- 3.54.0 (latest)
- 3.53.0
- 3.52.0
- 3.50.0
- 3.49.0
- 3.48.0
- 3.47.0
- 3.46.0
- 3.45.0
- 3.44.0
- 3.43.0
- 3.42.0
- 3.41.0
- 3.40.0
- 3.38.0
- 3.37.0
- 3.36.0
- 3.35.0
- 3.34.0
- 3.33.0
- 3.32.0
- 3.31.0
- 3.30.0
- 3.29.0
- 3.28.0
- 3.25.0
- 3.24.0
- 3.23.0
- 3.22.0
- 3.21.0
- 3.20.0
- 3.19.0
- 3.18.0
- 3.17.0
- 3.16.0
- 3.15.0
- 3.14.0
- 3.13.0
- 3.12.0
- 3.11.0
- 3.10.0
- 3.9.0
- 3.8.0
- 3.7.0
- 3.6.0
- 3.5.0
- 3.4.2
- 3.3.0
- 3.2.0
- 3.0.0
- 2.9.8
- 2.8.9
- 2.7.4
- 2.5.3
- 2.4.0
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:
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:
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:
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:
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:
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:
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:
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:
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:
try (MigrationServiceClient migrationServiceClient = MigrationServiceClient.create()) {
LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
for (MigratableResource element :
migrationServiceClient.searchMigratableResources(parent).iterateAll()) {
// doThingsWith(element);
}
}
ModelServiceClient
Service Description: A service for managing Vertex AI's machine learning Models.
Sample for ModelServiceClient:
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:
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:
try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
List<Value> instances = new ArrayList<>();
Value parameters = Value.newBuilder().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:
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:
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:
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
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.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
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. Next Id: 14
Protobuf type google.cloud.aiplatform.v1beta1.CustomJobSpec
CustomJobSpec.Builder
Represents the spec of a CustomJob. Next Id: 14
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
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:
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:
- 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.
- 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.
- 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:
DatasetServiceSettings datasetServiceSettings =
DatasetServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings);
To customize the endpoint:
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
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:
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
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
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
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
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:
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:
- 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.
- 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.
- 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:
EndpointServiceSettings endpointServiceSettings =
EndpointServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
EndpointServiceClient endpointServiceClient =
EndpointServiceClient.create(endpointServiceSettings);
To customize the endpoint:
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
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:
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.
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
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
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. NEXT ID: 15
Protobuf type google.cloud.aiplatform.v1beta1.FeatureValue
FeatureValue.Builder
Value for a feature. NEXT ID: 15
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.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
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:
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:
- 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.
- 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.
- 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:
FeaturestoreOnlineServingServiceSettings featurestoreOnlineServingServiceSettings =
FeaturestoreOnlineServingServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
FeaturestoreOnlineServingServiceClient featurestoreOnlineServingServiceClient =
FeaturestoreOnlineServingServiceClient.create(featurestoreOnlineServingServiceSettings);
To customize the endpoint:
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.
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:
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:
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:
- 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.
- 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.
- 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:
FeaturestoreServiceSettings featurestoreServiceSettings =
FeaturestoreServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
FeaturestoreServiceClient featurestoreServiceClient =
FeaturestoreServiceClient.create(featurestoreServiceSettings);
To customize the endpoint:
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.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:
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
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
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
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:
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:
- 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.
- 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.
- 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:
IndexEndpointServiceSettings indexEndpointServiceSettings =
IndexEndpointServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
IndexEndpointServiceClient indexEndpointServiceClient =
IndexEndpointServiceClient.create(indexEndpointServiceSettings);
To customize the endpoint:
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
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:
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:
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:
- 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.
- 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.
- 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:
IndexServiceSettings indexServiceSettings =
IndexServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
IndexServiceClient indexServiceClient = IndexServiceClient.create(indexServiceSettings);
To customize the endpoint:
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
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:
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.
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:
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:
- 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.
- 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.
- 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:
JobServiceSettings jobServiceSettings =
JobServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings);
To customize the endpoint:
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.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:
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
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
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
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
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:
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:
- 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.
- 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.
