Namespace Google.Cloud.AIPlatform.V1 (2.2.0)

Classes

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.

AddContextArtifactsAndExecutionsRequest

Request message for [MetadataService.AddContextArtifactsAndExecutions][google.cloud.aiplatform.v1.MetadataService.AddContextArtifactsAndExecutions].

AddContextArtifactsAndExecutionsResponse

Response message for [MetadataService.AddContextArtifactsAndExecutions][google.cloud.aiplatform.v1.MetadataService.AddContextArtifactsAndExecutions].

AddContextChildrenRequest

Request message for [MetadataService.AddContextChildren][google.cloud.aiplatform.v1.MetadataService.AddContextChildren].

AddContextChildrenResponse

Response message for [MetadataService.AddContextChildren][google.cloud.aiplatform.v1.MetadataService.AddContextChildren].

AddExecutionEventsRequest

Request message for [MetadataService.AddExecutionEvents][google.cloud.aiplatform.v1.MetadataService.AddExecutionEvents].

AddExecutionEventsResponse

Response message for [MetadataService.AddExecutionEvents][google.cloud.aiplatform.v1.MetadataService.AddExecutionEvents].

AddTrialMeasurementRequest

Request message for [VizierService.AddTrialMeasurement][google.cloud.aiplatform.v1.VizierService.AddTrialMeasurement].

AnnotatedDatasetName

Resource name for the AnnotatedDataset resource.

Annotation

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

AnnotationName

Resource name for the Annotation resource.

AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

AnnotationSpecName

Resource name for the AnnotationSpec resource.

Artifact

Instance of a general artifact.

Artifact.Types

Container for nested types declared in the Artifact message type.

ArtifactName

Resource name for the Artifact resource.

Attribution

Attribution that explains a particular prediction output.

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.

AutoMLDatasetName

Resource name for the AutoMLDataset resource.

AutoMLModelName

Resource name for the AutoMLModel resource.

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.

AvroSource

The storage details for Avro input content.

BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

BatchCreateFeaturesRequest

Request message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeaturestoreService.BatchCreateFeatures].

BatchCreateFeaturesResponse

Response message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeaturestoreService.BatchCreateFeatures].

BatchCreateTensorboardRunsRequest

Request message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardRuns].

BatchCreateTensorboardRunsResponse

Response message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardRuns].

BatchCreateTensorboardTimeSeriesRequest

Request message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardTimeSeries].

BatchCreateTensorboardTimeSeriesResponse

Response message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardTimeSeries].

BatchDedicatedResources

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

BatchImportModelEvaluationSlicesRequest

Request message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.BatchImportModelEvaluationSlices]

BatchImportModelEvaluationSlicesResponse

Response message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.BatchImportModelEvaluationSlices]

BatchMigrateResourcesOperationMetadata

Runtime operation information for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1.MigrationService.BatchMigrateResources].

BatchMigrateResourcesOperationMetadata.Types

Container for nested types declared in the BatchMigrateResourcesOperationMetadata message type.

BatchMigrateResourcesOperationMetadata.Types.PartialResult

Represents a partial result in batch migration operation for one [MigrateResourceRequest][google.cloud.aiplatform.v1.MigrateResourceRequest].

BatchMigrateResourcesRequest

Request message for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1.MigrationService.BatchMigrateResources].

BatchMigrateResourcesResponse

Response message for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1.MigrationService.BatchMigrateResources].

BatchPredictionJob

A job that uses a [Model][google.cloud.aiplatform.v1.BatchPredictionJob.model] to produce predictions on multiple [input instances][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

BatchPredictionJob.Types

Container for nested types declared in the BatchPredictionJob message type.

BatchPredictionJob.Types.InputConfig

Configures the input to [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. See [Model.supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats] for Model's supported input formats, and how instances should be expressed via any of them.

BatchPredictionJob.Types.OutputConfig

Configures the output of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. See [Model.supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats] for supported output formats, and how predictions are expressed via any of them.

BatchPredictionJob.Types.OutputInfo

Further describes this job's output. Supplements [output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].

BatchPredictionJobName

Resource name for the BatchPredictionJob resource.

BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

BatchReadFeatureValuesRequest

Request message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].

BatchReadFeatureValuesRequest.Types

Container for nested types declared in the BatchReadFeatureValuesRequest message type.

