google-cloud-aiplatform overview (3.15.0)

com.google.cloud.aiplatform.util

com.google.cloud.aiplatform.v1

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);
 }

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);
 }

com.google.cloud.aiplatform.v1.schema.predict.instance

com.google.cloud.aiplatform.v1.schema.predict.params

com.google.cloud.aiplatform.v1.schema.predict.prediction

com.google.cloud.aiplatform.v1.schema.trainingjob.definition

com.google.cloud.aiplatform.v1.stub

com.google.cloud.aiplatform.v1beta1