

ListMetadataSchemasResponse

Response message for MetadataService.ListMetadataSchemas.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataSchemasResponse

ListMetadataSchemasResponse.Builder

Response message for MetadataService.ListMetadataSchemas.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataSchemasResponse

ListMetadataStoresRequest

Request message for MetadataService.ListMetadataStores.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataStoresRequest

ListMetadataStoresRequest.Builder

Request message for MetadataService.ListMetadataStores.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataStoresRequest

ListMetadataStoresResponse

Response message for MetadataService.ListMetadataStores.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataStoresResponse

ListMetadataStoresResponse.Builder

Response message for MetadataService.ListMetadataStores.

Protobuf type google.cloud.aiplatform.v1beta1.ListMetadataStoresResponse

ListModelDeploymentMonitoringJobsRequest

Request message for JobService.ListModelDeploymentMonitoringJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelDeploymentMonitoringJobsRequest

ListModelDeploymentMonitoringJobsRequest.Builder

Request message for JobService.ListModelDeploymentMonitoringJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelDeploymentMonitoringJobsRequest

ListModelDeploymentMonitoringJobsResponse

Response message for JobService.ListModelDeploymentMonitoringJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelDeploymentMonitoringJobsResponse

ListModelDeploymentMonitoringJobsResponse.Builder

Response message for JobService.ListModelDeploymentMonitoringJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelDeploymentMonitoringJobsResponse

ListModelEvaluationSlicesRequest

Request message for ModelService.ListModelEvaluationSlices.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationSlicesRequest

ListModelEvaluationSlicesRequest.Builder

Request message for ModelService.ListModelEvaluationSlices.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationSlicesRequest

ListModelEvaluationSlicesResponse

Response message for ModelService.ListModelEvaluationSlices.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationSlicesResponse

ListModelEvaluationSlicesResponse.Builder

Response message for ModelService.ListModelEvaluationSlices.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationSlicesResponse

ListModelEvaluationsRequest

Request message for ModelService.ListModelEvaluations.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationsRequest

ListModelEvaluationsRequest.Builder

Request message for ModelService.ListModelEvaluations.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationsRequest

ListModelEvaluationsResponse

Response message for ModelService.ListModelEvaluations.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationsResponse

ListModelEvaluationsResponse.Builder

Response message for ModelService.ListModelEvaluations.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelEvaluationsResponse

ListModelVersionsRequest

Request message for ModelService.ListModelVersions.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelVersionsRequest

ListModelVersionsRequest.Builder

Request message for ModelService.ListModelVersions.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelVersionsRequest

ListModelVersionsResponse

Response message for ModelService.ListModelVersions

Protobuf type google.cloud.aiplatform.v1beta1.ListModelVersionsResponse

ListModelVersionsResponse.Builder

Response message for ModelService.ListModelVersions

Protobuf type google.cloud.aiplatform.v1beta1.ListModelVersionsResponse

ListModelsRequest

Request message for ModelService.ListModels.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelsRequest

ListModelsRequest.Builder

Request message for ModelService.ListModels.

Protobuf type google.cloud.aiplatform.v1beta1.ListModelsRequest

ListModelsResponse

Response message for ModelService.ListModels

Protobuf type google.cloud.aiplatform.v1beta1.ListModelsResponse

ListModelsResponse.Builder

Response message for ModelService.ListModels

Protobuf type google.cloud.aiplatform.v1beta1.ListModelsResponse

ListOptimalTrialsRequest

Request message for VizierService.ListOptimalTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListOptimalTrialsRequest

ListOptimalTrialsRequest.Builder

Request message for VizierService.ListOptimalTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListOptimalTrialsRequest

ListOptimalTrialsResponse

Response message for VizierService.ListOptimalTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListOptimalTrialsResponse

ListOptimalTrialsResponse.Builder

Response message for VizierService.ListOptimalTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListOptimalTrialsResponse

ListPipelineJobsRequest

Request message for PipelineService.ListPipelineJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListPipelineJobsRequest

ListPipelineJobsRequest.Builder

Request message for PipelineService.ListPipelineJobs.

Protobuf type google.cloud.aiplatform.v1beta1.ListPipelineJobsRequest

ListPipelineJobsResponse

Response message for PipelineService.ListPipelineJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListPipelineJobsResponse

ListPipelineJobsResponse.Builder

Response message for PipelineService.ListPipelineJobs

Protobuf type google.cloud.aiplatform.v1beta1.ListPipelineJobsResponse

ListSavedQueriesRequest

Request message for DatasetService.ListSavedQueries.

Protobuf type google.cloud.aiplatform.v1beta1.ListSavedQueriesRequest

ListSavedQueriesRequest.Builder

Request message for DatasetService.ListSavedQueries.

Protobuf type google.cloud.aiplatform.v1beta1.ListSavedQueriesRequest

ListSavedQueriesResponse

Response message for DatasetService.ListSavedQueries.

Protobuf type google.cloud.aiplatform.v1beta1.ListSavedQueriesResponse

ListSavedQueriesResponse.Builder

Response message for DatasetService.ListSavedQueries.

Protobuf type google.cloud.aiplatform.v1beta1.ListSavedQueriesResponse

ListSpecialistPoolsRequest

Request message for SpecialistPoolService.ListSpecialistPools.

Protobuf type google.cloud.aiplatform.v1beta1.ListSpecialistPoolsRequest

ListSpecialistPoolsRequest.Builder

Request message for SpecialistPoolService.ListSpecialistPools.

Protobuf type google.cloud.aiplatform.v1beta1.ListSpecialistPoolsRequest

ListSpecialistPoolsResponse

Response message for SpecialistPoolService.ListSpecialistPools.

Protobuf type google.cloud.aiplatform.v1beta1.ListSpecialistPoolsResponse

ListSpecialistPoolsResponse.Builder

Response message for SpecialistPoolService.ListSpecialistPools.

Protobuf type google.cloud.aiplatform.v1beta1.ListSpecialistPoolsResponse

ListStudiesRequest

Request message for VizierService.ListStudies.

Protobuf type google.cloud.aiplatform.v1beta1.ListStudiesRequest

ListStudiesRequest.Builder

Request message for VizierService.ListStudies.

Protobuf type google.cloud.aiplatform.v1beta1.ListStudiesRequest

ListStudiesResponse

Response message for VizierService.ListStudies.

Protobuf type google.cloud.aiplatform.v1beta1.ListStudiesResponse

ListStudiesResponse.Builder

Response message for VizierService.ListStudies.

Protobuf type google.cloud.aiplatform.v1beta1.ListStudiesResponse

ListTensorboardExperimentsRequest

Request message for TensorboardService.ListTensorboardExperiments.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardExperimentsRequest

ListTensorboardExperimentsRequest.Builder

Request message for TensorboardService.ListTensorboardExperiments.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardExperimentsRequest

ListTensorboardExperimentsResponse

Response message for TensorboardService.ListTensorboardExperiments.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardExperimentsResponse

ListTensorboardExperimentsResponse.Builder

Response message for TensorboardService.ListTensorboardExperiments.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardExperimentsResponse

ListTensorboardRunsRequest

Request message for TensorboardService.ListTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardRunsRequest

ListTensorboardRunsRequest.Builder

Request message for TensorboardService.ListTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardRunsRequest

ListTensorboardRunsResponse

Response message for TensorboardService.ListTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardRunsResponse

ListTensorboardRunsResponse.Builder

Response message for TensorboardService.ListTensorboardRuns.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardRunsResponse

ListTensorboardTimeSeriesRequest

Request message for TensorboardService.ListTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardTimeSeriesRequest

ListTensorboardTimeSeriesRequest.Builder

Request message for TensorboardService.ListTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardTimeSeriesRequest

ListTensorboardTimeSeriesResponse

Response message for TensorboardService.ListTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardTimeSeriesResponse

ListTensorboardTimeSeriesResponse.Builder

Response message for TensorboardService.ListTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardTimeSeriesResponse

ListTensorboardsRequest

Request message for TensorboardService.ListTensorboards.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardsRequest

ListTensorboardsRequest.Builder

Request message for TensorboardService.ListTensorboards.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardsRequest

ListTensorboardsResponse

Response message for TensorboardService.ListTensorboards.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardsResponse

ListTensorboardsResponse.Builder

Response message for TensorboardService.ListTensorboards.

Protobuf type google.cloud.aiplatform.v1beta1.ListTensorboardsResponse

ListTrainingPipelinesRequest

Request message for PipelineService.ListTrainingPipelines.

Protobuf type google.cloud.aiplatform.v1beta1.ListTrainingPipelinesRequest

ListTrainingPipelinesRequest.Builder

Request message for PipelineService.ListTrainingPipelines.

Protobuf type google.cloud.aiplatform.v1beta1.ListTrainingPipelinesRequest

ListTrainingPipelinesResponse

Response message for PipelineService.ListTrainingPipelines

Protobuf type google.cloud.aiplatform.v1beta1.ListTrainingPipelinesResponse

ListTrainingPipelinesResponse.Builder

Response message for PipelineService.ListTrainingPipelines

Protobuf type google.cloud.aiplatform.v1beta1.ListTrainingPipelinesResponse

ListTrialsRequest

Request message for VizierService.ListTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListTrialsRequest

ListTrialsRequest.Builder

Request message for VizierService.ListTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListTrialsRequest

ListTrialsResponse

Response message for VizierService.ListTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListTrialsResponse

ListTrialsResponse.Builder

Response message for VizierService.ListTrials.

Protobuf type google.cloud.aiplatform.v1beta1.ListTrialsResponse

LocationName

LocationName.Builder

Builder for projects/{project}/locations/{location}.

LookupStudyRequest

Request message for VizierService.LookupStudy.

Protobuf type google.cloud.aiplatform.v1beta1.LookupStudyRequest

LookupStudyRequest.Builder

Request message for VizierService.LookupStudy.

Protobuf type google.cloud.aiplatform.v1beta1.LookupStudyRequest

MachineResourcesProto

MachineSpec

Specification of a single machine.

Protobuf type google.cloud.aiplatform.v1beta1.MachineSpec

MachineSpec.Builder

Specification of a single machine.

Protobuf type google.cloud.aiplatform.v1beta1.MachineSpec

ManualBatchTuningParameters

Manual batch tuning parameters.

Protobuf type google.cloud.aiplatform.v1beta1.ManualBatchTuningParameters

ManualBatchTuningParameters.Builder

Manual batch tuning parameters.

Protobuf type google.cloud.aiplatform.v1beta1.ManualBatchTuningParameters

ManualBatchTuningParametersProto

Measurement

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Protobuf type google.cloud.aiplatform.v1beta1.Measurement

Measurement.Builder

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Protobuf type google.cloud.aiplatform.v1beta1.Measurement

Measurement.Metric

A message representing a metric in the measurement.

Protobuf type google.cloud.aiplatform.v1beta1.Measurement.Metric

Measurement.Metric.Builder

A message representing a metric in the measurement.

Protobuf type google.cloud.aiplatform.v1beta1.Measurement.Metric

MergeVersionAliasesRequest

Request message for ModelService.MergeVersionAliases.

Protobuf type google.cloud.aiplatform.v1beta1.MergeVersionAliasesRequest

MergeVersionAliasesRequest.Builder

Request message for ModelService.MergeVersionAliases.

Protobuf type google.cloud.aiplatform.v1beta1.MergeVersionAliasesRequest

MetadataProto

MetadataSchema

Instance of a general MetadataSchema.

Protobuf type google.cloud.aiplatform.v1beta1.MetadataSchema

MetadataSchema.Builder

Instance of a general MetadataSchema.

Protobuf type google.cloud.aiplatform.v1beta1.MetadataSchema

MetadataSchemaName

MetadataSchemaName.Builder

Builder for projects/{project}/locations/{location}/metadataStores/{metadata_store}/metadataSchemas/{metadata_schema}.

MetadataSchemaProto

MetadataServiceClient

Service Description: Service for reading and writing metadata entries.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (MetadataServiceClient metadataServiceClient = MetadataServiceClient.create()) {
   MetadataStoreName name = MetadataStoreName.of("[PROJECT]", "[LOCATION]", "[METADATA_STORE]");
   MetadataStore response = metadataServiceClient.getMetadataStore(name);
 }
 

Note: close() needs to be called on the MetadataServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of MetadataServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 MetadataServiceSettings metadataServiceSettings =
     MetadataServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 MetadataServiceClient metadataServiceClient =
     MetadataServiceClient.create(metadataServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 MetadataServiceSettings metadataServiceSettings =
     MetadataServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 MetadataServiceClient metadataServiceClient =
     MetadataServiceClient.create(metadataServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

MetadataServiceClient.ListArtifactsFixedSizeCollection

MetadataServiceClient.ListArtifactsPage

MetadataServiceClient.ListArtifactsPagedResponse

MetadataServiceClient.ListContextsFixedSizeCollection

MetadataServiceClient.ListContextsPage

MetadataServiceClient.ListContextsPagedResponse

MetadataServiceClient.ListExecutionsFixedSizeCollection

MetadataServiceClient.ListExecutionsPage

MetadataServiceClient.ListExecutionsPagedResponse

MetadataServiceClient.ListLocationsFixedSizeCollection

MetadataServiceClient.ListLocationsPage

MetadataServiceClient.ListLocationsPagedResponse

MetadataServiceClient.ListMetadataSchemasFixedSizeCollection

MetadataServiceClient.ListMetadataSchemasPage

MetadataServiceClient.ListMetadataSchemasPagedResponse

MetadataServiceClient.ListMetadataStoresFixedSizeCollection

MetadataServiceClient.ListMetadataStoresPage

MetadataServiceClient.ListMetadataStoresPagedResponse

MetadataServiceGrpc

Service for reading and writing metadata entries.

MetadataServiceGrpc.MetadataServiceBlockingStub

Service for reading and writing metadata entries.

MetadataServiceGrpc.MetadataServiceFutureStub

Service for reading and writing metadata entries.

MetadataServiceGrpc.MetadataServiceImplBase

Service for reading and writing metadata entries.

MetadataServiceGrpc.MetadataServiceStub

Service for reading and writing metadata entries.

MetadataServiceProto

MetadataServiceSettings

Settings class to configure an instance of MetadataServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getMetadataStore to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 MetadataServiceSettings.Builder metadataServiceSettingsBuilder =
     MetadataServiceSettings.newBuilder();
 metadataServiceSettingsBuilder
     .getMetadataStoreSettings()
     .setRetrySettings(
         metadataServiceSettingsBuilder.getMetadataStoreSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 MetadataServiceSettings metadataServiceSettings = metadataServiceSettingsBuilder.build();
 

MetadataServiceSettings.Builder

Builder for MetadataServiceSettings.

MetadataStore

Instance of a metadata store. Contains a set of metadata that can be queried.