BatchReadFeatureValuesRequest.Types.EntityTypeSpec

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

BatchReadFeatureValuesRequest.Types.PassThroughField

Describe pass-through fields in read_instance source.

BatchReadFeatureValuesResponse

Response message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].

BatchReadTensorboardTimeSeriesDataRequest

Request message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.BatchReadTensorboardTimeSeriesData].

BatchReadTensorboardTimeSeriesDataResponse

Response message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.BatchReadTensorboardTimeSeriesData].

BigQueryDestination

The BigQuery location for the output content.

BigQuerySource

The BigQuery location for the input content.

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

BoolArray

A list of boolean values.

CancelBatchPredictionJobRequest

Request message for [JobService.CancelBatchPredictionJob][google.cloud.aiplatform.v1.JobService.CancelBatchPredictionJob].

CancelCustomJobRequest

Request message for [JobService.CancelCustomJob][google.cloud.aiplatform.v1.JobService.CancelCustomJob].

CancelDataLabelingJobRequest

Request message for [JobService.CancelDataLabelingJob][google.cloud.aiplatform.v1.JobService.CancelDataLabelingJob].

CancelHyperparameterTuningJobRequest

Request message for [JobService.CancelHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.CancelHyperparameterTuningJob].

CancelPipelineJobRequest

Request message for [PipelineService.CancelPipelineJob][google.cloud.aiplatform.v1.PipelineService.CancelPipelineJob].

CancelTrainingPipelineRequest

Request message for [PipelineService.CancelTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.CancelTrainingPipeline].

CheckTrialEarlyStoppingStateMetatdata

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

CheckTrialEarlyStoppingStateRequest

Request message for [VizierService.CheckTrialEarlyStoppingState][google.cloud.aiplatform.v1.VizierService.CheckTrialEarlyStoppingState].

CheckTrialEarlyStoppingStateResponse

Response message for [VizierService.CheckTrialEarlyStoppingState][google.cloud.aiplatform.v1.VizierService.CheckTrialEarlyStoppingState].

CompleteTrialRequest

Request message for [VizierService.CompleteTrial][google.cloud.aiplatform.v1.VizierService.CompleteTrial].

CompletionStats

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

ContainerRegistryDestination

The Container Registry location for the container image.

ContainerSpec

The spec of a Container.

Context

Instance of a general context.

ContextName

Resource name for the Context resource.

CreateArtifactRequest

Request message for [MetadataService.CreateArtifact][google.cloud.aiplatform.v1.MetadataService.CreateArtifact].

CreateBatchPredictionJobRequest

Request message for [JobService.CreateBatchPredictionJob][google.cloud.aiplatform.v1.JobService.CreateBatchPredictionJob].

CreateContextRequest

Request message for [MetadataService.CreateContext][google.cloud.aiplatform.v1.MetadataService.CreateContext].

CreateCustomJobRequest

Request message for [JobService.CreateCustomJob][google.cloud.aiplatform.v1.JobService.CreateCustomJob].

CreateDataLabelingJobRequest

Request message for [JobService.CreateDataLabelingJob][google.cloud.aiplatform.v1.JobService.CreateDataLabelingJob].

CreateDatasetOperationMetadata

Runtime operation information for [DatasetService.CreateDataset][google.cloud.aiplatform.v1.DatasetService.CreateDataset].

CreateDatasetRequest

Request message for [DatasetService.CreateDataset][google.cloud.aiplatform.v1.DatasetService.CreateDataset].

CreateEndpointOperationMetadata

Runtime operation information for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1.EndpointService.CreateEndpoint].

CreateEndpointRequest

Request message for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1.EndpointService.CreateEndpoint].

CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

CreateEntityTypeRequest

Request message for [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.CreateEntityType].

CreateExecutionRequest

Request message for [MetadataService.CreateExecution][google.cloud.aiplatform.v1.MetadataService.CreateExecution].

CreateFeatureOperationMetadata

Details of operations that perform create Feature.

CreateFeatureRequest

Request message for [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeature].

CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

CreateFeaturestoreRequest

Request message for [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeaturestore].

CreateHyperparameterTuningJobRequest

Request message for [JobService.CreateHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.CreateHyperparameterTuningJob].

CreateIndexEndpointOperationMetadata

Runtime operation information for [IndexEndpointService.CreateIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.CreateIndexEndpoint].

