Cloud AI Platform v1 API - Namespace Google.Cloud.AIPlatform.V1 (2.10.0)

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.InstanceConfig

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

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].

CancelNasJobRequest

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

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.

CopyModelOperationMetadata

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

CopyModelRequest

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

CopyModelResponse

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

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].

CreateNasJobRequest

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

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.

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.

DataItemView

A container for a single DataItem and Annotations on it.

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].

DeleteNasJobRequest

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

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].

GetNasJobRequest

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

GetNasTrialDetailRequest

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

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.

Index.Types

Container for nested types declared in the Index message type.

IndexDatapoint

A datapoint of Index.

IndexDatapoint.Types

Container for nested types declared in the IndexDatapoint message type.

IndexDatapoint.Types.CrowdingTag

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

IndexDatapoint.Types.Restriction

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

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.

IndexService

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

IndexService.IndexServiceBase

Base class for server-side implementations of IndexService

IndexService.IndexServiceClient

Client for IndexService

IndexServiceClient

IndexService client wrapper, for convenient use.

IndexServiceClientBuilder

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

IndexServiceClientImpl

IndexService client wrapper implementation, for convenient use.

IndexServiceSettings

Settings for IndexServiceClient instances.

IndexStats

Stats of the Index.

InputDataConfig

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

Int64Array

A list of int64 values.

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

JobService

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

JobService.JobServiceBase

Base class for server-side implementations of JobService

JobService.JobServiceClient

Client for JobService

JobServiceClient

JobService client wrapper, for convenient use.

JobServiceClientBuilder

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

JobServiceClientImpl

JobService client wrapper implementation, for convenient use.

JobServiceSettings

Settings for JobServiceClient instances.

LineageSubgraph

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

ListAnnotationsRequest

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

ListAnnotationsResponse

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

ListArtifactsRequest

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

ListArtifactsResponse

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

ListBatchPredictionJobsRequest

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

ListBatchPredictionJobsResponse

Response message for [JobService.ListBatchPredictionJobs][google.cloud.aiplatform.v1.JobService.ListBatchPredictionJobs]

ListContextsRequest

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

ListContextsResponse

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

ListCustomJobsRequest

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

ListCustomJobsResponse

Response message for [JobService.ListCustomJobs][google.cloud.aiplatform.v1.JobService.ListCustomJobs]

ListDataItemsRequest

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

ListDataItemsResponse

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

ListDataLabelingJobsRequest

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

ListDataLabelingJobsResponse

Response message for [JobService.ListDataLabelingJobs][google.cloud.aiplatform.v1.JobService.ListDataLabelingJobs].

ListDatasetsRequest

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

ListDatasetsResponse

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

ListEndpointsRequest

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

ListEndpointsResponse

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

ListEntityTypesRequest

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

ListEntityTypesResponse

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

ListExecutionsRequest

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

ListExecutionsResponse

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

ListFeaturesRequest

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

ListFeaturesResponse

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

ListFeaturestoresRequest

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

ListFeaturestoresResponse

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

ListHyperparameterTuningJobsRequest

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

ListHyperparameterTuningJobsResponse

Response message for [JobService.ListHyperparameterTuningJobs][google.cloud.aiplatform.v1.JobService.ListHyperparameterTuningJobs]

ListIndexEndpointsRequest

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

ListIndexEndpointsResponse

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

ListIndexesRequest

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

ListIndexesResponse

Response message for [IndexService.ListIndexes][google.cloud.aiplatform.v1.IndexService.ListIndexes].

ListMetadataSchemasRequest

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

ListMetadataSchemasResponse

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

ListMetadataStoresRequest

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

ListMetadataStoresResponse

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

ListModelDeploymentMonitoringJobsRequest

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

ListModelDeploymentMonitoringJobsResponse

Response message for [JobService.ListModelDeploymentMonitoringJobs][google.cloud.aiplatform.v1.JobService.ListModelDeploymentMonitoringJobs].

ListModelEvaluationSlicesRequest

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

ListModelEvaluationSlicesResponse

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

ListModelEvaluationsRequest

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

ListModelEvaluationsResponse

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

ListModelsRequest

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

ListModelsResponse

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

ListModelVersionsRequest

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

ListModelVersionsResponse

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

ListNasJobsRequest

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

ListNasJobsResponse

Response message for [JobService.ListNasJobs][google.cloud.aiplatform.v1.JobService.ListNasJobs]

ListNasTrialDetailsRequest

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

ListNasTrialDetailsResponse

Response message for [JobService.ListNasTrialDetails][google.cloud.aiplatform.v1.JobService.ListNasTrialDetails]

ListOptimalTrialsRequest

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

ListOptimalTrialsResponse

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

ListPipelineJobsRequest

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

ListPipelineJobsResponse

Response message for [PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1.PipelineService.ListPipelineJobs]

ListSavedQueriesRequest

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

ListSavedQueriesResponse

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

ListSpecialistPoolsRequest

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

ListSpecialistPoolsResponse

Response message for [SpecialistPoolService.ListSpecialistPools][google.cloud.aiplatform.v1.SpecialistPoolService.ListSpecialistPools].