Protobuf type google.cloud.aiplatform.v1beta1.MetadataStore

MetadataStore.Builder

Instance of a metadata store. Contains a set of metadata that can be queried.

Protobuf type google.cloud.aiplatform.v1beta1.MetadataStore

MetadataStore.MetadataStoreState

Represents state information for a MetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.MetadataStore.MetadataStoreState

MetadataStore.MetadataStoreState.Builder

Represents state information for a MetadataStore.

Protobuf type google.cloud.aiplatform.v1beta1.MetadataStore.MetadataStoreState

MetadataStoreName

MetadataStoreName.Builder

Builder for projects/{project}/locations/{location}/metadataStores/{metadata_store}.

MigratableResource

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource

MigratableResource.AutomlDataset

Represents one Dataset in automl.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.AutomlDataset

MigratableResource.AutomlDataset.Builder

Represents one Dataset in automl.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.AutomlDataset

MigratableResource.AutomlModel

Represents one Model in automl.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.AutomlModel

MigratableResource.AutomlModel.Builder

Represents one Model in automl.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.AutomlModel

MigratableResource.Builder

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource

MigratableResource.DataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.DataLabelingDataset

MigratableResource.DataLabelingDataset.Builder

Represents one Dataset in datalabeling.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.DataLabelingDataset

MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDataset

MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDataset.Builder

Represents one AnnotatedDataset in datalabeling.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDataset

MigratableResource.MlEngineModelVersion

Represents one model Version in ml.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.MlEngineModelVersion

MigratableResource.MlEngineModelVersion.Builder

Represents one model Version in ml.googleapis.com.

Protobuf type google.cloud.aiplatform.v1beta1.MigratableResource.MlEngineModelVersion

MigratableResourceProto

MigrateResourceRequest

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest

MigrateResourceRequest.Builder

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest

MigrateResourceRequest.MigrateAutomlDatasetConfig

Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateAutomlDatasetConfig

MigrateResourceRequest.MigrateAutomlDatasetConfig.Builder

Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateAutomlDatasetConfig

MigrateResourceRequest.MigrateAutomlModelConfig

Config for migrating Model in automl.googleapis.com to Vertex AI's Model.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateAutomlModelConfig

MigrateResourceRequest.MigrateAutomlModelConfig.Builder

Config for migrating Model in automl.googleapis.com to Vertex AI's Model.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateAutomlModelConfig

MigrateResourceRequest.MigrateDataLabelingDatasetConfig

Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateDataLabelingDatasetConfig

MigrateResourceRequest.MigrateDataLabelingDatasetConfig.Builder

Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateDataLabelingDatasetConfig

MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfig

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfig

MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfig.Builder

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfig

MigrateResourceRequest.MigrateMlEngineModelVersionConfig

Config for migrating version in ml.googleapis.com to Vertex AI's Model.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateMlEngineModelVersionConfig

MigrateResourceRequest.MigrateMlEngineModelVersionConfig.Builder

Config for migrating version in ml.googleapis.com to Vertex AI's Model.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceRequest.MigrateMlEngineModelVersionConfig

MigrateResourceResponse

Describes a successfully migrated resource.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceResponse

MigrateResourceResponse.Builder

Describes a successfully migrated resource.

Protobuf type google.cloud.aiplatform.v1beta1.MigrateResourceResponse

MigrationServiceClient

Service Description: A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (MigrationServiceClient migrationServiceClient = MigrationServiceClient.create()) {
   GetLocationRequest request = GetLocationRequest.newBuilder().setName("name3373707").build();
   Location response = migrationServiceClient.getLocation(request);
 }
 

Note: close() needs to be called on the MigrationServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of MigrationServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 MigrationServiceSettings migrationServiceSettings =
     MigrationServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 MigrationServiceClient migrationServiceClient =
     MigrationServiceClient.create(migrationServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 MigrationServiceSettings migrationServiceSettings =
     MigrationServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 MigrationServiceClient migrationServiceClient =
     MigrationServiceClient.create(migrationServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

MigrationServiceClient.ListLocationsFixedSizeCollection

MigrationServiceClient.ListLocationsPage

MigrationServiceClient.ListLocationsPagedResponse

MigrationServiceClient.SearchMigratableResourcesFixedSizeCollection

MigrationServiceClient.SearchMigratableResourcesPage

MigrationServiceClient.SearchMigratableResourcesPagedResponse

MigrationServiceGrpc

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceGrpc.MigrationServiceBlockingStub

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceGrpc.MigrationServiceFutureStub

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceGrpc.MigrationServiceImplBase

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceGrpc.MigrationServiceStub

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceProto

MigrationServiceSettings

Settings class to configure an instance of MigrationServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getLocation to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 MigrationServiceSettings.Builder migrationServiceSettingsBuilder =
     MigrationServiceSettings.newBuilder();
 migrationServiceSettingsBuilder
     .getLocationSettings()
     .setRetrySettings(
         migrationServiceSettingsBuilder.getLocationSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 MigrationServiceSettings migrationServiceSettings = migrationServiceSettingsBuilder.build();
 

MigrationServiceSettings.Builder

Builder for MigrationServiceSettings.

Model

A trained machine learning Model.

Protobuf type google.cloud.aiplatform.v1beta1.Model

Model.Builder

A trained machine learning Model.

Protobuf type google.cloud.aiplatform.v1beta1.Model

Model.ExportFormat

Represents export format supported by the Model. All formats export to Google Cloud Storage.

Protobuf type google.cloud.aiplatform.v1beta1.Model.ExportFormat

Model.ExportFormat.Builder

Represents export format supported by the Model. All formats export to Google Cloud Storage.

Protobuf type google.cloud.aiplatform.v1beta1.Model.ExportFormat

ModelContainerSpec

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.

Protobuf type google.cloud.aiplatform.v1beta1.ModelContainerSpec

ModelContainerSpec.Builder

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.

Protobuf type google.cloud.aiplatform.v1beta1.ModelContainerSpec

ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringBigQueryTable.Builder

ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringJob

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob

ModelDeploymentMonitoringJob.Builder

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob

ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata

ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata.Builder

All metadata of most recent monitoring pipelines.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata

ModelDeploymentMonitoringJobName

ModelDeploymentMonitoringJobName.Builder

Builder for projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}.

ModelDeploymentMonitoringJobProto

ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig.Builder

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringScheduleConfig

ModelDeploymentMonitoringScheduleConfig.Builder

The config for scheduling monitoring job.

Protobuf type google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringScheduleConfig

ModelEvaluation

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation

ModelEvaluation.Builder

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation

ModelEvaluation.ModelEvaluationExplanationSpec

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation.ModelEvaluationExplanationSpec

ModelEvaluation.ModelEvaluationExplanationSpec.Builder

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation.ModelEvaluationExplanationSpec

ModelEvaluationName

ModelEvaluationName.Builder

Builder for projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}.

ModelEvaluationProto

ModelEvaluationSlice

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluationSlice

ModelEvaluationSlice.Builder

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluationSlice

ModelEvaluationSlice.Slice

Definition of a slice.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice

ModelEvaluationSlice.Slice.Builder

Definition of a slice.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice

ModelEvaluationSliceName

ModelEvaluationSliceName.Builder

Builder for projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}.

ModelEvaluationSliceProto

ModelExplanation

Aggregated explanation metrics for a Model over a set of instances.

Protobuf type google.cloud.aiplatform.v1beta1.ModelExplanation

ModelExplanation.Builder

Aggregated explanation metrics for a Model over a set of instances.

Protobuf type google.cloud.aiplatform.v1beta1.ModelExplanation

ModelMonitoringAlertConfig

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig

ModelMonitoringAlertConfig.Builder

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig

ModelMonitoringAlertConfig.EmailAlertConfig

The config for email alert.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig.EmailAlertConfig

ModelMonitoringAlertConfig.EmailAlertConfig.Builder

The config for email alert.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig.EmailAlertConfig

ModelMonitoringConfig

The model monitoring configuration used for Batch Prediction Job.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringConfig

ModelMonitoringConfig.Builder

The model monitoring configuration used for Batch Prediction Job.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringConfig

ModelMonitoringObjectiveConfig

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig

ModelMonitoringObjectiveConfig.Builder

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig

ModelMonitoringObjectiveConfig.ExplanationConfig

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig

ModelMonitoringObjectiveConfig.ExplanationConfig.Builder

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig

ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline

Output from BatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline

ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.Builder

Output from BatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline

ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig

The config for Prediction data drift detection.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig

ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.Builder

The config for Prediction data drift detection.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig

ModelMonitoringObjectiveConfig.TrainingDataset

Training Dataset information.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset

ModelMonitoringObjectiveConfig.TrainingDataset.Builder

Training Dataset information.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset

ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

ModelMonitoringProto

ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringStatsAnomalies

ModelMonitoringStatsAnomalies.Builder

Statistics and anomalies generated by Model Monitoring.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringStatsAnomalies

ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific Feature.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies

ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.Builder

Historical Stats (and Anomalies) for a specific Feature.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies

ModelName

ModelName.Builder

Builder for projects/{project}/locations/{location}/models/{model}.

ModelProto

ModelServiceClient

Service Description: A service for managing Vertex AI's machine learning Models.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (ModelServiceClient modelServiceClient = ModelServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   Model response = modelServiceClient.getModel(name);
 }
 

Note: close() needs to be called on the ModelServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of ModelServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 ModelServiceSettings modelServiceSettings =
     ModelServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 ModelServiceSettings modelServiceSettings =
     ModelServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

ModelServiceClient.ListLocationsFixedSizeCollection

ModelServiceClient.ListLocationsPage

ModelServiceClient.ListLocationsPagedResponse

ModelServiceClient.ListModelEvaluationSlicesFixedSizeCollection

ModelServiceClient.ListModelEvaluationSlicesPage

ModelServiceClient.ListModelEvaluationSlicesPagedResponse

ModelServiceClient.ListModelEvaluationsFixedSizeCollection

ModelServiceClient.ListModelEvaluationsPage

ModelServiceClient.ListModelEvaluationsPagedResponse

ModelServiceClient.ListModelVersionsFixedSizeCollection

ModelServiceClient.ListModelVersionsPage

ModelServiceClient.ListModelVersionsPagedResponse

ModelServiceClient.ListModelsFixedSizeCollection

ModelServiceClient.ListModelsPage

ModelServiceClient.ListModelsPagedResponse

ModelServiceGrpc

A service for managing Vertex AI's machine learning Models.

ModelServiceGrpc.ModelServiceBlockingStub

A service for managing Vertex AI's machine learning Models.

ModelServiceGrpc.ModelServiceFutureStub

A service for managing Vertex AI's machine learning Models.

ModelServiceGrpc.ModelServiceImplBase

A service for managing Vertex AI's machine learning Models.

ModelServiceGrpc.ModelServiceStub

A service for managing Vertex AI's machine learning Models.

ModelServiceProto

ModelServiceSettings

Settings class to configure an instance of ModelServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getModel to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 ModelServiceSettings.Builder modelServiceSettingsBuilder = ModelServiceSettings.newBuilder();
 modelServiceSettingsBuilder
     .getModelSettings()
     .setRetrySettings(
         modelServiceSettingsBuilder.getModelSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 ModelServiceSettings modelServiceSettings = modelServiceSettingsBuilder.build();
 

ModelServiceSettings.Builder

Builder for ModelServiceSettings.

ModelSourceInfo

Detail description of the source information of the model.

Protobuf type google.cloud.aiplatform.v1beta1.ModelSourceInfo

ModelSourceInfo.Builder

Detail description of the source information of the model.

Protobuf type google.cloud.aiplatform.v1beta1.ModelSourceInfo

MutateDeployedIndexOperationMetadata

Runtime operation information for IndexEndpointService.MutateDeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.MutateDeployedIndexOperationMetadata

MutateDeployedIndexOperationMetadata.Builder

Runtime operation information for IndexEndpointService.MutateDeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.MutateDeployedIndexOperationMetadata

MutateDeployedIndexRequest

Request message for IndexEndpointService.MutateDeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.MutateDeployedIndexRequest

MutateDeployedIndexRequest.Builder

Request message for IndexEndpointService.MutateDeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.MutateDeployedIndexRequest

MutateDeployedIndexResponse

Response message for IndexEndpointService.MutateDeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.MutateDeployedIndexResponse

MutateDeployedIndexResponse.Builder

Response message for IndexEndpointService.MutateDeployedIndex.

Protobuf type google.cloud.aiplatform.v1beta1.MutateDeployedIndexResponse

NearestNeighborSearchOperationMetadata

Runtime operation metadata with regard to Matching Engine Index.

Protobuf type google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata

NearestNeighborSearchOperationMetadata.Builder

Runtime operation metadata with regard to Matching Engine Index.

Protobuf type google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata

NearestNeighborSearchOperationMetadata.ContentValidationStats

Protobuf type google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata.ContentValidationStats

NearestNeighborSearchOperationMetadata.ContentValidationStats.Builder

Protobuf type google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata.ContentValidationStats

NearestNeighborSearchOperationMetadata.RecordError

Protobuf type google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata.RecordError

NearestNeighborSearchOperationMetadata.RecordError.Builder

Protobuf type google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata.RecordError

Neighbor

Neighbors for example-based explanations.

Protobuf type google.cloud.aiplatform.v1beta1.Neighbor

Neighbor.Builder

Neighbors for example-based explanations.

Protobuf type google.cloud.aiplatform.v1beta1.Neighbor

NfsMount

Represents a mount configuration for Network File System (NFS) to mount.

Protobuf type google.cloud.aiplatform.v1beta1.NfsMount

NfsMount.Builder

Represents a mount configuration for Network File System (NFS) to mount.

Protobuf type google.cloud.aiplatform.v1beta1.NfsMount

OperationProto

PauseModelDeploymentMonitoringJobRequest

Request message for JobService.PauseModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.PauseModelDeploymentMonitoringJobRequest

PauseModelDeploymentMonitoringJobRequest.Builder

Request message for JobService.PauseModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.PauseModelDeploymentMonitoringJobRequest

Pipeline

PipelineFailurePolicyProto

PipelineJob

An instance of a machine learning PipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJob

PipelineJob.Builder

An instance of a machine learning PipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJob

PipelineJob.RuntimeConfig

The runtime config of a PipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJob.RuntimeConfig

PipelineJob.RuntimeConfig.Builder

The runtime config of a PipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJob.RuntimeConfig

PipelineJob.RuntimeConfig.InputArtifact

The type of an input artifact.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJob.RuntimeConfig.InputArtifact

PipelineJob.RuntimeConfig.InputArtifact.Builder

The type of an input artifact.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJob.RuntimeConfig.InputArtifact

PipelineJobDetail

The runtime detail of PipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJobDetail

PipelineJobDetail.Builder

The runtime detail of PipelineJob.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineJobDetail

PipelineJobName

PipelineJobName.Builder

Builder for projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}.