CreateIndexEndpointRequest

Request message for [IndexEndpointService.CreateIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.CreateIndexEndpoint].

CreateIndexOperationMetadata

Runtime operation information for [IndexService.CreateIndex][google.cloud.aiplatform.v1.IndexService.CreateIndex].

CreateIndexRequest

Request message for [IndexService.CreateIndex][google.cloud.aiplatform.v1.IndexService.CreateIndex].

CreateMetadataSchemaRequest

Request message for [MetadataService.CreateMetadataSchema][google.cloud.aiplatform.v1.MetadataService.CreateMetadataSchema].

CreateMetadataStoreOperationMetadata

Details of operations that perform [MetadataService.CreateMetadataStore][google.cloud.aiplatform.v1.MetadataService.CreateMetadataStore].

CreateMetadataStoreRequest

Request message for [MetadataService.CreateMetadataStore][google.cloud.aiplatform.v1.MetadataService.CreateMetadataStore].

CreateModelDeploymentMonitoringJobRequest

Request message for [JobService.CreateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.CreateModelDeploymentMonitoringJob].

CreatePipelineJobRequest

Request message for [PipelineService.CreatePipelineJob][google.cloud.aiplatform.v1.PipelineService.CreatePipelineJob].

CreateSpecialistPoolOperationMetadata

Runtime operation information for [SpecialistPoolService.CreateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.CreateSpecialistPool].

CreateSpecialistPoolRequest

Request message for [SpecialistPoolService.CreateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.CreateSpecialistPool].

CreateStudyRequest

Request message for [VizierService.CreateStudy][google.cloud.aiplatform.v1.VizierService.CreateStudy].

CreateTensorboardExperimentRequest

Request message for [TensorboardService.CreateTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboardExperiment].

CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

CreateTensorboardRequest

Request message for [TensorboardService.CreateTensorboard][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboard].

CreateTensorboardRunRequest

Request message for [TensorboardService.CreateTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboardRun].

CreateTensorboardTimeSeriesRequest

Request message for [TensorboardService.CreateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboardTimeSeries].

CreateTrainingPipelineRequest

Request message for [PipelineService.CreateTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.CreateTrainingPipeline].

CreateTrialRequest

Request message for [VizierService.CreateTrial][google.cloud.aiplatform.v1.VizierService.CreateTrial].

CsvDestination

The storage details for CSV output content.

CsvSource

The storage details for CSV input content.

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).

CustomJobName

Resource name for the CustomJob resource.

CustomJobSpec

Represents the spec of a CustomJob. Next Id: 15

DataItem

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

DataItemName

Resource name for the DataItem resource.

DataLabelingDatasetName

Resource name for the DataLabelingDataset resource.

DataLabelingJob

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

DataLabelingJobName

Resource name for the DataLabelingJob resource.

Dataset

A collection of DataItems and Annotations on them.

DatasetName

Resource name for the Dataset resource.

DatasetService

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

DatasetService.DatasetServiceBase

Base class for server-side implementations of DatasetService

DatasetService.DatasetServiceClient

Client for DatasetService

DatasetServiceClient

DatasetService client wrapper, for convenient use.

DatasetServiceClientBuilder

Builder class for DatasetServiceClient to provide simple configuration of credentials, endpoint etc.

DatasetServiceClientImpl

DatasetService client wrapper implementation, for convenient use.

DatasetServiceSettings

Settings for DatasetServiceClient instances.

DedicatedResources

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

DeleteArtifactRequest

Request message for [MetadataService.DeleteArtifact][google.cloud.aiplatform.v1.MetadataService.DeleteArtifact].

DeleteBatchPredictionJobRequest

Request message for [JobService.DeleteBatchPredictionJob][google.cloud.aiplatform.v1.JobService.DeleteBatchPredictionJob].

DeleteContextRequest

Request message for [MetadataService.DeleteContext][google.cloud.aiplatform.v1.MetadataService.DeleteContext].

DeleteCustomJobRequest

Request message for [JobService.DeleteCustomJob][google.cloud.aiplatform.v1.JobService.DeleteCustomJob].

DeleteDataLabelingJobRequest

Request message for [JobService.DeleteDataLabelingJob][google.cloud.aiplatform.v1.JobService.DeleteDataLabelingJob].

DeleteDatasetRequest

Request message for [DatasetService.DeleteDataset][google.cloud.aiplatform.v1.DatasetService.DeleteDataset].