- 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:
MetadataServiceSettings metadataServiceSettings =
MetadataServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
MetadataServiceClient metadataServiceClient =
MetadataServiceClient.create(metadataServiceSettings);
To customize the endpoint:
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.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:
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:
try (MigrationServiceClient migrationServiceClient = MigrationServiceClient.create()) {
LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
for (MigratableResource element :
migrationServiceClient.searchMigratableResources(parent).iterateAll()) {
// doThingsWith(element);
}
}
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:
- 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.
- 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.
- 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:
MigrationServiceSettings migrationServiceSettings =
MigrationServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
MigrationServiceClient migrationServiceClient =
MigrationServiceClient.create(migrationServiceSettings);
To customize the endpoint:
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.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 searchMigratableResources to 30 seconds:
MigrationServiceSettings.Builder migrationServiceSettingsBuilder =
MigrationServiceSettings.newBuilder();
migrationServiceSettingsBuilder
.searchMigratableResourcesSettings()
.setRetrySettings(
migrationServiceSettingsBuilder
.searchMigratableResourcesSettings()
.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
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
Next ID: 3
Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig
ModelMonitoringAlertConfig.Builder
Next ID: 3
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
ModelMonitoringObjectiveConfig
Next ID: 6
Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig
ModelMonitoringObjectiveConfig.Builder
Next ID: 6
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:
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:
- 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.
- 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.
- 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:
ModelServiceSettings modelServiceSettings =
ModelServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings);
To customize the endpoint:
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.ListModelEvaluationSlicesFixedSizeCollection
ModelServiceClient.ListModelEvaluationSlicesPage
ModelServiceClient.ListModelEvaluationSlicesPagedResponse
ModelServiceClient.ListModelEvaluationsFixedSizeCollection
ModelServiceClient.ListModelEvaluationsPage
ModelServiceClient.ListModelEvaluationsPagedResponse
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:
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.
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
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
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
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:
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:
- 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.
- 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.
- 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:
PipelineServiceSettings pipelineServiceSettings =
PipelineServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
PipelineServiceClient pipelineServiceClient =
PipelineServiceClient.create(pipelineServiceSettings);
To customize the endpoint:
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.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:
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
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
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:
try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
List<Value> instances = new ArrayList<>();
Value parameters = Value.newBuilder().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:
- 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.
- 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.
- 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:
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
To customize the endpoint:
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.
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:
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.
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
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
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
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. Next ID: 2
Protobuf type google.cloud.aiplatform.v1beta1.SamplingStrategy
SamplingStrategy.Builder
Sampling Strategy for logging, can be for both training and prediction dataset. Next ID: 2
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
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
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
Similarity
Similarity explainability that returns the nearest neighbors from the provided dataset.
Protobuf type google.cloud.aiplatform.v1beta1.Similarity
Similarity.Builder
Similarity explainability that returns the nearest neighbors from the provided dataset.
Protobuf type google.cloud.aiplatform.v1beta1.Similarity
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:
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:
- 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.
- 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.
- 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:
SpecialistPoolServiceSettings specialistPoolServiceSettings =
SpecialistPoolServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
SpecialistPoolServiceClient specialistPoolServiceClient =
SpecialistPoolServiceClient.create(specialistPoolServiceSettings);
To customize the endpoint:
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.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:
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.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
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 GCP 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 GCP 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:
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:
- 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.
- 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.
- 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:
TensorboardServiceSettings tensorboardServiceSettings =
TensorboardServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
TensorboardServiceClient tensorboardServiceClient =
TensorboardServiceClient.create(tensorboardServiceSettings);
To customize the endpoint:
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.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:
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. Next ID: 3
Protobuf type google.cloud.aiplatform.v1beta1.ThresholdConfig
ThresholdConfig.Builder
The config for feature monitoring threshold. Next ID: 3
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
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
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
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:
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:
- 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.
- 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.