PipelineServiceClient

Service Description: A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PipelineServiceClient pipelineServiceClient = PipelineServiceClient.create()) {
   LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
   TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().build();
   TrainingPipeline response =
       pipelineServiceClient.createTrainingPipeline(parent, trainingPipeline);
 }
 

Note: close() needs to be called on the PipelineServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of PipelineServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PipelineServiceSettings pipelineServiceSettings =
     PipelineServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 PipelineServiceClient pipelineServiceClient =
     PipelineServiceClient.create(pipelineServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PipelineServiceSettings pipelineServiceSettings =
     PipelineServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 PipelineServiceClient pipelineServiceClient =
     PipelineServiceClient.create(pipelineServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

PipelineServiceClient.ListLocationsFixedSizeCollection

PipelineServiceClient.ListLocationsPage

PipelineServiceClient.ListLocationsPagedResponse

PipelineServiceClient.ListPipelineJobsFixedSizeCollection

PipelineServiceClient.ListPipelineJobsPage

PipelineServiceClient.ListPipelineJobsPagedResponse

PipelineServiceClient.ListTrainingPipelinesFixedSizeCollection

PipelineServiceClient.ListTrainingPipelinesPage

PipelineServiceClient.ListTrainingPipelinesPagedResponse

PipelineServiceGrpc

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineServiceGrpc.PipelineServiceBlockingStub

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineServiceGrpc.PipelineServiceFutureStub

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineServiceGrpc.PipelineServiceImplBase

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineServiceGrpc.PipelineServiceStub

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineServiceProto

PipelineServiceSettings

Settings class to configure an instance of PipelineServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of createTrainingPipeline to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PipelineServiceSettings.Builder pipelineServiceSettingsBuilder =
     PipelineServiceSettings.newBuilder();
 pipelineServiceSettingsBuilder
     .createTrainingPipelineSettings()
     .setRetrySettings(
         pipelineServiceSettingsBuilder
             .createTrainingPipelineSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 PipelineServiceSettings pipelineServiceSettings = pipelineServiceSettingsBuilder.build();
 

PipelineServiceSettings.Builder

Builder for PipelineServiceSettings.

PipelineStateProto

PipelineTaskDetail

The runtime detail of a task execution.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskDetail

PipelineTaskDetail.ArtifactList

A list of artifact metadata.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskDetail.ArtifactList

PipelineTaskDetail.ArtifactList.Builder

A list of artifact metadata.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskDetail.ArtifactList

PipelineTaskDetail.Builder

The runtime detail of a task execution.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskDetail

PipelineTaskDetail.PipelineTaskStatus

A single record of the task status.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskDetail.PipelineTaskStatus

PipelineTaskDetail.PipelineTaskStatus.Builder

A single record of the task status.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskDetail.PipelineTaskStatus

PipelineTaskExecutorDetail

The runtime detail of a pipeline executor.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskExecutorDetail

PipelineTaskExecutorDetail.Builder

The runtime detail of a pipeline executor.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskExecutorDetail

PipelineTaskExecutorDetail.ContainerDetail

The detail of a container execution. It contains the job names of the lifecycle of a container execution.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskExecutorDetail.ContainerDetail

PipelineTaskExecutorDetail.ContainerDetail.Builder

The detail of a container execution. It contains the job names of the lifecycle of a container execution.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskExecutorDetail.ContainerDetail

PipelineTaskExecutorDetail.CustomJobDetail

The detailed info for a custom job executor.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskExecutorDetail.CustomJobDetail

PipelineTaskExecutorDetail.CustomJobDetail.Builder

The detailed info for a custom job executor.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTaskExecutorDetail.CustomJobDetail

PipelineTemplateMetadata

Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTemplateMetadata

PipelineTemplateMetadata.Builder

Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.

Protobuf type google.cloud.aiplatform.v1beta1.PipelineTemplateMetadata

Port

Represents a network port in a container.

Protobuf type google.cloud.aiplatform.v1beta1.Port

Port.Builder

Represents a network port in a container.

Protobuf type google.cloud.aiplatform.v1beta1.Port

PredefinedSplit

Assigns input data to training, validation, and test sets based on the value of a provided key. Supported only for tabular Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.PredefinedSplit

PredefinedSplit.Builder

Assigns input data to training, validation, and test sets based on the value of a provided key. Supported only for tabular Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.PredefinedSplit

PredictRequest

Request message for PredictionService.Predict.

Protobuf type google.cloud.aiplatform.v1beta1.PredictRequest

PredictRequest.Builder

Request message for PredictionService.Predict.

Protobuf type google.cloud.aiplatform.v1beta1.PredictRequest

PredictRequestResponseLoggingConfig

Configuration for logging request-response to a BigQuery table.

Protobuf type google.cloud.aiplatform.v1beta1.PredictRequestResponseLoggingConfig

PredictRequestResponseLoggingConfig.Builder

Configuration for logging request-response to a BigQuery table.

Protobuf type google.cloud.aiplatform.v1beta1.PredictRequestResponseLoggingConfig

PredictResponse

Response message for PredictionService.Predict.

Protobuf type google.cloud.aiplatform.v1beta1.PredictResponse

PredictResponse.Builder

Response message for PredictionService.Predict.

Protobuf type google.cloud.aiplatform.v1beta1.PredictResponse

PredictSchemata

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.PredictSchemata

PredictSchemata.Builder

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.

Protobuf type google.cloud.aiplatform.v1beta1.PredictSchemata

PredictionServiceClient

Service Description: A service for online predictions and explanations.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().setBoolValue(true).build();
   PredictResponse response = predictionServiceClient.predict(endpoint, instances, parameters);
 }
 

Note: close() needs to be called on the PredictionServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of PredictionServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

PredictionServiceClient.ListLocationsFixedSizeCollection

PredictionServiceClient.ListLocationsPage

PredictionServiceClient.ListLocationsPagedResponse

PredictionServiceGrpc

A service for online predictions and explanations.

PredictionServiceGrpc.PredictionServiceBlockingStub

A service for online predictions and explanations.

PredictionServiceGrpc.PredictionServiceFutureStub

A service for online predictions and explanations.

PredictionServiceGrpc.PredictionServiceImplBase

A service for online predictions and explanations.

PredictionServiceGrpc.PredictionServiceStub

A service for online predictions and explanations.

PredictionServiceProto

PredictionServiceSettings

Settings class to configure an instance of PredictionServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of predict to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PredictionServiceSettings.Builder predictionServiceSettingsBuilder =
     PredictionServiceSettings.newBuilder();
 predictionServiceSettingsBuilder
     .predictSettings()
     .setRetrySettings(
         predictionServiceSettingsBuilder.predictSettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 PredictionServiceSettings predictionServiceSettings = predictionServiceSettingsBuilder.build();
 

PredictionServiceSettings.Builder

Builder for PredictionServiceSettings.

Presets

Preset configuration for example-based explanations

Protobuf type google.cloud.aiplatform.v1beta1.Presets

Presets.Builder

Preset configuration for example-based explanations

Protobuf type google.cloud.aiplatform.v1beta1.Presets

PrivateEndpoints

PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predict_http_uri, explain_http_uri or health_http_uri. To send request via private service connect, use service_attachment.

Protobuf type google.cloud.aiplatform.v1beta1.PrivateEndpoints

PrivateEndpoints.Builder

PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predict_http_uri, explain_http_uri or health_http_uri. To send request via private service connect, use service_attachment.

Protobuf type google.cloud.aiplatform.v1beta1.PrivateEndpoints

ProjectName

ProjectName.Builder

Builder for projects/{project}.

PurgeArtifactsMetadata

Details of operations that perform MetadataService.PurgeArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeArtifactsMetadata

PurgeArtifactsMetadata.Builder

Details of operations that perform MetadataService.PurgeArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeArtifactsMetadata

PurgeArtifactsRequest

Request message for MetadataService.PurgeArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeArtifactsRequest

PurgeArtifactsRequest.Builder

Request message for MetadataService.PurgeArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeArtifactsRequest

PurgeArtifactsResponse

Response message for MetadataService.PurgeArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeArtifactsResponse

PurgeArtifactsResponse.Builder

Response message for MetadataService.PurgeArtifacts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeArtifactsResponse

PurgeContextsMetadata

Details of operations that perform MetadataService.PurgeContexts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeContextsMetadata

PurgeContextsMetadata.Builder

Details of operations that perform MetadataService.PurgeContexts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeContextsMetadata

PurgeContextsRequest

Request message for MetadataService.PurgeContexts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeContextsRequest

PurgeContextsRequest.Builder

Request message for MetadataService.PurgeContexts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeContextsRequest

PurgeContextsResponse

Response message for MetadataService.PurgeContexts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeContextsResponse

PurgeContextsResponse.Builder

Response message for MetadataService.PurgeContexts.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeContextsResponse

PurgeExecutionsMetadata

Details of operations that perform MetadataService.PurgeExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeExecutionsMetadata

PurgeExecutionsMetadata.Builder

Details of operations that perform MetadataService.PurgeExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeExecutionsMetadata

PurgeExecutionsRequest

Request message for MetadataService.PurgeExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeExecutionsRequest

PurgeExecutionsRequest.Builder

Request message for MetadataService.PurgeExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeExecutionsRequest

PurgeExecutionsResponse

Response message for MetadataService.PurgeExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeExecutionsResponse

PurgeExecutionsResponse.Builder

Response message for MetadataService.PurgeExecutions.

Protobuf type google.cloud.aiplatform.v1beta1.PurgeExecutionsResponse

PythonPackageSpec

The spec of a Python packaged code.

Protobuf type google.cloud.aiplatform.v1beta1.PythonPackageSpec

PythonPackageSpec.Builder

The spec of a Python packaged code.

Protobuf type google.cloud.aiplatform.v1beta1.PythonPackageSpec

QueryArtifactLineageSubgraphRequest

Request message for MetadataService.QueryArtifactLineageSubgraph.

Protobuf type google.cloud.aiplatform.v1beta1.QueryArtifactLineageSubgraphRequest

QueryArtifactLineageSubgraphRequest.Builder

Request message for MetadataService.QueryArtifactLineageSubgraph.

Protobuf type google.cloud.aiplatform.v1beta1.QueryArtifactLineageSubgraphRequest

QueryContextLineageSubgraphRequest

Request message for MetadataService.QueryContextLineageSubgraph.

Protobuf type google.cloud.aiplatform.v1beta1.QueryContextLineageSubgraphRequest

QueryContextLineageSubgraphRequest.Builder

Request message for MetadataService.QueryContextLineageSubgraph.

Protobuf type google.cloud.aiplatform.v1beta1.QueryContextLineageSubgraphRequest

QueryDeployedModelsRequest

Request message for QueryDeployedModels method.

Protobuf type google.cloud.aiplatform.v1beta1.QueryDeployedModelsRequest

QueryDeployedModelsRequest.Builder

Request message for QueryDeployedModels method.

Protobuf type google.cloud.aiplatform.v1beta1.QueryDeployedModelsRequest

QueryDeployedModelsResponse

Response message for QueryDeployedModels method.

Protobuf type google.cloud.aiplatform.v1beta1.QueryDeployedModelsResponse

QueryDeployedModelsResponse.Builder

Response message for QueryDeployedModels method.

Protobuf type google.cloud.aiplatform.v1beta1.QueryDeployedModelsResponse

QueryExecutionInputsAndOutputsRequest

Request message for MetadataService.QueryExecutionInputsAndOutputs.

Protobuf type google.cloud.aiplatform.v1beta1.QueryExecutionInputsAndOutputsRequest

QueryExecutionInputsAndOutputsRequest.Builder

Request message for MetadataService.QueryExecutionInputsAndOutputs.

Protobuf type google.cloud.aiplatform.v1beta1.QueryExecutionInputsAndOutputsRequest

RawPredictRequest

Request message for PredictionService.RawPredict.

Protobuf type google.cloud.aiplatform.v1beta1.RawPredictRequest

RawPredictRequest.Builder

Request message for PredictionService.RawPredict.

Protobuf type google.cloud.aiplatform.v1beta1.RawPredictRequest

ReadFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.ReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesRequest

ReadFeatureValuesRequest.Builder

Request message for FeaturestoreOnlineServingService.ReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesRequest

ReadFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.ReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse

ReadFeatureValuesResponse.Builder

Response message for FeaturestoreOnlineServingService.ReadFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse

ReadFeatureValuesResponse.EntityView

Entity view with Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.EntityView

ReadFeatureValuesResponse.EntityView.Builder

Entity view with Feature values.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.EntityView

ReadFeatureValuesResponse.EntityView.Data

Container to hold value(s), successive in time, for one Feature from the request.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.EntityView.Data

ReadFeatureValuesResponse.EntityView.Data.Builder

Container to hold value(s), successive in time, for one Feature from the request.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.EntityView.Data

ReadFeatureValuesResponse.FeatureDescriptor

Metadata for requested Features.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.FeatureDescriptor

ReadFeatureValuesResponse.FeatureDescriptor.Builder

Metadata for requested Features.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.FeatureDescriptor

ReadFeatureValuesResponse.Header

Response header with metadata for the requested ReadFeatureValuesRequest.entity_type and Features.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.Header

ReadFeatureValuesResponse.Header.Builder

Response header with metadata for the requested ReadFeatureValuesRequest.entity_type and Features.

Protobuf type google.cloud.aiplatform.v1beta1.ReadFeatureValuesResponse.Header

ReadTensorboardBlobDataRequest

Request message for TensorboardService.ReadTensorboardBlobData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardBlobDataRequest

ReadTensorboardBlobDataRequest.Builder

Request message for TensorboardService.ReadTensorboardBlobData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardBlobDataRequest

ReadTensorboardBlobDataResponse

Response message for TensorboardService.ReadTensorboardBlobData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardBlobDataResponse

ReadTensorboardBlobDataResponse.Builder

Response message for TensorboardService.ReadTensorboardBlobData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardBlobDataResponse

ReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardTimeSeriesDataRequest

ReadTensorboardTimeSeriesDataRequest.Builder

Request message for TensorboardService.ReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardTimeSeriesDataRequest

ReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardTimeSeriesDataResponse

ReadTensorboardTimeSeriesDataResponse.Builder

Response message for TensorboardService.ReadTensorboardTimeSeriesData.

Protobuf type google.cloud.aiplatform.v1beta1.ReadTensorboardTimeSeriesDataResponse

RemoveContextChildrenRequest

Request message for [MetadataService.DeleteContextChildrenRequest][].