DeleteEndpointRequest

Request message for [EndpointService.DeleteEndpoint][google.cloud.aiplatform.v1.EndpointService.DeleteEndpoint].

DeleteEntityTypeRequest

Request message for [FeaturestoreService.DeleteEntityTypes][].

DeleteExecutionRequest

Request message for [MetadataService.DeleteExecution][google.cloud.aiplatform.v1.MetadataService.DeleteExecution].

DeleteFeatureRequest

Request message for [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeature].

DeleteFeaturestoreRequest

Request message for [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeaturestore].

DeleteHyperparameterTuningJobRequest

Request message for [JobService.DeleteHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.DeleteHyperparameterTuningJob].

DeleteIndexEndpointRequest

Request message for [IndexEndpointService.DeleteIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.DeleteIndexEndpoint].

DeleteIndexRequest

Request message for [IndexService.DeleteIndex][google.cloud.aiplatform.v1.IndexService.DeleteIndex].

DeleteMetadataStoreOperationMetadata

Details of operations that perform [MetadataService.DeleteMetadataStore][google.cloud.aiplatform.v1.MetadataService.DeleteMetadataStore].

DeleteMetadataStoreRequest

Request message for [MetadataService.DeleteMetadataStore][google.cloud.aiplatform.v1.MetadataService.DeleteMetadataStore].

DeleteModelDeploymentMonitoringJobRequest

Request message for [JobService.DeleteModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.DeleteModelDeploymentMonitoringJob].

DeleteModelRequest

Request message for [ModelService.DeleteModel][google.cloud.aiplatform.v1.ModelService.DeleteModel].

DeleteModelVersionRequest

Request message for [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1.ModelService.DeleteModelVersion].

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

DeletePipelineJobRequest

Request message for [PipelineService.DeletePipelineJob][google.cloud.aiplatform.v1.PipelineService.DeletePipelineJob].

DeleteSpecialistPoolRequest

Request message for [SpecialistPoolService.DeleteSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.DeleteSpecialistPool].

DeleteStudyRequest

Request message for [VizierService.DeleteStudy][google.cloud.aiplatform.v1.VizierService.DeleteStudy].

DeleteTensorboardExperimentRequest

Request message for [TensorboardService.DeleteTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboardExperiment].

DeleteTensorboardRequest

Request message for [TensorboardService.DeleteTensorboard][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboard].

DeleteTensorboardRunRequest

Request message for [TensorboardService.DeleteTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboardRun].

DeleteTensorboardTimeSeriesRequest

Request message for [TensorboardService.DeleteTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboardTimeSeries].

DeleteTrainingPipelineRequest

Request message for [PipelineService.DeleteTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.DeleteTrainingPipeline].

DeleteTrialRequest

Request message for [VizierService.DeleteTrial][google.cloud.aiplatform.v1.VizierService.DeleteTrial].

DeployedIndex

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

DeployedIndexAuthConfig

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

DeployedIndexAuthConfig.Types

Container for nested types declared in the DeployedIndexAuthConfig message type.

DeployedIndexAuthConfig.Types.AuthProvider

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

DeployedIndexRef

Points to a DeployedIndex.

DeployedModel

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

DeployedModelRef

Points to a DeployedModel.

DeployIndexOperationMetadata

Runtime operation information for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.DeployIndex].

DeployIndexRequest

Request message for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.DeployIndex].

DeployIndexResponse

Response message for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.DeployIndex].

DeployModelOperationMetadata

Runtime operation information for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

DeployModelRequest

Request message for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

DeployModelResponse

Response message for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

DestinationFeatureSetting

DiskSpec

Represents the spec of disk options.

DoubleArray

A list of double values.

EncryptionSpec

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

Endpoint

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

EndpointName

Resource name for the Endpoint resource.

EndpointService

A service for managing Vertex AI's Endpoints.

EndpointService.EndpointServiceBase

Base class for server-side implementations of EndpointService

EndpointService.EndpointServiceClient

Client for EndpointService

EndpointServiceClient

EndpointService client wrapper, for convenient use.

EndpointServiceClientBuilder

Builder class for EndpointServiceClient to provide simple configuration of credentials, endpoint etc.

EndpointServiceClientImpl

EndpointService client wrapper implementation, for convenient use.

EndpointServiceSettings

Settings for EndpointServiceClient instances.