- 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:
VizierServiceSettings vizierServiceSettings =
VizierServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
VizierServiceClient vizierServiceClient = VizierServiceClient.create(vizierServiceSettings);
To customize the endpoint:
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.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:
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
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
BatchMigrateResourcesOperationMetadata.PartialResultOrBuilder
BatchMigrateResourcesOperationMetadataOrBuilder
BatchMigrateResourcesRequestOrBuilder
BatchMigrateResourcesResponseOrBuilder
BatchPredictionJob.InputConfigOrBuilder
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
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
DataLabelingJobOrBuilder
DatasetOrBuilder
DedicatedResourcesOrBuilder
DeleteArtifactRequestOrBuilder
DeleteBatchPredictionJobRequestOrBuilder
DeleteContextRequestOrBuilder
DeleteCustomJobRequestOrBuilder
DeleteDataLabelingJobRequestOrBuilder
DeleteDatasetRequestOrBuilder
DeleteEndpointRequestOrBuilder
DeleteEntityTypeRequestOrBuilder
DeleteExecutionRequestOrBuilder
DeleteFeatureRequestOrBuilder
DeleteFeaturestoreRequestOrBuilder
DeleteHyperparameterTuningJobRequestOrBuilder
DeleteIndexEndpointRequestOrBuilder
DeleteIndexRequestOrBuilder
DeleteMetadataStoreOperationMetadataOrBuilder
DeleteMetadataStoreRequestOrBuilder
DeleteModelDeploymentMonitoringJobRequestOrBuilder
DeleteModelRequestOrBuilder
DeleteOperationMetadataOrBuilder
DeletePipelineJobRequestOrBuilder
DeleteSpecialistPoolRequestOrBuilder
DeleteStudyRequestOrBuilder
DeleteTensorboardExperimentRequestOrBuilder
DeleteTensorboardRequestOrBuilder
DeleteTensorboardRunRequestOrBuilder
DeleteTensorboardTimeSeriesRequestOrBuilder
DeleteTrainingPipelineRequestOrBuilder
DeleteTrialRequestOrBuilder
DeployIndexOperationMetadataOrBuilder
DeployIndexRequestOrBuilder
DeployIndexResponseOrBuilder
DeployModelOperationMetadataOrBuilder
DeployModelRequestOrBuilder
DeployModelResponseOrBuilder
DeployedIndexAuthConfig.AuthProviderOrBuilder
DeployedIndexAuthConfigOrBuilder
DeployedIndexOrBuilder
DeployedIndexRefOrBuilder
DeployedModelOrBuilder
DeployedModelRefOrBuilder
DestinationFeatureSettingOrBuilder
DiskSpecOrBuilder
DoubleArrayOrBuilder
EncryptionSpecOrBuilder
EndpointOrBuilder
EntityTypeOrBuilder
EnvVarOrBuilder
EventOrBuilder
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
FeatureNoiseSigma.NoiseSigmaForFeatureOrBuilder
FeatureNoiseSigmaOrBuilder
FeatureOrBuilder
FeatureSelectorOrBuilder
FeatureStatsAnomalyOrBuilder
FeatureValue.MetadataOrBuilder
FeatureValueDestinationOrBuilder
FeatureValueListOrBuilder
FeatureValueOrBuilder
Featurestore.OnlineServingConfig.ScalingOrBuilder
Featurestore.OnlineServingConfigOrBuilder
FeaturestoreMonitoringConfig.SnapshotAnalysisOrBuilder
FeaturestoreMonitoringConfigOrBuilder
FeaturestoreOrBuilder
FilterSplitOrBuilder
FractionSplitOrBuilder
GcsDestinationOrBuilder
GcsSourceOrBuilder
GenericOperationMetadataOrBuilder
GetAnnotationSpecRequestOrBuilder
GetArtifactRequestOrBuilder
GetBatchPredictionJobRequestOrBuilder
GetContextRequestOrBuilder
GetCustomJobRequestOrBuilder
GetDataLabelingJobRequestOrBuilder
GetDatasetRequestOrBuilder
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
IndexEndpointOrBuilder
IndexOrBuilder
IndexPrivateEndpointsOrBuilder
InputDataConfigOrBuilder