Protobuf type google.cloud.aiplatform.v1beta1.RemoveContextChildrenRequest

RemoveContextChildrenRequest.Builder

Request message for [MetadataService.DeleteContextChildrenRequest][].

Protobuf type google.cloud.aiplatform.v1beta1.RemoveContextChildrenRequest

RemoveContextChildrenResponse

Response message for MetadataService.RemoveContextChildren.

Protobuf type google.cloud.aiplatform.v1beta1.RemoveContextChildrenResponse

RemoveContextChildrenResponse.Builder

Response message for MetadataService.RemoveContextChildren.

Protobuf type google.cloud.aiplatform.v1beta1.RemoveContextChildrenResponse

RemoveDatapointsRequest

Request message for IndexService.RemoveDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.RemoveDatapointsRequest

RemoveDatapointsRequest.Builder

Request message for IndexService.RemoveDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.RemoveDatapointsRequest

RemoveDatapointsResponse

Response message for IndexService.RemoveDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.RemoveDatapointsResponse

RemoveDatapointsResponse.Builder

Response message for IndexService.RemoveDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.RemoveDatapointsResponse

ResourcesConsumed

Statistics information about resource consumption.

Protobuf type google.cloud.aiplatform.v1beta1.ResourcesConsumed

ResourcesConsumed.Builder

Statistics information about resource consumption.

Protobuf type google.cloud.aiplatform.v1beta1.ResourcesConsumed

ResumeModelDeploymentMonitoringJobRequest

Request message for JobService.ResumeModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.ResumeModelDeploymentMonitoringJobRequest

ResumeModelDeploymentMonitoringJobRequest.Builder

Request message for JobService.ResumeModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.ResumeModelDeploymentMonitoringJobRequest

SampleConfig

Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.

Protobuf type google.cloud.aiplatform.v1beta1.SampleConfig

SampleConfig.Builder

Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.

Protobuf type google.cloud.aiplatform.v1beta1.SampleConfig

SampledShapleyAttribution

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.

Protobuf type google.cloud.aiplatform.v1beta1.SampledShapleyAttribution

SampledShapleyAttribution.Builder

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.

Protobuf type google.cloud.aiplatform.v1beta1.SampledShapleyAttribution

SamplingStrategy

Sampling Strategy for logging, can be for both training and prediction dataset.

Protobuf type google.cloud.aiplatform.v1beta1.SamplingStrategy

SamplingStrategy.Builder

Sampling Strategy for logging, can be for both training and prediction dataset.

Protobuf type google.cloud.aiplatform.v1beta1.SamplingStrategy

SamplingStrategy.RandomSampleConfig

Requests are randomly selected.

Protobuf type google.cloud.aiplatform.v1beta1.SamplingStrategy.RandomSampleConfig

SamplingStrategy.RandomSampleConfig.Builder

Requests are randomly selected.

Protobuf type google.cloud.aiplatform.v1beta1.SamplingStrategy.RandomSampleConfig

SavedQuery

A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.

Protobuf type google.cloud.aiplatform.v1beta1.SavedQuery

SavedQuery.Builder

A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.

Protobuf type google.cloud.aiplatform.v1beta1.SavedQuery

SavedQueryName

SavedQueryName.Builder

Builder for projects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{saved_query}.

SavedQueryProto

Scalar

One point viewable on a scalar metric plot.

Protobuf type google.cloud.aiplatform.v1beta1.Scalar

Scalar.Builder

One point viewable on a scalar metric plot.

Protobuf type google.cloud.aiplatform.v1beta1.Scalar

Scheduling

All parameters related to queuing and scheduling of custom jobs.

Protobuf type google.cloud.aiplatform.v1beta1.Scheduling

Scheduling.Builder

All parameters related to queuing and scheduling of custom jobs.

Protobuf type google.cloud.aiplatform.v1beta1.Scheduling

SearchDataItemsRequest

Request message for DatasetService.SearchDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.SearchDataItemsRequest

SearchDataItemsRequest.Builder

Request message for DatasetService.SearchDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.SearchDataItemsRequest

SearchDataItemsRequest.OrderByAnnotation

Expression that allows ranking results based on annotation's property.

Protobuf type google.cloud.aiplatform.v1beta1.SearchDataItemsRequest.OrderByAnnotation

SearchDataItemsRequest.OrderByAnnotation.Builder

Expression that allows ranking results based on annotation's property.

Protobuf type google.cloud.aiplatform.v1beta1.SearchDataItemsRequest.OrderByAnnotation

SearchDataItemsResponse

Response message for DatasetService.SearchDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.SearchDataItemsResponse

SearchDataItemsResponse.Builder

Response message for DatasetService.SearchDataItems.

Protobuf type google.cloud.aiplatform.v1beta1.SearchDataItemsResponse

SearchFeaturesRequest

Request message for FeaturestoreService.SearchFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.SearchFeaturesRequest

SearchFeaturesRequest.Builder

Request message for FeaturestoreService.SearchFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.SearchFeaturesRequest

SearchFeaturesResponse

Response message for FeaturestoreService.SearchFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.SearchFeaturesResponse

SearchFeaturesResponse.Builder

Response message for FeaturestoreService.SearchFeatures.

Protobuf type google.cloud.aiplatform.v1beta1.SearchFeaturesResponse

SearchMigratableResourcesRequest

Request message for MigrationService.SearchMigratableResources.

Protobuf type google.cloud.aiplatform.v1beta1.SearchMigratableResourcesRequest

SearchMigratableResourcesRequest.Builder

Request message for MigrationService.SearchMigratableResources.

Protobuf type google.cloud.aiplatform.v1beta1.SearchMigratableResourcesRequest

SearchMigratableResourcesResponse

Response message for MigrationService.SearchMigratableResources.

Protobuf type google.cloud.aiplatform.v1beta1.SearchMigratableResourcesResponse

SearchMigratableResourcesResponse.Builder

Response message for MigrationService.SearchMigratableResources.

Protobuf type google.cloud.aiplatform.v1beta1.SearchMigratableResourcesResponse

SearchModelDeploymentMonitoringStatsAnomaliesRequest

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Protobuf type google.cloud.aiplatform.v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesRequest

SearchModelDeploymentMonitoringStatsAnomaliesRequest.Builder

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Protobuf type google.cloud.aiplatform.v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesRequest

SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjective

Stats requested for specific objective.

Protobuf type google.cloud.aiplatform.v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjective

SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjective.Builder

Stats requested for specific objective.

Protobuf type google.cloud.aiplatform.v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjective

SearchModelDeploymentMonitoringStatsAnomaliesResponse

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Protobuf type google.cloud.aiplatform.v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesResponse

SearchModelDeploymentMonitoringStatsAnomaliesResponse.Builder

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Protobuf type google.cloud.aiplatform.v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesResponse

SmoothGradConfig

Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

Protobuf type google.cloud.aiplatform.v1beta1.SmoothGradConfig

SmoothGradConfig.Builder

Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

Protobuf type google.cloud.aiplatform.v1beta1.SmoothGradConfig

SpecialistPool

SpecialistPool represents customers' own workforce to work on their data labeling jobs. It includes a group of specialist managers and workers. Managers are responsible for managing the workers in this pool as well as customers' data labeling jobs associated with this pool. Customers create specialist pool as well as start data labeling jobs on Cloud, managers and workers handle the jobs using CrowdCompute console.

Protobuf type google.cloud.aiplatform.v1beta1.SpecialistPool

SpecialistPool.Builder

SpecialistPool represents customers' own workforce to work on their data labeling jobs. It includes a group of specialist managers and workers. Managers are responsible for managing the workers in this pool as well as customers' data labeling jobs associated with this pool. Customers create specialist pool as well as start data labeling jobs on Cloud, managers and workers handle the jobs using CrowdCompute console.

Protobuf type google.cloud.aiplatform.v1beta1.SpecialistPool

SpecialistPoolName

SpecialistPoolName.Builder

Builder for projects/{project}/locations/{location}/specialistPools/{specialist_pool}.

SpecialistPoolProto

SpecialistPoolServiceClient

Service Description: A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (SpecialistPoolServiceClient specialistPoolServiceClient =
     SpecialistPoolServiceClient.create()) {
   SpecialistPoolName name =
       SpecialistPoolName.of("[PROJECT]", "[LOCATION]", "[SPECIALIST_POOL]");
   SpecialistPool response = specialistPoolServiceClient.getSpecialistPool(name);
 }
 

Note: close() needs to be called on the SpecialistPoolServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of SpecialistPoolServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 SpecialistPoolServiceSettings specialistPoolServiceSettings =
     SpecialistPoolServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 SpecialistPoolServiceClient specialistPoolServiceClient =
     SpecialistPoolServiceClient.create(specialistPoolServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 SpecialistPoolServiceSettings specialistPoolServiceSettings =
     SpecialistPoolServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 SpecialistPoolServiceClient specialistPoolServiceClient =
     SpecialistPoolServiceClient.create(specialistPoolServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

SpecialistPoolServiceClient.ListLocationsFixedSizeCollection

SpecialistPoolServiceClient.ListLocationsPage

SpecialistPoolServiceClient.ListLocationsPagedResponse

SpecialistPoolServiceClient.ListSpecialistPoolsFixedSizeCollection

SpecialistPoolServiceClient.ListSpecialistPoolsPage

SpecialistPoolServiceClient.ListSpecialistPoolsPagedResponse

SpecialistPoolServiceGrpc

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolServiceGrpc.SpecialistPoolServiceBlockingStub

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolServiceGrpc.SpecialistPoolServiceFutureStub

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolServiceGrpc.SpecialistPoolServiceImplBase

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolServiceGrpc.SpecialistPoolServiceStub

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolServiceProto

SpecialistPoolServiceSettings

Settings class to configure an instance of SpecialistPoolServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getSpecialistPool to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 SpecialistPoolServiceSettings.Builder specialistPoolServiceSettingsBuilder =
     SpecialistPoolServiceSettings.newBuilder();
 specialistPoolServiceSettingsBuilder
     .getSpecialistPoolSettings()
     .setRetrySettings(
         specialistPoolServiceSettingsBuilder
             .getSpecialistPoolSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 SpecialistPoolServiceSettings specialistPoolServiceSettings =
     specialistPoolServiceSettingsBuilder.build();
 

SpecialistPoolServiceSettings.Builder

Builder for SpecialistPoolServiceSettings.

StopTrialRequest

Request message for VizierService.StopTrial.

Protobuf type google.cloud.aiplatform.v1beta1.StopTrialRequest

StopTrialRequest.Builder

Request message for VizierService.StopTrial.

Protobuf type google.cloud.aiplatform.v1beta1.StopTrialRequest

StratifiedSplit

Assigns input data to the training, validation, and test sets so that the distribution of values found in the categorical column (as specified by the key field) is mirrored within each split. The fraction values determine the relative sizes of the splits. For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C". Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned. Supported only for tabular Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.StratifiedSplit

StratifiedSplit.Builder

Assigns input data to the training, validation, and test sets so that the distribution of values found in the categorical column (as specified by the key field) is mirrored within each split. The fraction values determine the relative sizes of the splits. For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C". Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned. Supported only for tabular Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.StratifiedSplit

StreamingReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

Protobuf type google.cloud.aiplatform.v1beta1.StreamingReadFeatureValuesRequest

StreamingReadFeatureValuesRequest.Builder

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

Protobuf type google.cloud.aiplatform.v1beta1.StreamingReadFeatureValuesRequest

StringArray

A list of string values.

Protobuf type google.cloud.aiplatform.v1beta1.StringArray

StringArray.Builder

A list of string values.

Protobuf type google.cloud.aiplatform.v1beta1.StringArray

Study

A message representing a Study.

Protobuf type google.cloud.aiplatform.v1beta1.Study

Study.Builder

A message representing a Study.

Protobuf type google.cloud.aiplatform.v1beta1.Study

StudyName

StudyName.Builder

Builder for projects/{project}/locations/{location}/studies/{study}.

StudyProto

StudySpec

Represents specification of a Study.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec

StudySpec.Builder

Represents specification of a Study.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec

StudySpec.ConvexAutomatedStoppingSpec

Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ConvexAutomatedStoppingSpec

StudySpec.ConvexAutomatedStoppingSpec.Builder

Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ConvexAutomatedStoppingSpec

StudySpec.ConvexStopConfig

Configuration for ConvexStopPolicy.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ConvexStopConfig

StudySpec.ConvexStopConfig.Builder

Configuration for ConvexStopPolicy.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ConvexStopConfig

StudySpec.DecayCurveAutomatedStoppingSpec

The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.DecayCurveAutomatedStoppingSpec

StudySpec.DecayCurveAutomatedStoppingSpec.Builder

The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.DecayCurveAutomatedStoppingSpec

StudySpec.MedianAutomatedStoppingSpec

The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.MedianAutomatedStoppingSpec

StudySpec.MedianAutomatedStoppingSpec.Builder

The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.MedianAutomatedStoppingSpec

StudySpec.MetricSpec

Represents a metric to optimize.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.MetricSpec

StudySpec.MetricSpec.Builder

Represents a metric to optimize.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.MetricSpec

StudySpec.ParameterSpec

Represents a single parameter to optimize.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec

StudySpec.ParameterSpec.Builder

Represents a single parameter to optimize.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec

StudySpec.ParameterSpec.CategoricalValueSpec

Value specification for a parameter in CATEGORICAL type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.CategoricalValueSpec

StudySpec.ParameterSpec.CategoricalValueSpec.Builder

Value specification for a parameter in CATEGORICAL type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.CategoricalValueSpec

StudySpec.ParameterSpec.ConditionalParameterSpec

Represents a parameter spec with condition from its parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec

StudySpec.ParameterSpec.ConditionalParameterSpec.Builder

Represents a parameter spec with condition from its parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec

StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition

Represents the spec to match categorical values from parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition

StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition.Builder

Represents the spec to match categorical values from parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition

StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition

Represents the spec to match discrete values from parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition

StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition.Builder

Represents the spec to match discrete values from parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition

StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition

Represents the spec to match integer values from parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition

StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition.Builder

Represents the spec to match integer values from parent parameter.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition

StudySpec.ParameterSpec.DiscreteValueSpec

Value specification for a parameter in DISCRETE type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.DiscreteValueSpec

StudySpec.ParameterSpec.DiscreteValueSpec.Builder

Value specification for a parameter in DISCRETE type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.DiscreteValueSpec

StudySpec.ParameterSpec.DoubleValueSpec

Value specification for a parameter in DOUBLE type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.DoubleValueSpec

StudySpec.ParameterSpec.DoubleValueSpec.Builder

Value specification for a parameter in DOUBLE type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.DoubleValueSpec

StudySpec.ParameterSpec.IntegerValueSpec

Value specification for a parameter in INTEGER type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.IntegerValueSpec

StudySpec.ParameterSpec.IntegerValueSpec.Builder

Value specification for a parameter in INTEGER type.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.IntegerValueSpec

StudySpec.TransferLearningConfig

This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.TransferLearningConfig

StudySpec.TransferLearningConfig.Builder

This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.TransferLearningConfig

SuggestTrialsMetadata

Details of operations that perform Trials suggestion.