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.

EntityTypeName

Resource name for the EntityType resource.

EnvVar

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

Event

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

Event.Types

Container for nested types declared in the Event message type.

ExamplesOverride

Overrides for example-based explanations.

ExamplesOverride.Types

Container for nested types declared in the ExamplesOverride message type.

ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

Execution

Instance of a general execution.

Execution.Types

Container for nested types declared in the Execution message type.

ExecutionName

Resource name for the Execution resource.

ExplainRequest

Request message for [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

ExplainResponse

Response message for [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

Explanation

Explanation of a prediction (provided in [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions]) produced by the Model on a given [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].

ExplanationMetadata

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

ExplanationMetadata.Types

Container for nested types declared in the ExplanationMetadata message type.

ExplanationMetadata.Types.InputMetadata

Metadata of the input of a feature.

Fields other than [InputMetadata.input_baselines][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.input_baselines] are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

ExplanationMetadata.Types.InputMetadata.Types

Container for nested types declared in the InputMetadata message type.

ExplanationMetadata.Types.InputMetadata.Types.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.

ExplanationMetadata.Types.InputMetadata.Types.Visualization

Visualization configurations for image explanation.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types

Container for nested types declared in the Visualization message type.

ExplanationMetadata.Types.OutputMetadata

Metadata of the prediction output to be explained.

ExplanationMetadataOverride

The [ExplanationMetadata][google.cloud.aiplatform.v1.ExplanationMetadata] entries that can be overridden at [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.

ExplanationMetadataOverride.Types

Container for nested types declared in the ExplanationMetadataOverride message type.

ExplanationMetadataOverride.Types.InputMetadataOverride

The [input metadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] entries to be overridden.

ExplanationParameters

Parameters to configure explaining for Model's predictions.

ExplanationSpec

Specification of Model explanation.

ExplanationSpecOverride

The [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] entries that can be overridden at [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.

ExportDataConfig

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

ExportDataOperationMetadata

Runtime operation information for [DatasetService.ExportData][google.cloud.aiplatform.v1.DatasetService.ExportData].

ExportDataRequest

Request message for [DatasetService.ExportData][google.cloud.aiplatform.v1.DatasetService.ExportData].

ExportDataResponse

Response message for [DatasetService.ExportData][google.cloud.aiplatform.v1.DatasetService.ExportData].

ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

ExportFeatureValuesRequest

Request message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].

ExportFeatureValuesRequest.Types

Container for nested types declared in the ExportFeatureValuesRequest message type.

ExportFeatureValuesRequest.Types.FullExport

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

ExportFeatureValuesRequest.Types.SnapshotExport

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

ExportFeatureValuesResponse

Response message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].

ExportModelOperationMetadata

Details of [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel] operation.

ExportModelOperationMetadata.Types

Container for nested types declared in the ExportModelOperationMetadata message type.

ExportModelOperationMetadata.Types.OutputInfo

Further describes the output of the ExportModel. Supplements [ExportModelRequest.OutputConfig][google.cloud.aiplatform.v1.ExportModelRequest.OutputConfig].

ExportModelRequest

Request message for [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel].

ExportModelRequest.Types

Container for nested types declared in the ExportModelRequest message type.

ExportModelRequest.Types.OutputConfig

Output configuration for the Model export.

ExportModelResponse

Response message of [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel] operation.

ExportTensorboardTimeSeriesDataRequest

Request message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ExportTensorboardTimeSeriesData].

ExportTensorboardTimeSeriesDataResponse

Response message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ExportTensorboardTimeSeriesData].

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.

Feature.Types

Container for nested types declared in the Feature message type.

Feature.Types.MonitoringStatsAnomaly

A list of historical [Snapshot Analysis][FeaturestoreMonitoringConfig.SnapshotAnalysis] or [Import Feature Analysis] [FeaturestoreMonitoringConfig.ImportFeatureAnalysis] stats requested by user, sorted by [FeatureStatsAnomaly.start_time][google.cloud.aiplatform.v1.FeatureStatsAnomaly.start_time] descending.

Feature.Types.MonitoringStatsAnomaly.Types

Container for nested types declared in the MonitoringStatsAnomaly message type.

FeatureName

Resource name for the Feature resource.

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.

FeatureNoiseSigma.Types

Container for nested types declared in the FeatureNoiseSigma message type.