Int64ArrayOrBuilder
IntegratedGradientsAttributionOrBuilder
LineageSubgraphOrBuilder
ListAnnotationsRequestOrBuilder
ListAnnotationsResponseOrBuilder
ListArtifactsRequestOrBuilder
ListArtifactsResponseOrBuilder
ListBatchPredictionJobsRequestOrBuilder
ListBatchPredictionJobsResponseOrBuilder
ListContextsRequestOrBuilder
ListContextsResponseOrBuilder
ListCustomJobsRequestOrBuilder
ListCustomJobsResponseOrBuilder
ListDataItemsRequestOrBuilder
ListDataItemsResponseOrBuilder
ListDataLabelingJobsRequestOrBuilder
ListDataLabelingJobsResponseOrBuilder
ListDatasetsRequestOrBuilder
ListDatasetsResponseOrBuilder
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
ListModelsRequestOrBuilder
ListModelsResponseOrBuilder
ListOptimalTrialsRequestOrBuilder
ListOptimalTrialsResponseOrBuilder
ListPipelineJobsRequestOrBuilder
ListPipelineJobsResponseOrBuilder
ListSpecialistPoolsRequestOrBuilder
ListSpecialistPoolsResponseOrBuilder
ListStudiesRequestOrBuilder
ListStudiesResponseOrBuilder
ListTensorboardExperimentsRequestOrBuilder
ListTensorboardExperimentsResponseOrBuilder
ListTensorboardRunsRequestOrBuilder
ListTensorboardRunsResponseOrBuilder
ListTensorboardTimeSeriesRequestOrBuilder
ListTensorboardTimeSeriesResponseOrBuilder
ListTensorboardsRequestOrBuilder
ListTensorboardsResponseOrBuilder
ListTrainingPipelinesRequestOrBuilder
ListTrainingPipelinesResponseOrBuilder
ListTrialsRequestOrBuilder
ListTrialsResponseOrBuilder
LookupStudyRequestOrBuilder
MachineSpecOrBuilder
ManualBatchTuningParametersOrBuilder
Measurement.MetricOrBuilder
MeasurementOrBuilder
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
ModelDeploymentMonitoringJobOrBuilder
ModelDeploymentMonitoringObjectiveConfigOrBuilder
ModelDeploymentMonitoringScheduleConfigOrBuilder
ModelEvaluation.ModelEvaluationExplanationSpecOrBuilder
ModelEvaluationOrBuilder
ModelEvaluationSlice.SliceOrBuilder
ModelEvaluationSliceOrBuilder
ModelExplanationOrBuilder
ModelMonitoringAlertConfig.EmailAlertConfigOrBuilder
ModelMonitoringAlertConfigOrBuilder
ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaselineOrBuilder
ModelMonitoringObjectiveConfig.ExplanationConfigOrBuilder
ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfigOrBuilder
ModelMonitoringObjectiveConfig.TrainingDatasetOrBuilder
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfigOrBuilder
ModelMonitoringObjectiveConfigOrBuilder
ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomaliesOrBuilder
ModelMonitoringStatsAnomaliesOrBuilder
ModelOrBuilder
MutateDeployedIndexOperationMetadataOrBuilder
MutateDeployedIndexRequestOrBuilder
MutateDeployedIndexResponseOrBuilder
NearestNeighborSearchOperationMetadata.ContentValidationStatsOrBuilder
NearestNeighborSearchOperationMetadata.RecordErrorOrBuilder
NearestNeighborSearchOperationMetadataOrBuilder
PauseModelDeploymentMonitoringJobRequestOrBuilder
PipelineJob.RuntimeConfigOrBuilder
PipelineJobDetailOrBuilder
PipelineJobOrBuilder
PipelineTaskDetail.ArtifactListOrBuilder
PipelineTaskDetail.PipelineTaskStatusOrBuilder
PipelineTaskDetailOrBuilder
PipelineTaskExecutorDetail.ContainerDetailOrBuilder
PipelineTaskExecutorDetail.CustomJobDetailOrBuilder
PipelineTaskExecutorDetailOrBuilder
PortOrBuilder
PredefinedSplitOrBuilder
PredictRequestOrBuilder
PredictResponseOrBuilder
PredictSchemataOrBuilder
PrivateEndpointsOrBuilder
PurgeArtifactsMetadataOrBuilder
PurgeArtifactsRequestOrBuilder
PurgeArtifactsResponseOrBuilder
PurgeContextsMetadataOrBuilder