Protobuf type google.cloud.aiplatform.v1beta1.SuggestTrialsMetadata

SuggestTrialsMetadata.Builder

Details of operations that perform Trials suggestion.

Protobuf type google.cloud.aiplatform.v1beta1.SuggestTrialsMetadata

SuggestTrialsRequest

Request message for VizierService.SuggestTrials.

Protobuf type google.cloud.aiplatform.v1beta1.SuggestTrialsRequest

SuggestTrialsRequest.Builder

Request message for VizierService.SuggestTrials.

Protobuf type google.cloud.aiplatform.v1beta1.SuggestTrialsRequest

SuggestTrialsResponse

Response message for VizierService.SuggestTrials.

Protobuf type google.cloud.aiplatform.v1beta1.SuggestTrialsResponse

SuggestTrialsResponse.Builder

Response message for VizierService.SuggestTrials.

Protobuf type google.cloud.aiplatform.v1beta1.SuggestTrialsResponse

TFRecordDestination

The storage details for TFRecord output content.

Protobuf type google.cloud.aiplatform.v1beta1.TFRecordDestination

TFRecordDestination.Builder

The storage details for TFRecord output content.

Protobuf type google.cloud.aiplatform.v1beta1.TFRecordDestination

Tensorboard

Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Protobuf type google.cloud.aiplatform.v1beta1.Tensorboard

Tensorboard.Builder

Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Protobuf type google.cloud.aiplatform.v1beta1.Tensorboard

TensorboardBlob

One blob (e.g, image, graph) viewable on a blob metric plot.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardBlob

TensorboardBlob.Builder

One blob (e.g, image, graph) viewable on a blob metric plot.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardBlob

TensorboardBlobSequence

One point viewable on a blob metric plot, but mostly just a wrapper message to work around repeated fields can't be used directly within oneof fields.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardBlobSequence

TensorboardBlobSequence.Builder

One point viewable on a blob metric plot, but mostly just a wrapper message to work around repeated fields can't be used directly within oneof fields.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardBlobSequence

TensorboardDataProto

TensorboardExperiment

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardExperiment

TensorboardExperiment.Builder

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardExperiment

TensorboardExperimentName

TensorboardExperimentName.Builder

Builder for projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}.

TensorboardExperimentProto

TensorboardName

TensorboardName.Builder

Builder for projects/{project}/locations/{location}/tensorboards/{tensorboard}.

TensorboardProto

TensorboardRun

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardRun

TensorboardRun.Builder

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardRun

TensorboardRunName

TensorboardRunName.Builder

Builder for projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}.

TensorboardRunProto

TensorboardServiceClient

Service Description: TensorboardService

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (TensorboardServiceClient tensorboardServiceClient = TensorboardServiceClient.create()) {
   TensorboardName name = TensorboardName.of("[PROJECT]", "[LOCATION]", "[TENSORBOARD]");
   Tensorboard response = tensorboardServiceClient.getTensorboard(name);
 }
 

Note: close() needs to be called on the TensorboardServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of TensorboardServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 TensorboardServiceSettings tensorboardServiceSettings =
     TensorboardServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 TensorboardServiceClient tensorboardServiceClient =
     TensorboardServiceClient.create(tensorboardServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 TensorboardServiceSettings tensorboardServiceSettings =
     TensorboardServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 TensorboardServiceClient tensorboardServiceClient =
     TensorboardServiceClient.create(tensorboardServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

TensorboardServiceClient.ExportTensorboardTimeSeriesDataFixedSizeCollection

TensorboardServiceClient.ExportTensorboardTimeSeriesDataPage

TensorboardServiceClient.ExportTensorboardTimeSeriesDataPagedResponse

TensorboardServiceClient.ListLocationsFixedSizeCollection

TensorboardServiceClient.ListLocationsPage

TensorboardServiceClient.ListLocationsPagedResponse

TensorboardServiceClient.ListTensorboardExperimentsFixedSizeCollection

TensorboardServiceClient.ListTensorboardExperimentsPage

TensorboardServiceClient.ListTensorboardExperimentsPagedResponse

TensorboardServiceClient.ListTensorboardRunsFixedSizeCollection

TensorboardServiceClient.ListTensorboardRunsPage

TensorboardServiceClient.ListTensorboardRunsPagedResponse

TensorboardServiceClient.ListTensorboardTimeSeriesFixedSizeCollection

TensorboardServiceClient.ListTensorboardTimeSeriesPage

TensorboardServiceClient.ListTensorboardTimeSeriesPagedResponse

TensorboardServiceClient.ListTensorboardsFixedSizeCollection

TensorboardServiceClient.ListTensorboardsPage

TensorboardServiceClient.ListTensorboardsPagedResponse

TensorboardServiceGrpc

TensorboardService

TensorboardServiceGrpc.TensorboardServiceBlockingStub

TensorboardService

TensorboardServiceGrpc.TensorboardServiceFutureStub

TensorboardService

TensorboardServiceGrpc.TensorboardServiceImplBase

TensorboardService

TensorboardServiceGrpc.TensorboardServiceStub

TensorboardService

TensorboardServiceProto

TensorboardServiceSettings

Settings class to configure an instance of TensorboardServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getTensorboard to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 TensorboardServiceSettings.Builder tensorboardServiceSettingsBuilder =
     TensorboardServiceSettings.newBuilder();
 tensorboardServiceSettingsBuilder
     .getTensorboardSettings()
     .setRetrySettings(
         tensorboardServiceSettingsBuilder
             .getTensorboardSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 TensorboardServiceSettings tensorboardServiceSettings =
     tensorboardServiceSettingsBuilder.build();
 

TensorboardServiceSettings.Builder

Builder for TensorboardServiceSettings.

TensorboardTensor

One point viewable on a tensor metric plot.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardTensor

TensorboardTensor.Builder

One point viewable on a tensor metric plot.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardTensor

TensorboardTimeSeries

TensorboardTimeSeries maps to times series produced in training runs

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardTimeSeries

TensorboardTimeSeries.Builder

TensorboardTimeSeries maps to times series produced in training runs

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardTimeSeries

TensorboardTimeSeries.Metadata

Describes metadata for a TensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardTimeSeries.Metadata

TensorboardTimeSeries.Metadata.Builder

Describes metadata for a TensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.TensorboardTimeSeries.Metadata

TensorboardTimeSeriesName

TensorboardTimeSeriesName.Builder

Builder for projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}.

TensorboardTimeSeriesProto

ThresholdConfig

The config for feature monitoring threshold.

Protobuf type google.cloud.aiplatform.v1beta1.ThresholdConfig

ThresholdConfig.Builder

The config for feature monitoring threshold.

Protobuf type google.cloud.aiplatform.v1beta1.ThresholdConfig

TimeSeriesData

All the data stored in a TensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.TimeSeriesData

TimeSeriesData.Builder

All the data stored in a TensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.TimeSeriesData

TimeSeriesDataPoint

A TensorboardTimeSeries data point.

Protobuf type google.cloud.aiplatform.v1beta1.TimeSeriesDataPoint

TimeSeriesDataPoint.Builder

A TensorboardTimeSeries data point.

Protobuf type google.cloud.aiplatform.v1beta1.TimeSeriesDataPoint

TimestampSplit

Assigns input data to training, validation, and test sets based on a provided timestamps. The youngest data pieces are assigned to training set, next to validation set, and the oldest to the test set. Supported only for tabular Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.TimestampSplit

TimestampSplit.Builder

Assigns input data to training, validation, and test sets based on a provided timestamps. The youngest data pieces are assigned to training set, next to validation set, and the oldest to the test set. Supported only for tabular Datasets.

Protobuf type google.cloud.aiplatform.v1beta1.TimestampSplit

TrainingConfig

CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

Protobuf type google.cloud.aiplatform.v1beta1.TrainingConfig

TrainingConfig.Builder

CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

Protobuf type google.cloud.aiplatform.v1beta1.TrainingConfig

TrainingPipeline

The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

Protobuf type google.cloud.aiplatform.v1beta1.TrainingPipeline

TrainingPipeline.Builder

The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

Protobuf type google.cloud.aiplatform.v1beta1.TrainingPipeline

TrainingPipelineName

TrainingPipelineName.Builder

Builder for projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}.

TrainingPipelineProto

Trial

A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.

Protobuf type google.cloud.aiplatform.v1beta1.Trial

Trial.Builder

A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.

Protobuf type google.cloud.aiplatform.v1beta1.Trial

Trial.Parameter

A message representing a parameter to be tuned.

Protobuf type google.cloud.aiplatform.v1beta1.Trial.Parameter

Trial.Parameter.Builder

A message representing a parameter to be tuned.

Protobuf type google.cloud.aiplatform.v1beta1.Trial.Parameter

TrialName

TrialName.Builder

Builder for projects/{project}/locations/{location}/studies/{study}/trials/{trial}.

TypesProto

UndeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.UndeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployIndexOperationMetadata

UndeployIndexOperationMetadata.Builder

Runtime operation information for IndexEndpointService.UndeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployIndexOperationMetadata

UndeployIndexRequest

Request message for IndexEndpointService.UndeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployIndexRequest

UndeployIndexRequest.Builder

Request message for IndexEndpointService.UndeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployIndexRequest

UndeployIndexResponse

Response message for IndexEndpointService.UndeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployIndexResponse

UndeployIndexResponse.Builder

Response message for IndexEndpointService.UndeployIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployIndexResponse

UndeployModelOperationMetadata

Runtime operation information for EndpointService.UndeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployModelOperationMetadata

UndeployModelOperationMetadata.Builder

Runtime operation information for EndpointService.UndeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployModelOperationMetadata

UndeployModelRequest

Request message for EndpointService.UndeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployModelRequest

UndeployModelRequest.Builder

Request message for EndpointService.UndeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployModelRequest

UndeployModelResponse

Response message for EndpointService.UndeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployModelResponse

UndeployModelResponse.Builder

Response message for EndpointService.UndeployModel.

Protobuf type google.cloud.aiplatform.v1beta1.UndeployModelResponse

UnmanagedContainerModel

Contains model information necessary to perform batch prediction without requiring a full model import.

Protobuf type google.cloud.aiplatform.v1beta1.UnmanagedContainerModel

UnmanagedContainerModel.Builder

Contains model information necessary to perform batch prediction without requiring a full model import.

Protobuf type google.cloud.aiplatform.v1beta1.UnmanagedContainerModel

UnmanagedContainerModelProto

UpdateArtifactRequest

Request message for MetadataService.UpdateArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateArtifactRequest

UpdateArtifactRequest.Builder

Request message for MetadataService.UpdateArtifact.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateArtifactRequest

UpdateContextRequest

Request message for MetadataService.UpdateContext.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateContextRequest

UpdateContextRequest.Builder

Request message for MetadataService.UpdateContext.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateContextRequest

UpdateDatasetRequest

Request message for DatasetService.UpdateDataset.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateDatasetRequest

UpdateDatasetRequest.Builder

Request message for DatasetService.UpdateDataset.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateDatasetRequest

UpdateDeploymentResourcePoolOperationMetadata

Runtime operation information for UpdateDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateDeploymentResourcePoolOperationMetadata

UpdateDeploymentResourcePoolOperationMetadata.Builder

Runtime operation information for UpdateDeploymentResourcePool method.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateDeploymentResourcePoolOperationMetadata

UpdateEndpointRequest

Request message for EndpointService.UpdateEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateEndpointRequest

UpdateEndpointRequest.Builder

Request message for EndpointService.UpdateEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateEndpointRequest

UpdateEntityTypeRequest

Request message for FeaturestoreService.UpdateEntityType.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateEntityTypeRequest

UpdateEntityTypeRequest.Builder

Request message for FeaturestoreService.UpdateEntityType.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateEntityTypeRequest

UpdateExecutionRequest

Request message for MetadataService.UpdateExecution.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExecutionRequest

UpdateExecutionRequest.Builder

Request message for MetadataService.UpdateExecution.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExecutionRequest

UpdateExplanationDatasetOperationMetadata

Runtime operation information for ModelService.UpdateExplanationDataset.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExplanationDatasetOperationMetadata

UpdateExplanationDatasetOperationMetadata.Builder

Runtime operation information for ModelService.UpdateExplanationDataset.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExplanationDatasetOperationMetadata

UpdateExplanationDatasetRequest

Request message for ModelService.UpdateExplanationDataset.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExplanationDatasetRequest

UpdateExplanationDatasetRequest.Builder

Request message for ModelService.UpdateExplanationDataset.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExplanationDatasetRequest

UpdateExplanationDatasetResponse

Response message of ModelService.UpdateExplanationDataset operation.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExplanationDatasetResponse

UpdateExplanationDatasetResponse.Builder

Response message of ModelService.UpdateExplanationDataset operation.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateExplanationDatasetResponse

UpdateFeatureRequest

Request message for FeaturestoreService.UpdateFeature.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateFeatureRequest

UpdateFeatureRequest.Builder

Request message for FeaturestoreService.UpdateFeature.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateFeatureRequest

UpdateFeaturestoreOperationMetadata

Details of operations that perform update Featurestore.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateFeaturestoreOperationMetadata

UpdateFeaturestoreOperationMetadata.Builder

Details of operations that perform update Featurestore.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateFeaturestoreOperationMetadata

UpdateFeaturestoreRequest

Request message for FeaturestoreService.UpdateFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateFeaturestoreRequest

UpdateFeaturestoreRequest.Builder

Request message for FeaturestoreService.UpdateFeaturestore.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateFeaturestoreRequest

UpdateIndexEndpointRequest

Request message for IndexEndpointService.UpdateIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateIndexEndpointRequest

UpdateIndexEndpointRequest.Builder

Request message for IndexEndpointService.UpdateIndexEndpoint.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateIndexEndpointRequest

UpdateIndexOperationMetadata

Runtime operation information for IndexService.UpdateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateIndexOperationMetadata

UpdateIndexOperationMetadata.Builder

Runtime operation information for IndexService.UpdateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateIndexOperationMetadata

UpdateIndexRequest

Request message for IndexService.UpdateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateIndexRequest

UpdateIndexRequest.Builder

Request message for IndexService.UpdateIndex.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateIndexRequest

UpdateModelDeploymentMonitoringJobOperationMetadata

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateModelDeploymentMonitoringJobOperationMetadata

UpdateModelDeploymentMonitoringJobOperationMetadata.Builder

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateModelDeploymentMonitoringJobOperationMetadata

UpdateModelDeploymentMonitoringJobRequest

Request message for JobService.UpdateModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateModelDeploymentMonitoringJobRequest

UpdateModelDeploymentMonitoringJobRequest.Builder

Request message for JobService.UpdateModelDeploymentMonitoringJob.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateModelDeploymentMonitoringJobRequest

UpdateModelRequest

Request message for ModelService.UpdateModel.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateModelRequest

UpdateModelRequest.Builder

Request message for ModelService.UpdateModel.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateModelRequest

UpdateSpecialistPoolOperationMetadata

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateSpecialistPoolOperationMetadata

UpdateSpecialistPoolOperationMetadata.Builder

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateSpecialistPoolOperationMetadata

UpdateSpecialistPoolRequest

Request message for SpecialistPoolService.UpdateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateSpecialistPoolRequest

UpdateSpecialistPoolRequest.Builder

Request message for SpecialistPoolService.UpdateSpecialistPool.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateSpecialistPoolRequest

UpdateTensorboardExperimentRequest

Request message for TensorboardService.UpdateTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardExperimentRequest

UpdateTensorboardExperimentRequest.Builder

Request message for TensorboardService.UpdateTensorboardExperiment.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardExperimentRequest

UpdateTensorboardOperationMetadata

Details of operations that perform update Tensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardOperationMetadata

UpdateTensorboardOperationMetadata.Builder

Details of operations that perform update Tensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardOperationMetadata

UpdateTensorboardRequest

Request message for TensorboardService.UpdateTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardRequest

UpdateTensorboardRequest.Builder

Request message for TensorboardService.UpdateTensorboard.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardRequest

UpdateTensorboardRunRequest

Request message for TensorboardService.UpdateTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardRunRequest

UpdateTensorboardRunRequest.Builder

Request message for TensorboardService.UpdateTensorboardRun.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardRunRequest

UpdateTensorboardTimeSeriesRequest

Request message for TensorboardService.UpdateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardTimeSeriesRequest

UpdateTensorboardTimeSeriesRequest.Builder

Request message for TensorboardService.UpdateTensorboardTimeSeries.