FeatureNoiseSigma.Types.NoiseSigmaForFeature

Noise sigma for a single feature.

FeatureSelector

Selector for Features of an EntityType.

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.

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.

Featurestore.Types

Container for nested types declared in the Featurestore message type.

Featurestore.Types.OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Featurestore.Types.OnlineServingConfig.Types

Container for nested types declared in the OnlineServingConfig message type.

Featurestore.Types.OnlineServingConfig.Types.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).

FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

FeaturestoreMonitoringConfig.Types

Container for nested types declared in the FeaturestoreMonitoringConfig message type.

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis

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

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis.Types

Container for nested types declared in the ImportFeaturesAnalysis message type.

FeaturestoreMonitoringConfig.Types.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.

FeaturestoreMonitoringConfig.Types.ThresholdConfig

The config for Featurestore Monitoring threshold.

FeaturestoreName

Resource name for the Featurestore resource.

FeaturestoreOnlineServingService

A service for serving online feature values.

FeaturestoreOnlineServingService.FeaturestoreOnlineServingServiceBase

Base class for server-side implementations of FeaturestoreOnlineServingService

FeaturestoreOnlineServingService.FeaturestoreOnlineServingServiceClient

Client for FeaturestoreOnlineServingService

FeaturestoreOnlineServingServiceClient

FeaturestoreOnlineServingService client wrapper, for convenient use.

FeaturestoreOnlineServingServiceClient.StreamingReadFeatureValuesStream

Server streaming methods for StreamingReadFeatureValues(StreamingReadFeatureValuesRequest, CallSettings).

FeaturestoreOnlineServingServiceClientBuilder

Builder class for FeaturestoreOnlineServingServiceClient to provide simple configuration of credentials, endpoint etc.

FeaturestoreOnlineServingServiceClientImpl

FeaturestoreOnlineServingService client wrapper implementation, for convenient use.

FeaturestoreOnlineServingServiceSettings

Settings for FeaturestoreOnlineServingServiceClient instances.

FeaturestoreService

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

FeaturestoreService.FeaturestoreServiceBase

Base class for server-side implementations of FeaturestoreService

FeaturestoreService.FeaturestoreServiceClient

Client for FeaturestoreService

FeaturestoreServiceClient

FeaturestoreService client wrapper, for convenient use.

FeaturestoreServiceClientBuilder

Builder class for FeaturestoreServiceClient to provide simple configuration of credentials, endpoint etc.

FeaturestoreServiceClientImpl

FeaturestoreService client wrapper implementation, for convenient use.

FeaturestoreServiceSettings

Settings for FeaturestoreServiceClient instances.

FeatureValue

Value for a feature.

FeatureValue.Types

Container for nested types declared in the FeatureValue message type.

FeatureValue.Types.Metadata

Metadata of feature value.

FeatureValueDestination

A destination location for Feature values and format.

FeatureValueList

Container for list of values.

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.

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.

GcsDestination

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

GcsSource

The Google Cloud Storage location for the input content.

GenericOperationMetadata

Generic Metadata shared by all operations.

GetAnnotationSpecRequest

Request message for [DatasetService.GetAnnotationSpec][google.cloud.aiplatform.v1.DatasetService.GetAnnotationSpec].

GetArtifactRequest

Request message for [MetadataService.GetArtifact][google.cloud.aiplatform.v1.MetadataService.GetArtifact].

GetBatchPredictionJobRequest

Request message for [JobService.GetBatchPredictionJob][google.cloud.aiplatform.v1.JobService.GetBatchPredictionJob].

GetContextRequest

Request message for [MetadataService.GetContext][google.cloud.aiplatform.v1.MetadataService.GetContext].

GetCustomJobRequest

Request message for [JobService.GetCustomJob][google.cloud.aiplatform.v1.JobService.GetCustomJob].

GetDataLabelingJobRequest

Request message for [JobService.GetDataLabelingJob][google.cloud.aiplatform.v1.JobService.GetDataLabelingJob].

GetDatasetRequest

Request message for [DatasetService.GetDataset][google.cloud.aiplatform.v1.DatasetService.GetDataset].

GetEndpointRequest

Request message for [EndpointService.GetEndpoint][google.cloud.aiplatform.v1.EndpointService.GetEndpoint]

GetEntityTypeRequest

Request message for [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1.FeaturestoreService.GetEntityType].