PurgeContextsRequestOrBuilder
PurgeContextsResponseOrBuilder
PurgeExecutionsMetadataOrBuilder
PurgeExecutionsRequestOrBuilder
PurgeExecutionsResponseOrBuilder
PythonPackageSpecOrBuilder
QueryArtifactLineageSubgraphRequestOrBuilder
QueryContextLineageSubgraphRequestOrBuilder
QueryExecutionInputsAndOutputsRequestOrBuilder
RawPredictRequestOrBuilder
ReadFeatureValuesRequestOrBuilder
ReadFeatureValuesResponse.EntityView.DataOrBuilder
ReadFeatureValuesResponse.EntityViewOrBuilder
ReadFeatureValuesResponse.FeatureDescriptorOrBuilder
ReadFeatureValuesResponse.HeaderOrBuilder
ReadFeatureValuesResponseOrBuilder
ReadTensorboardBlobDataRequestOrBuilder
ReadTensorboardBlobDataResponseOrBuilder
ReadTensorboardTimeSeriesDataRequestOrBuilder
ReadTensorboardTimeSeriesDataResponseOrBuilder
ResourcesConsumedOrBuilder
ResumeModelDeploymentMonitoringJobRequestOrBuilder
SampleConfigOrBuilder
SampledShapleyAttributionOrBuilder
SamplingStrategy.RandomSampleConfigOrBuilder
SamplingStrategyOrBuilder
ScalarOrBuilder
SchedulingOrBuilder
SearchFeaturesRequestOrBuilder
SearchFeaturesResponseOrBuilder
SearchMigratableResourcesRequestOrBuilder
SearchMigratableResourcesResponseOrBuilder
SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjectiveOrBuilder
SearchModelDeploymentMonitoringStatsAnomaliesRequestOrBuilder
SearchModelDeploymentMonitoringStatsAnomaliesResponseOrBuilder
SimilarityOrBuilder
SmoothGradConfigOrBuilder
SpecialistPoolOrBuilder
StopTrialRequestOrBuilder
StratifiedSplitOrBuilder
StreamingReadFeatureValuesRequestOrBuilder
StringArrayOrBuilder
StudyOrBuilder
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
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
UpdateEndpointRequestOrBuilder
UpdateEntityTypeRequestOrBuilder
UpdateExecutionRequestOrBuilder
UpdateFeatureRequestOrBuilder
UpdateFeaturestoreOperationMetadataOrBuilder
UpdateFeaturestoreRequestOrBuilder
UpdateIndexEndpointRequestOrBuilder
UpdateIndexOperationMetadataOrBuilder
UpdateIndexRequestOrBuilder
UpdateModelDeploymentMonitoringJobOperationMetadataOrBuilder
UpdateModelDeploymentMonitoringJobRequestOrBuilder
UpdateModelRequestOrBuilder
UpdateSpecialistPoolOperationMetadataOrBuilder
UpdateSpecialistPoolRequestOrBuilder
UpdateTensorboardExperimentRequestOrBuilder
UpdateTensorboardOperationMetadataOrBuilder
UpdateTensorboardRequestOrBuilder
UpdateTensorboardRunRequestOrBuilder
UpdateTensorboardTimeSeriesRequestOrBuilder
UploadModelOperationMetadataOrBuilder
UploadModelRequestOrBuilder
UploadModelResponseOrBuilder
UserActionReferenceOrBuilder
ValueOrBuilder
WorkerPoolSpecOrBuilder
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
DeployedModel.PredictionResourcesCase
Event.Type
Describes whether an Event's Artifact is the Execution's input or output.
Protobuf enum google.cloud.aiplatform.v1beta1.Event.Type
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.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
ImportDataConfig.SourceCase
ImportFeatureValuesRequest.FeatureTimeSourceCase
ImportFeatureValuesRequest.SourceCase
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
NearestNeighborSearchOperationMetadata.RecordError.RecordErrorType
Protobuf enum
google.cloud.aiplatform.v1beta1.NearestNeighborSearchOperationMetadata.RecordError.RecordErrorType
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
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
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