Protobuf type google.cloud.aiplatform.v1beta1.UpdateTensorboardTimeSeriesRequest

UploadModelOperationMetadata

Details of ModelService.UploadModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.UploadModelOperationMetadata

UploadModelOperationMetadata.Builder

Details of ModelService.UploadModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.UploadModelOperationMetadata

UploadModelRequest

Request message for ModelService.UploadModel.

Protobuf type google.cloud.aiplatform.v1beta1.UploadModelRequest

UploadModelRequest.Builder

Request message for ModelService.UploadModel.

Protobuf type google.cloud.aiplatform.v1beta1.UploadModelRequest

UploadModelResponse

Response message of ModelService.UploadModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.UploadModelResponse

UploadModelResponse.Builder

Response message of ModelService.UploadModel operation.

Protobuf type google.cloud.aiplatform.v1beta1.UploadModelResponse

UpsertDatapointsRequest

Request message for IndexService.UpsertDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.UpsertDatapointsRequest

UpsertDatapointsRequest.Builder

Request message for IndexService.UpsertDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.UpsertDatapointsRequest

UpsertDatapointsResponse

Response message for IndexService.UpsertDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.UpsertDatapointsResponse

UpsertDatapointsResponse.Builder

Response message for IndexService.UpsertDatapoints

Protobuf type google.cloud.aiplatform.v1beta1.UpsertDatapointsResponse

UserActionReference

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

Protobuf type google.cloud.aiplatform.v1beta1.UserActionReference

UserActionReference.Builder

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

Protobuf type google.cloud.aiplatform.v1beta1.UserActionReference

UserActionReferenceProto

Value

Value is the value of the field.

Protobuf type google.cloud.aiplatform.v1beta1.Value

Value.Builder

Value is the value of the field.

Protobuf type google.cloud.aiplatform.v1beta1.Value

ValueProto

VizierServiceClient

Service Description: Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (VizierServiceClient vizierServiceClient = VizierServiceClient.create()) {
   LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
   Study study = Study.newBuilder().build();
   Study response = vizierServiceClient.createStudy(parent, study);
 }
 

Note: close() needs to be called on the VizierServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of VizierServiceSettings to create(). For example:

To customize credentials:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 VizierServiceSettings vizierServiceSettings =
     VizierServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 VizierServiceClient vizierServiceClient = VizierServiceClient.create(vizierServiceSettings);
 

To customize the endpoint:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 VizierServiceSettings vizierServiceSettings =
     VizierServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 VizierServiceClient vizierServiceClient = VizierServiceClient.create(vizierServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

VizierServiceClient.ListLocationsFixedSizeCollection

VizierServiceClient.ListLocationsPage

VizierServiceClient.ListLocationsPagedResponse

VizierServiceClient.ListStudiesFixedSizeCollection

VizierServiceClient.ListStudiesPage

VizierServiceClient.ListStudiesPagedResponse

VizierServiceClient.ListTrialsFixedSizeCollection

VizierServiceClient.ListTrialsPage

VizierServiceClient.ListTrialsPagedResponse

VizierServiceGrpc

Vertex AI Vizier API. Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceGrpc.VizierServiceBlockingStub

Vertex AI Vizier API. Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceGrpc.VizierServiceFutureStub

Vertex AI Vizier API. Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceGrpc.VizierServiceImplBase

Vertex AI Vizier API. Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceGrpc.VizierServiceStub

Vertex AI Vizier API. Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceProto

VizierServiceSettings

Settings class to configure an instance of VizierServiceClient.

The default instance has everything set to sensible defaults:

• The default service address (aiplatform.googleapis.com) and default port (443) are used.
• Credentials are acquired automatically through Application Default Credentials.
• Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of createStudy to 30 seconds:

 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 VizierServiceSettings.Builder vizierServiceSettingsBuilder = VizierServiceSettings.newBuilder();
 vizierServiceSettingsBuilder
     .createStudySettings()
     .setRetrySettings(
         vizierServiceSettingsBuilder.createStudySettings().getRetrySettings().toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 VizierServiceSettings vizierServiceSettings = vizierServiceSettingsBuilder.build();
 

VizierServiceSettings.Builder

Builder for VizierServiceSettings.

WorkerPoolSpec

Represents the spec of a worker pool in a job.

Protobuf type google.cloud.aiplatform.v1beta1.WorkerPoolSpec

WorkerPoolSpec.Builder

Represents the spec of a worker pool in a job.

Protobuf type google.cloud.aiplatform.v1beta1.WorkerPoolSpec

WriteFeatureValuesPayload

Contains Feature values to be written for a specific entity.

Protobuf type google.cloud.aiplatform.v1beta1.WriteFeatureValuesPayload

WriteFeatureValuesPayload.Builder

Contains Feature values to be written for a specific entity.

Protobuf type google.cloud.aiplatform.v1beta1.WriteFeatureValuesPayload

WriteFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.WriteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.WriteFeatureValuesRequest

WriteFeatureValuesRequest.Builder

Request message for FeaturestoreOnlineServingService.WriteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.WriteFeatureValuesRequest

WriteFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.WriteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.WriteFeatureValuesResponse

WriteFeatureValuesResponse.Builder

Response message for FeaturestoreOnlineServingService.WriteFeatureValues.

Protobuf type google.cloud.aiplatform.v1beta1.WriteFeatureValuesResponse

WriteTensorboardExperimentDataRequest

Request message for TensorboardService.WriteTensorboardExperimentData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardExperimentDataRequest

WriteTensorboardExperimentDataRequest.Builder

Request message for TensorboardService.WriteTensorboardExperimentData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardExperimentDataRequest

WriteTensorboardExperimentDataResponse

Response message for TensorboardService.WriteTensorboardExperimentData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardExperimentDataResponse

WriteTensorboardExperimentDataResponse.Builder

Response message for TensorboardService.WriteTensorboardExperimentData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardExperimentDataResponse

WriteTensorboardRunDataRequest

Request message for TensorboardService.WriteTensorboardRunData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardRunDataRequest

WriteTensorboardRunDataRequest.Builder

Request message for TensorboardService.WriteTensorboardRunData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardRunDataRequest

WriteTensorboardRunDataResponse

Response message for TensorboardService.WriteTensorboardRunData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardRunDataResponse

WriteTensorboardRunDataResponse.Builder

Response message for TensorboardService.WriteTensorboardRunData.

Protobuf type google.cloud.aiplatform.v1beta1.WriteTensorboardRunDataResponse

XraiAttribution

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models.

Protobuf type google.cloud.aiplatform.v1beta1.XraiAttribution

XraiAttribution.Builder

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models.