GetExecutionRequest

Request message for [MetadataService.GetExecution][google.cloud.aiplatform.v1.MetadataService.GetExecution].

GetFeatureRequest

Request message for [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1.FeaturestoreService.GetFeature].

GetFeaturestoreRequest

Request message for [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.GetFeaturestore].

GetHyperparameterTuningJobRequest

Request message for [JobService.GetHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.GetHyperparameterTuningJob].

GetIndexEndpointRequest

Request message for [IndexEndpointService.GetIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.GetIndexEndpoint]

GetIndexRequest

Request message for [IndexService.GetIndex][google.cloud.aiplatform.v1.IndexService.GetIndex]

GetMetadataSchemaRequest

Request message for [MetadataService.GetMetadataSchema][google.cloud.aiplatform.v1.MetadataService.GetMetadataSchema].

GetMetadataStoreRequest

Request message for [MetadataService.GetMetadataStore][google.cloud.aiplatform.v1.MetadataService.GetMetadataStore].

GetModelDeploymentMonitoringJobRequest

Request message for [JobService.GetModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.GetModelDeploymentMonitoringJob].

GetModelEvaluationRequest

Request message for [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1.ModelService.GetModelEvaluation].

GetModelEvaluationSliceRequest

Request message for [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1.ModelService.GetModelEvaluationSlice].

GetModelRequest

Request message for [ModelService.GetModel][google.cloud.aiplatform.v1.ModelService.GetModel].

GetPipelineJobRequest

Request message for [PipelineService.GetPipelineJob][google.cloud.aiplatform.v1.PipelineService.GetPipelineJob].

GetSpecialistPoolRequest

Request message for [SpecialistPoolService.GetSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.GetSpecialistPool].

GetStudyRequest

Request message for [VizierService.GetStudy][google.cloud.aiplatform.v1.VizierService.GetStudy].

GetTensorboardExperimentRequest

Request message for [TensorboardService.GetTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.GetTensorboardExperiment].

GetTensorboardRequest

Request message for [TensorboardService.GetTensorboard][google.cloud.aiplatform.v1.TensorboardService.GetTensorboard].

GetTensorboardRunRequest

Request message for [TensorboardService.GetTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.GetTensorboardRun].

GetTensorboardTimeSeriesRequest

Request message for [TensorboardService.GetTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.GetTensorboardTimeSeries].

GetTrainingPipelineRequest

Request message for [PipelineService.GetTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.GetTrainingPipeline].

GetTrialRequest

Request message for [VizierService.GetTrial][google.cloud.aiplatform.v1.VizierService.GetTrial].

HyperparameterTuningJob

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

HyperparameterTuningJobName

Resource name for the HyperparameterTuningJob resource.

IdMatcher

Matcher for Features of an EntityType by Feature ID.

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.

ImportDataOperationMetadata

Runtime operation information for [DatasetService.ImportData][google.cloud.aiplatform.v1.DatasetService.ImportData].

ImportDataRequest

Request message for [DatasetService.ImportData][google.cloud.aiplatform.v1.DatasetService.ImportData].

ImportDataResponse

Response message for [DatasetService.ImportData][google.cloud.aiplatform.v1.DatasetService.ImportData].

ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

ImportFeatureValuesRequest

Request message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].

ImportFeatureValuesRequest.Types

Container for nested types declared in the ImportFeatureValuesRequest message type.

ImportFeatureValuesRequest.Types.FeatureSpec

Defines the Feature value(s) to import.

ImportFeatureValuesResponse

Response message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].

ImportModelEvaluationRequest

Request message for [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1.ModelService.ImportModelEvaluation]

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.

IndexEndpoint

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

IndexEndpointName

Resource name for the IndexEndpoint resource.

IndexEndpointService

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointService.IndexEndpointServiceBase

Base class for server-side implementations of IndexEndpointService

IndexEndpointService.IndexEndpointServiceClient

Client for IndexEndpointService

IndexEndpointServiceClient

IndexEndpointService client wrapper, for convenient use.

IndexEndpointServiceClientBuilder

Builder class for IndexEndpointServiceClient to provide simple configuration of credentials, endpoint etc.

IndexEndpointServiceClientImpl

IndexEndpointService client wrapper implementation, for convenient use.

IndexEndpointServiceSettings

Settings for IndexEndpointServiceClient instances.

IndexName

Resource name for the Index resource.

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.