Protobuf type google.cloud.aiplatform.v1beta1.XraiAttribution

Interfaces

ActiveLearningConfigOrBuilder

AddContextArtifactsAndExecutionsRequestOrBuilder

AddContextArtifactsAndExecutionsResponseOrBuilder

AddContextChildrenRequestOrBuilder

AddContextChildrenResponseOrBuilder

AddExecutionEventsRequestOrBuilder

AddExecutionEventsResponseOrBuilder

AddTrialMeasurementRequestOrBuilder

AnnotationOrBuilder

AnnotationSpecOrBuilder

ArtifactOrBuilder

AttributionOrBuilder

AutomaticResourcesOrBuilder

AutoscalingMetricSpecOrBuilder

AvroSourceOrBuilder

BatchCreateFeaturesOperationMetadataOrBuilder

BatchCreateFeaturesRequestOrBuilder

BatchCreateFeaturesResponseOrBuilder

BatchCreateTensorboardRunsRequestOrBuilder

BatchCreateTensorboardRunsResponseOrBuilder

BatchCreateTensorboardTimeSeriesRequestOrBuilder

BatchCreateTensorboardTimeSeriesResponseOrBuilder

BatchDedicatedResourcesOrBuilder

BatchImportModelEvaluationSlicesRequestOrBuilder

BatchImportModelEvaluationSlicesResponseOrBuilder

BatchMigrateResourcesOperationMetadata.PartialResultOrBuilder

BatchMigrateResourcesOperationMetadataOrBuilder

BatchMigrateResourcesRequestOrBuilder

BatchMigrateResourcesResponseOrBuilder

BatchPredictionJob.InputConfigOrBuilder

BatchPredictionJob.InstanceConfigOrBuilder

BatchPredictionJob.OutputConfigOrBuilder

BatchPredictionJob.OutputInfoOrBuilder

BatchPredictionJobOrBuilder

BatchReadFeatureValuesOperationMetadataOrBuilder

BatchReadFeatureValuesRequest.EntityTypeSpecOrBuilder

BatchReadFeatureValuesRequest.PassThroughFieldOrBuilder

BatchReadFeatureValuesRequestOrBuilder

BatchReadFeatureValuesResponseOrBuilder

BatchReadTensorboardTimeSeriesDataRequestOrBuilder

BatchReadTensorboardTimeSeriesDataResponseOrBuilder

BigQueryDestinationOrBuilder

BigQuerySourceOrBuilder

BlurBaselineConfigOrBuilder

BoolArrayOrBuilder

CancelBatchPredictionJobRequestOrBuilder

CancelCustomJobRequestOrBuilder

CancelDataLabelingJobRequestOrBuilder

CancelHyperparameterTuningJobRequestOrBuilder

CancelPipelineJobRequestOrBuilder

CancelTrainingPipelineRequestOrBuilder

CheckTrialEarlyStoppingStateMetatdataOrBuilder

CheckTrialEarlyStoppingStateRequestOrBuilder

CheckTrialEarlyStoppingStateResponseOrBuilder

CompleteTrialRequestOrBuilder

CompletionStatsOrBuilder

ContainerRegistryDestinationOrBuilder

ContainerSpecOrBuilder

ContextOrBuilder

CreateArtifactRequestOrBuilder

CreateBatchPredictionJobRequestOrBuilder

CreateContextRequestOrBuilder

CreateCustomJobRequestOrBuilder

CreateDataLabelingJobRequestOrBuilder

CreateDatasetOperationMetadataOrBuilder

CreateDatasetRequestOrBuilder

CreateDeploymentResourcePoolOperationMetadataOrBuilder

CreateDeploymentResourcePoolRequestOrBuilder

CreateEndpointOperationMetadataOrBuilder

CreateEndpointRequestOrBuilder

CreateEntityTypeOperationMetadataOrBuilder

CreateEntityTypeRequestOrBuilder

CreateExecutionRequestOrBuilder

CreateFeatureOperationMetadataOrBuilder

CreateFeatureRequestOrBuilder

CreateFeaturestoreOperationMetadataOrBuilder

CreateFeaturestoreRequestOrBuilder

CreateHyperparameterTuningJobRequestOrBuilder

CreateIndexEndpointOperationMetadataOrBuilder

CreateIndexEndpointRequestOrBuilder

CreateIndexOperationMetadataOrBuilder

CreateIndexRequestOrBuilder

CreateMetadataSchemaRequestOrBuilder

CreateMetadataStoreOperationMetadataOrBuilder

CreateMetadataStoreRequestOrBuilder

CreateModelDeploymentMonitoringJobRequestOrBuilder

CreatePipelineJobRequestOrBuilder

CreateSpecialistPoolOperationMetadataOrBuilder

CreateSpecialistPoolRequestOrBuilder

CreateStudyRequestOrBuilder

CreateTensorboardExperimentRequestOrBuilder

CreateTensorboardOperationMetadataOrBuilder

CreateTensorboardRequestOrBuilder

CreateTensorboardRunRequestOrBuilder

CreateTensorboardTimeSeriesRequestOrBuilder

CreateTrainingPipelineRequestOrBuilder

CreateTrialRequestOrBuilder

CsvDestinationOrBuilder

CsvSourceOrBuilder

CustomJobOrBuilder

CustomJobSpecOrBuilder

DataItemOrBuilder

DataItemViewOrBuilder

DataLabelingJobOrBuilder

DatasetOrBuilder

DedicatedResourcesOrBuilder

DeleteArtifactRequestOrBuilder

DeleteBatchPredictionJobRequestOrBuilder

DeleteContextRequestOrBuilder

DeleteCustomJobRequestOrBuilder

DeleteDataLabelingJobRequestOrBuilder

DeleteDatasetRequestOrBuilder

DeleteDeploymentResourcePoolRequestOrBuilder

DeleteEndpointRequestOrBuilder

DeleteEntityTypeRequestOrBuilder

DeleteExecutionRequestOrBuilder

DeleteFeatureRequestOrBuilder

DeleteFeatureValuesOperationMetadataOrBuilder

DeleteFeatureValuesRequest.SelectEntityOrBuilder

DeleteFeatureValuesRequest.SelectTimeRangeAndFeatureOrBuilder

DeleteFeatureValuesRequestOrBuilder

DeleteFeatureValuesResponseOrBuilder

DeleteFeaturestoreRequestOrBuilder

DeleteHyperparameterTuningJobRequestOrBuilder

DeleteIndexEndpointRequestOrBuilder

DeleteIndexRequestOrBuilder

DeleteMetadataStoreOperationMetadataOrBuilder

DeleteMetadataStoreRequestOrBuilder

DeleteModelDeploymentMonitoringJobRequestOrBuilder

DeleteModelRequestOrBuilder

DeleteModelVersionRequestOrBuilder

DeleteOperationMetadataOrBuilder

DeletePipelineJobRequestOrBuilder

DeleteSpecialistPoolRequestOrBuilder

DeleteStudyRequestOrBuilder

DeleteTensorboardExperimentRequestOrBuilder

DeleteTensorboardRequestOrBuilder

DeleteTensorboardRunRequestOrBuilder

DeleteTensorboardTimeSeriesRequestOrBuilder

DeleteTrainingPipelineRequestOrBuilder

DeleteTrialRequestOrBuilder

DeployIndexOperationMetadataOrBuilder

DeployIndexRequestOrBuilder

DeployIndexResponseOrBuilder

DeployModelOperationMetadataOrBuilder

DeployModelRequestOrBuilder

DeployModelResponseOrBuilder

DeployedIndexAuthConfig.AuthProviderOrBuilder

DeployedIndexAuthConfigOrBuilder

DeployedIndexOrBuilder

DeployedIndexRefOrBuilder

DeployedModelOrBuilder

DeployedModelRefOrBuilder

DeploymentResourcePoolOrBuilder

DestinationFeatureSettingOrBuilder

DiskSpecOrBuilder

DoubleArrayOrBuilder

EncryptionSpecOrBuilder

EndpointOrBuilder

EntityIdSelectorOrBuilder

EntityTypeOrBuilder

EnvVarOrBuilder

EventOrBuilder

ExamplesOrBuilder

ExamplesOverrideOrBuilder

ExamplesRestrictionsNamespaceOrBuilder

ExecutionOrBuilder

ExplainRequestOrBuilder

ExplainResponseOrBuilder

ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder

ExplanationMetadata.InputMetadata.VisualizationOrBuilder

ExplanationMetadata.InputMetadataOrBuilder

ExplanationMetadata.OutputMetadataOrBuilder

ExplanationMetadataOrBuilder

ExplanationMetadataOverride.InputMetadataOverrideOrBuilder

ExplanationMetadataOverrideOrBuilder

ExplanationOrBuilder

ExplanationParametersOrBuilder

ExplanationSpecOrBuilder

ExplanationSpecOverrideOrBuilder

ExportDataConfigOrBuilder

ExportDataOperationMetadataOrBuilder

ExportDataRequestOrBuilder

ExportDataResponseOrBuilder

ExportFeatureValuesOperationMetadataOrBuilder

ExportFeatureValuesRequest.FullExportOrBuilder

ExportFeatureValuesRequest.SnapshotExportOrBuilder

ExportFeatureValuesRequestOrBuilder

ExportFeatureValuesResponseOrBuilder

ExportModelOperationMetadata.OutputInfoOrBuilder

ExportModelOperationMetadataOrBuilder

ExportModelRequest.OutputConfigOrBuilder

ExportModelRequestOrBuilder

ExportModelResponseOrBuilder

ExportTensorboardTimeSeriesDataRequestOrBuilder

ExportTensorboardTimeSeriesDataResponseOrBuilder

Feature.MonitoringStatsAnomalyOrBuilder

FeatureNoiseSigma.NoiseSigmaForFeatureOrBuilder

FeatureNoiseSigmaOrBuilder

FeatureOrBuilder

FeatureSelectorOrBuilder

FeatureStatsAnomalyOrBuilder

FeatureValue.MetadataOrBuilder

FeatureValueDestinationOrBuilder

FeatureValueListOrBuilder

FeatureValueOrBuilder

Featurestore.OnlineServingConfig.ScalingOrBuilder

Featurestore.OnlineServingConfigOrBuilder

FeaturestoreMonitoringConfig.ImportFeaturesAnalysisOrBuilder

FeaturestoreMonitoringConfig.SnapshotAnalysisOrBuilder

FeaturestoreMonitoringConfig.ThresholdConfigOrBuilder

FeaturestoreMonitoringConfigOrBuilder

FeaturestoreOrBuilder

FilterSplitOrBuilder

FractionSplitOrBuilder

GcsDestinationOrBuilder

GcsSourceOrBuilder

GenericOperationMetadataOrBuilder

GetAnnotationSpecRequestOrBuilder

GetArtifactRequestOrBuilder

GetBatchPredictionJobRequestOrBuilder

GetContextRequestOrBuilder

GetCustomJobRequestOrBuilder

GetDataLabelingJobRequestOrBuilder

GetDatasetRequestOrBuilder

GetDeploymentResourcePoolRequestOrBuilder

GetEndpointRequestOrBuilder

GetEntityTypeRequestOrBuilder

GetExecutionRequestOrBuilder

GetFeatureRequestOrBuilder

GetFeaturestoreRequestOrBuilder

GetHyperparameterTuningJobRequestOrBuilder

GetIndexEndpointRequestOrBuilder

GetIndexRequestOrBuilder

GetMetadataSchemaRequestOrBuilder

GetMetadataStoreRequestOrBuilder

GetModelDeploymentMonitoringJobRequestOrBuilder

GetModelEvaluationRequestOrBuilder

GetModelEvaluationSliceRequestOrBuilder

GetModelRequestOrBuilder

GetPipelineJobRequestOrBuilder

GetSpecialistPoolRequestOrBuilder

GetStudyRequestOrBuilder

GetTensorboardExperimentRequestOrBuilder

GetTensorboardRequestOrBuilder

GetTensorboardRunRequestOrBuilder

GetTensorboardTimeSeriesRequestOrBuilder

GetTrainingPipelineRequestOrBuilder

GetTrialRequestOrBuilder

HyperparameterTuningJobOrBuilder

IdMatcherOrBuilder

ImportDataConfigOrBuilder

ImportDataOperationMetadataOrBuilder

ImportDataRequestOrBuilder

ImportDataResponseOrBuilder

ImportFeatureValuesOperationMetadataOrBuilder

ImportFeatureValuesRequest.FeatureSpecOrBuilder

ImportFeatureValuesRequestOrBuilder

ImportFeatureValuesResponseOrBuilder

ImportModelEvaluationRequestOrBuilder

IndexDatapoint.CrowdingTagOrBuilder

IndexDatapoint.RestrictionOrBuilder

IndexDatapointOrBuilder

IndexEndpointOrBuilder

IndexOrBuilder

IndexPrivateEndpointsOrBuilder

IndexStatsOrBuilder

InputDataConfigOrBuilder

Int64ArrayOrBuilder

IntegratedGradientsAttributionOrBuilder

LineageSubgraphOrBuilder

ListAnnotationsRequestOrBuilder

ListAnnotationsResponseOrBuilder

ListArtifactsRequestOrBuilder

ListArtifactsResponseOrBuilder

ListBatchPredictionJobsRequestOrBuilder

ListBatchPredictionJobsResponseOrBuilder

ListContextsRequestOrBuilder

ListContextsResponseOrBuilder

ListCustomJobsRequestOrBuilder

ListCustomJobsResponseOrBuilder

ListDataItemsRequestOrBuilder

ListDataItemsResponseOrBuilder

ListDataLabelingJobsRequestOrBuilder

ListDataLabelingJobsResponseOrBuilder

ListDatasetsRequestOrBuilder

ListDatasetsResponseOrBuilder

ListDeploymentResourcePoolsRequestOrBuilder

ListDeploymentResourcePoolsResponseOrBuilder

ListEndpointsRequestOrBuilder

ListEndpointsResponseOrBuilder

ListEntityTypesRequestOrBuilder

ListEntityTypesResponseOrBuilder

ListExecutionsRequestOrBuilder

ListExecutionsResponseOrBuilder

ListFeaturesRequestOrBuilder

ListFeaturesResponseOrBuilder

ListFeaturestoresRequestOrBuilder

ListFeaturestoresResponseOrBuilder

ListHyperparameterTuningJobsRequestOrBuilder

ListHyperparameterTuningJobsResponseOrBuilder

ListIndexEndpointsRequestOrBuilder

ListIndexEndpointsResponseOrBuilder

ListIndexesRequestOrBuilder

ListIndexesResponseOrBuilder

ListMetadataSchemasRequestOrBuilder

ListMetadataSchemasResponseOrBuilder

ListMetadataStoresRequestOrBuilder

ListMetadataStoresResponseOrBuilder

ListModelDeploymentMonitoringJobsRequestOrBuilder

ListModelDeploymentMonitoringJobsResponseOrBuilder

ListModelEvaluationSlicesRequestOrBuilder

ListModelEvaluationSlicesResponseOrBuilder

ListModelEvaluationsRequestOrBuilder

ListModelEvaluationsResponseOrBuilder

ListModelVersionsRequestOrBuilder

ListModelVersionsResponseOrBuilder

ListModelsRequestOrBuilder

ListModelsResponseOrBuilder

ListOptimalTrialsRequestOrBuilder

ListOptimalTrialsResponseOrBuilder

ListPipelineJobsRequestOrBuilder

ListPipelineJobsResponseOrBuilder

ListSavedQueriesRequestOrBuilder

ListSavedQueriesResponseOrBuilder

ListSpecialistPoolsRequestOrBuilder

ListSpecialistPoolsResponseOrBuilder

ListStudiesRequestOrBuilder

ListStudiesResponseOrBuilder

ListTensorboardExperimentsRequestOrBuilder

ListTensorboardExperimentsResponseOrBuilder

ListTensorboardRunsRequestOrBuilder

ListTensorboardRunsResponseOrBuilder

ListTensorboardTimeSeriesRequestOrBuilder

ListTensorboardTimeSeriesResponseOrBuilder

ListTensorboardsRequestOrBuilder

ListTensorboardsResponseOrBuilder

ListTrainingPipelinesRequestOrBuilder

ListTrainingPipelinesResponseOrBuilder

ListTrialsRequestOrBuilder

ListTrialsResponseOrBuilder

LookupStudyRequestOrBuilder

MachineSpecOrBuilder

ManualBatchTuningParametersOrBuilder

Measurement.MetricOrBuilder

MeasurementOrBuilder

MergeVersionAliasesRequestOrBuilder

MetadataSchemaOrBuilder

MetadataStore.MetadataStoreStateOrBuilder

MetadataStoreOrBuilder

MigratableResource.AutomlDatasetOrBuilder

MigratableResource.AutomlModelOrBuilder

MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDatasetOrBuilder

MigratableResource.DataLabelingDatasetOrBuilder

MigratableResource.MlEngineModelVersionOrBuilder

MigratableResourceOrBuilder

MigrateResourceRequest.MigrateAutomlDatasetConfigOrBuilder

MigrateResourceRequest.MigrateAutomlModelConfigOrBuilder

MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfigOrBuilder

MigrateResourceRequest.MigrateDataLabelingDatasetConfigOrBuilder

MigrateResourceRequest.MigrateMlEngineModelVersionConfigOrBuilder

MigrateResourceRequestOrBuilder

MigrateResourceResponseOrBuilder

Model.ExportFormatOrBuilder

ModelContainerSpecOrBuilder

ModelDeploymentMonitoringBigQueryTableOrBuilder

ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadataOrBuilder

ModelDeploymentMonitoringJobOrBuilder

ModelDeploymentMonitoringObjectiveConfigOrBuilder

ModelDeploymentMonitoringScheduleConfigOrBuilder

ModelEvaluation.ModelEvaluationExplanationSpecOrBuilder

ModelEvaluationOrBuilder

ModelEvaluationSlice.SliceOrBuilder

ModelEvaluationSliceOrBuilder

ModelExplanationOrBuilder

ModelMonitoringAlertConfig.EmailAlertConfigOrBuilder

ModelMonitoringAlertConfigOrBuilder

ModelMonitoringConfigOrBuilder

ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaselineOrBuilder

ModelMonitoringObjectiveConfig.ExplanationConfigOrBuilder

ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfigOrBuilder

ModelMonitoringObjectiveConfig.TrainingDatasetOrBuilder

ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfigOrBuilder

ModelMonitoringObjectiveConfigOrBuilder

ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomaliesOrBuilder

ModelMonitoringStatsAnomaliesOrBuilder

ModelOrBuilder

ModelSourceInfoOrBuilder

MutateDeployedIndexOperationMetadataOrBuilder

MutateDeployedIndexRequestOrBuilder

MutateDeployedIndexResponseOrBuilder

NearestNeighborSearchOperationMetadata.ContentValidationStatsOrBuilder

NearestNeighborSearchOperationMetadata.RecordErrorOrBuilder

NearestNeighborSearchOperationMetadataOrBuilder

NeighborOrBuilder

NfsMountOrBuilder

PauseModelDeploymentMonitoringJobRequestOrBuilder

PipelineJob.RuntimeConfig.InputArtifactOrBuilder

PipelineJob.RuntimeConfigOrBuilder

PipelineJobDetailOrBuilder

PipelineJobOrBuilder

PipelineTaskDetail.ArtifactListOrBuilder

PipelineTaskDetail.PipelineTaskStatusOrBuilder

PipelineTaskDetailOrBuilder

PipelineTaskExecutorDetail.ContainerDetailOrBuilder

PipelineTaskExecutorDetail.CustomJobDetailOrBuilder

PipelineTaskExecutorDetailOrBuilder

PipelineTemplateMetadataOrBuilder

PortOrBuilder

PredefinedSplitOrBuilder

PredictRequestOrBuilder

PredictRequestResponseLoggingConfigOrBuilder

PredictResponseOrBuilder

PredictSchemataOrBuilder

PresetsOrBuilder

PrivateEndpointsOrBuilder

PurgeArtifactsMetadataOrBuilder

PurgeArtifactsRequestOrBuilder

PurgeArtifactsResponseOrBuilder

PurgeContextsMetadataOrBuilder

PurgeContextsRequestOrBuilder

PurgeContextsResponseOrBuilder

PurgeExecutionsMetadataOrBuilder

PurgeExecutionsRequestOrBuilder

PurgeExecutionsResponseOrBuilder

PythonPackageSpecOrBuilder

QueryArtifactLineageSubgraphRequestOrBuilder

QueryContextLineageSubgraphRequestOrBuilder

QueryDeployedModelsRequestOrBuilder

QueryDeployedModelsResponseOrBuilder

QueryExecutionInputsAndOutputsRequestOrBuilder

RawPredictRequestOrBuilder

ReadFeatureValuesRequestOrBuilder

ReadFeatureValuesResponse.EntityView.DataOrBuilder

ReadFeatureValuesResponse.EntityViewOrBuilder

ReadFeatureValuesResponse.FeatureDescriptorOrBuilder

ReadFeatureValuesResponse.HeaderOrBuilder

ReadFeatureValuesResponseOrBuilder

ReadTensorboardBlobDataRequestOrBuilder

ReadTensorboardBlobDataResponseOrBuilder

ReadTensorboardTimeSeriesDataRequestOrBuilder

ReadTensorboardTimeSeriesDataResponseOrBuilder

RemoveContextChildrenRequestOrBuilder

RemoveContextChildrenResponseOrBuilder

RemoveDatapointsRequestOrBuilder

RemoveDatapointsResponseOrBuilder

ResourcesConsumedOrBuilder

ResumeModelDeploymentMonitoringJobRequestOrBuilder

SampleConfigOrBuilder

SampledShapleyAttributionOrBuilder

SamplingStrategy.RandomSampleConfigOrBuilder

SamplingStrategyOrBuilder

SavedQueryOrBuilder

ScalarOrBuilder

SchedulingOrBuilder

SearchDataItemsRequest.OrderByAnnotationOrBuilder

SearchDataItemsRequestOrBuilder

SearchDataItemsResponseOrBuilder

SearchFeaturesRequestOrBuilder

SearchFeaturesResponseOrBuilder

SearchMigratableResourcesRequestOrBuilder

SearchMigratableResourcesResponseOrBuilder

SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjectiveOrBuilder

SearchModelDeploymentMonitoringStatsAnomaliesRequestOrBuilder

SearchModelDeploymentMonitoringStatsAnomaliesResponseOrBuilder

SmoothGradConfigOrBuilder

SpecialistPoolOrBuilder

StopTrialRequestOrBuilder

StratifiedSplitOrBuilder

StreamingReadFeatureValuesRequestOrBuilder

StringArrayOrBuilder

StudyOrBuilder

StudySpec.ConvexAutomatedStoppingSpecOrBuilder

StudySpec.ConvexStopConfigOrBuilder

StudySpec.DecayCurveAutomatedStoppingSpecOrBuilder

StudySpec.MedianAutomatedStoppingSpecOrBuilder

StudySpec.MetricSpecOrBuilder

StudySpec.ParameterSpec.CategoricalValueSpecOrBuilder

StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueConditionOrBuilder

StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueConditionOrBuilder

StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueConditionOrBuilder

StudySpec.ParameterSpec.ConditionalParameterSpecOrBuilder

StudySpec.ParameterSpec.DiscreteValueSpecOrBuilder

StudySpec.ParameterSpec.DoubleValueSpecOrBuilder

StudySpec.ParameterSpec.IntegerValueSpecOrBuilder

StudySpec.ParameterSpecOrBuilder

StudySpec.TransferLearningConfigOrBuilder

StudySpecOrBuilder

SuggestTrialsMetadataOrBuilder

SuggestTrialsRequestOrBuilder

SuggestTrialsResponseOrBuilder

TFRecordDestinationOrBuilder

TensorboardBlobOrBuilder

TensorboardBlobSequenceOrBuilder

TensorboardExperimentOrBuilder

TensorboardOrBuilder

TensorboardRunOrBuilder

TensorboardTensorOrBuilder

TensorboardTimeSeries.MetadataOrBuilder

TensorboardTimeSeriesOrBuilder

ThresholdConfigOrBuilder

TimeSeriesDataOrBuilder

TimeSeriesDataPointOrBuilder

TimestampSplitOrBuilder

TrainingConfigOrBuilder

TrainingPipelineOrBuilder

Trial.ParameterOrBuilder

TrialOrBuilder

UndeployIndexOperationMetadataOrBuilder

UndeployIndexRequestOrBuilder

UndeployIndexResponseOrBuilder

UndeployModelOperationMetadataOrBuilder

UndeployModelRequestOrBuilder

UndeployModelResponseOrBuilder

UnmanagedContainerModelOrBuilder

UpdateArtifactRequestOrBuilder

UpdateContextRequestOrBuilder

UpdateDatasetRequestOrBuilder

UpdateDeploymentResourcePoolOperationMetadataOrBuilder

UpdateEndpointRequestOrBuilder

UpdateEntityTypeRequestOrBuilder

UpdateExecutionRequestOrBuilder

UpdateExplanationDatasetOperationMetadataOrBuilder

UpdateExplanationDatasetRequestOrBuilder

UpdateExplanationDatasetResponseOrBuilder

UpdateFeatureRequestOrBuilder

UpdateFeaturestoreOperationMetadataOrBuilder

UpdateFeaturestoreRequestOrBuilder

UpdateIndexEndpointRequestOrBuilder

UpdateIndexOperationMetadataOrBuilder

UpdateIndexRequestOrBuilder

UpdateModelDeploymentMonitoringJobOperationMetadataOrBuilder

UpdateModelDeploymentMonitoringJobRequestOrBuilder

UpdateModelRequestOrBuilder

UpdateSpecialistPoolOperationMetadataOrBuilder

UpdateSpecialistPoolRequestOrBuilder

UpdateTensorboardExperimentRequestOrBuilder

UpdateTensorboardOperationMetadataOrBuilder

UpdateTensorboardRequestOrBuilder

UpdateTensorboardRunRequestOrBuilder

UpdateTensorboardTimeSeriesRequestOrBuilder

UploadModelOperationMetadataOrBuilder

UploadModelRequestOrBuilder

UploadModelResponseOrBuilder

UpsertDatapointsRequestOrBuilder

UpsertDatapointsResponseOrBuilder

UserActionReferenceOrBuilder

ValueOrBuilder

WorkerPoolSpecOrBuilder

WriteFeatureValuesPayloadOrBuilder

WriteFeatureValuesRequestOrBuilder

WriteFeatureValuesResponseOrBuilder

WriteTensorboardExperimentDataRequestOrBuilder

WriteTensorboardExperimentDataResponseOrBuilder

WriteTensorboardRunDataRequestOrBuilder

WriteTensorboardRunDataResponseOrBuilder

XraiAttributionOrBuilder

Enums

AcceleratorType

Represents a hardware accelerator type.

Protobuf enum google.cloud.aiplatform.v1beta1.AcceleratorType

ActiveLearningConfig.HumanLabelingBudgetCase

Artifact.State

Describes the state of the Artifact.

Protobuf enum google.cloud.aiplatform.v1beta1.Artifact.State

BatchMigrateResourcesOperationMetadata.PartialResult.ResultCase

BatchPredictionJob.InputConfig.SourceCase

BatchPredictionJob.OutputConfig.DestinationCase

BatchPredictionJob.OutputInfo.OutputLocationCase

BatchReadFeatureValuesRequest.ReadOptionCase

DeleteFeatureValuesRequest.DeleteOptionCase

DeployedModel.PredictionResourcesCase

EntityIdSelector.EntityIdsSourceCase

Event.Type

Describes whether an Event's Artifact is the Execution's input or output.

Protobuf enum google.cloud.aiplatform.v1beta1.Event.Type

Examples.ConfigCase

ExamplesOverride.DataFormat

Data format enum.

Protobuf enum google.cloud.aiplatform.v1beta1.ExamplesOverride.DataFormat

Execution.State

Describes the state of the Execution.

Protobuf enum google.cloud.aiplatform.v1beta1.Execution.State

ExplanationMetadata.InputMetadata.Encoding

Defines how a feature is encoded. Defaults to IDENTITY.

Protobuf enum google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding

ExplanationMetadata.InputMetadata.Visualization.ColorMap

The color scheme used for highlighting areas.

Protobuf enum google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization.ColorMap

ExplanationMetadata.InputMetadata.Visualization.OverlayType

How the original image is displayed in the visualization.

Protobuf enum google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization.OverlayType

ExplanationMetadata.InputMetadata.Visualization.Polarity

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

Protobuf enum google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization.Polarity

ExplanationMetadata.InputMetadata.Visualization.Type

Type of the image visualization. Only applicable to Integrated Gradients attribution.

Protobuf enum google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization.Type

ExplanationMetadata.OutputMetadata.DisplayNameMappingCase

ExplanationParameters.MethodCase

ExportDataConfig.DestinationCase

ExportFeatureValuesRequest.ModeCase

Feature.MonitoringStatsAnomaly.Objective

If the objective in the request is both Import Feature Analysis and Snapshot Analysis, this objective could be one of them. Otherwise, this objective should be the same as the objective in the request.

Protobuf enum google.cloud.aiplatform.v1beta1.Feature.MonitoringStatsAnomaly.Objective

Feature.ValueType

An enum representing the value type of a feature.

Protobuf enum google.cloud.aiplatform.v1beta1.Feature.ValueType

FeatureValue.ValueCase

FeatureValueDestination.DestinationCase

Featurestore.State

Possible states a featurestore can have.

Protobuf enum google.cloud.aiplatform.v1beta1.Featurestore.State

FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline

Defines the baseline to do anomaly detection for feature values imported by each [ImportFeatureValues][] operation.

Protobuf enum google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline

FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State

The state defines whether to enable ImportFeature analysis.

Protobuf enum google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State

FeaturestoreMonitoringConfig.ThresholdConfig.ThresholdCase

ImportDataConfig.SourceCase

ImportFeatureValuesRequest.FeatureTimeSourceCase

ImportFeatureValuesRequest.SourceCase

Index.IndexUpdateMethod

The update method of an Index.

Protobuf enum google.cloud.aiplatform.v1beta1.Index.IndexUpdateMethod

InputDataConfig.DestinationCase

InputDataConfig.SplitCase

JobState

Describes the state of a job.

Protobuf enum google.cloud.aiplatform.v1beta1.JobState

MetadataSchema.MetadataSchemaType

Describes the type of the MetadataSchema.

Protobuf enum google.cloud.aiplatform.v1beta1.MetadataSchema.MetadataSchemaType

MigratableResource.ResourceCase

MigrateResourceRequest.RequestCase

MigrateResourceResponse.MigratedResourceCase

Model.DeploymentResourcesType

Identifies a type of Model's prediction resources.

Protobuf enum google.cloud.aiplatform.v1beta1.Model.DeploymentResourcesType

Model.ExportFormat.ExportableContent

The Model content that can be exported.

Protobuf enum google.cloud.aiplatform.v1beta1.Model.ExportFormat.ExportableContent

ModelDeploymentMonitoringBigQueryTable.LogSource

Indicates where does the log come from.

Protobuf enum google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable.LogSource

ModelDeploymentMonitoringBigQueryTable.LogType

Indicates what type of traffic does the log belong to.

Protobuf enum google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringBigQueryTable.LogType

ModelDeploymentMonitoringJob.MonitoringScheduleState

The state to Specify the monitoring pipeline.

Protobuf enum google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringJob.MonitoringScheduleState

ModelDeploymentMonitoringObjectiveType

The Model Monitoring Objective types.

Protobuf enum google.cloud.aiplatform.v1beta1.ModelDeploymentMonitoringObjectiveType

ModelMonitoringAlertConfig.AlertCase

ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.DestinationCase

ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat

The storage format of the predictions generated BatchPrediction job.

Protobuf enum google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat

ModelMonitoringObjectiveConfig.TrainingDataset.DataSourceCase

ModelSourceInfo.ModelSourceType

Source of the model.

Protobuf enum google.cloud.aiplatform.v1beta1.ModelSourceInfo.ModelSourceType

NearestNeighborSearchOperationMetadata.RecordError.RecordErrorType

Protobuf enum google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata.RecordError.RecordErrorType

PipelineFailurePolicy

Represents the failure policy of a pipeline. Currently, the default of a pipeline is that the pipeline will continue to run until no more tasks can be executed, also known as PIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion.

Protobuf enum google.cloud.aiplatform.v1beta1.PipelineFailurePolicy

PipelineJob.RuntimeConfig.InputArtifact.KindCase

PipelineState

Describes the state of a pipeline.

Protobuf enum google.cloud.aiplatform.v1beta1.PipelineState

PipelineTaskDetail.State

Specifies state of TaskExecution

Protobuf enum google.cloud.aiplatform.v1beta1.PipelineTaskDetail.State

PipelineTaskExecutorDetail.DetailsCase

Presets.Modality

Preset option controlling parameters for different modalities

Protobuf enum google.cloud.aiplatform.v1beta1.Presets.Modality

Presets.Query

Preset option controlling parameters for query speed-precision trade-off

Protobuf enum google.cloud.aiplatform.v1beta1.Presets.Query

ReadFeatureValuesResponse.EntityView.Data.DataCase

SampleConfig.FollowingBatchSampleSizeCase

SampleConfig.InitialBatchSampleSizeCase

SampleConfig.SampleStrategy

Sample strategy decides which subset of DataItems should be selected for human labeling in every batch.

Protobuf enum google.cloud.aiplatform.v1beta1.SampleConfig.SampleStrategy

SearchDataItemsRequest.OrderCase

SmoothGradConfig.GradientNoiseSigmaCase

Study.State

Describes the Study state.

Protobuf enum google.cloud.aiplatform.v1beta1.Study.State

StudySpec.Algorithm

The available search algorithms for the Study.

Protobuf enum google.cloud.aiplatform.v1beta1.StudySpec.Algorithm

StudySpec.AutomatedStoppingSpecCase

StudySpec.MeasurementSelectionType

This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose LAST_MEASUREMENT. On the other hand, if you're in a situation where your system can "over-train" and you expect the performance to get better for a while but then start declining, choose BEST_MEASUREMENT. B) Are your measurements significantly noisy and/or irreproducible? If so, BEST_MEASUREMENT will tend to be over-optimistic, and it may be better to choose LAST_MEASUREMENT. If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen.

Protobuf enum google.cloud.aiplatform.v1beta1.StudySpec.MeasurementSelectionType

StudySpec.MetricSpec.GoalType

The available types of optimization goals.

Protobuf enum google.cloud.aiplatform.v1beta1.StudySpec.MetricSpec.GoalType

StudySpec.ObservationNoise

Describes the noise level of the repeated observations. "Noisy" means that the repeated observations with the same Trial parameters may lead to different metric evaluations.

Protobuf enum google.cloud.aiplatform.v1beta1.StudySpec.ObservationNoise

StudySpec.ParameterSpec.ConditionalParameterSpec.ParentValueConditionCase

StudySpec.ParameterSpec.ParameterValueSpecCase

StudySpec.ParameterSpec.ScaleType

The type of scaling that should be applied to this parameter.

Protobuf enum google.cloud.aiplatform.v1beta1.StudySpec.ParameterSpec.ScaleType

TensorboardTimeSeries.ValueType

An enum representing the value type of a TensorboardTimeSeries.

Protobuf enum google.cloud.aiplatform.v1beta1.TensorboardTimeSeries.ValueType

ThresholdConfig.ThresholdCase

TimeSeriesDataPoint.ValueCase

Trial.State

Describes a Trial state.

Protobuf enum google.cloud.aiplatform.v1beta1.Trial.State

UserActionReference.ReferenceCase

Value.ValueCase

WorkerPoolSpec.TaskCase