ListStudiesRequest

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

ListStudiesResponse

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

ListTensorboardExperimentsRequest

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

ListTensorboardExperimentsResponse

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

ListTensorboardRunsRequest

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

ListTensorboardRunsResponse

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

ListTensorboardsRequest

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

ListTensorboardsResponse

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

ListTensorboardTimeSeriesRequest

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

ListTensorboardTimeSeriesResponse

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

ListTrainingPipelinesRequest

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

ListTrainingPipelinesResponse

Response message for [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines]

ListTrialsRequest

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

ListTrialsResponse

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

LookupStudyRequest

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

MachineSpec

Specification of a single machine.

ManualBatchTuningParameters

Manual batch tuning parameters.

Measurement

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

Measurement.Types

Container for nested types declared in the Measurement message type.

Measurement.Types.Metric

A message representing a metric in the measurement.

MergeVersionAliasesRequest

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

MetadataSchema

Instance of a general MetadataSchema.

MetadataSchema.Types

Container for nested types declared in the MetadataSchema message type.

MetadataSchemaName

Resource name for the MetadataSchema resource.

MetadataService

Service for reading and writing metadata entries.

MetadataService.MetadataServiceBase

Base class for server-side implementations of MetadataService

MetadataService.MetadataServiceClient

Client for MetadataService

MetadataServiceClient

MetadataService client wrapper, for convenient use.

MetadataServiceClientBuilder

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

MetadataServiceClientImpl

MetadataService client wrapper implementation, for convenient use.

MetadataServiceSettings

Settings for MetadataServiceClient instances.

MetadataStore

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

MetadataStore.Types

Container for nested types declared in the MetadataStore message type.

MetadataStore.Types.MetadataStoreState

Represents state information for a MetadataStore.

MetadataStoreName

Resource name for the MetadataStore resource.

MigratableResource

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

MigratableResource.Types

Container for nested types declared in the MigratableResource message type.

MigratableResource.Types.AutomlDataset

Represents one Dataset in automl.googleapis.com.

MigratableResource.Types.AutomlModel

Represents one Model in automl.googleapis.com.

MigratableResource.Types.DataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

MigratableResource.Types.DataLabelingDataset.Types

Container for nested types declared in the DataLabelingDataset message type.

MigratableResource.Types.DataLabelingDataset.Types.DataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

MigratableResource.Types.MlEngineModelVersion

Represents one model Version in ml.googleapis.com.

MigrateResourceRequest

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

MigrateResourceRequest.Types

Container for nested types declared in the MigrateResourceRequest message type.

MigrateResourceRequest.Types.MigrateAutomlDatasetConfig

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

MigrateResourceRequest.Types.MigrateAutomlModelConfig

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

MigrateResourceRequest.Types.MigrateDataLabelingDatasetConfig

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

MigrateResourceRequest.Types.MigrateDataLabelingDatasetConfig.Types

Container for nested types declared in the MigrateDataLabelingDatasetConfig message type.

MigrateResourceRequest.Types.MigrateDataLabelingDatasetConfig.Types.MigrateDataLabelingAnnotatedDatasetConfig

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

MigrateResourceRequest.Types.MigrateMlEngineModelVersionConfig

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

MigrateResourceResponse

Describes a successfully migrated resource.

MigrationService

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

MigrationService.MigrationServiceBase

Base class for server-side implementations of MigrationService

MigrationService.MigrationServiceClient

Client for MigrationService

MigrationServiceClient

MigrationService client wrapper, for convenient use.

MigrationServiceClientBuilder

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

MigrationServiceClientImpl

MigrationService client wrapper implementation, for convenient use.

MigrationServiceSettings

Settings for MigrationServiceClient instances.

Model

A trained machine learning Model.

Model.Types

Container for nested types declared in the Model message type.

Model.Types.ExportFormat

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

Model.Types.ExportFormat.Types

Container for nested types declared in the ExportFormat message type.

Model.Types.OriginalModelInfo

Contains information about the original Model if this Model is a copy.

ModelContainerSpec

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

ModelDeploymentMonitoringBigQueryTable

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

ModelDeploymentMonitoringBigQueryTable.Types

Container for nested types declared in the ModelDeploymentMonitoringBigQueryTable message type.

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.

ModelDeploymentMonitoringJob.Types

Container for nested types declared in the ModelDeploymentMonitoringJob message type.

ModelDeploymentMonitoringJob.Types.LatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines.

ModelDeploymentMonitoringJobName

Resource name for the ModelDeploymentMonitoringJob resource.

ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job.

ModelEvaluation

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

ModelEvaluation.Types

Container for nested types declared in the ModelEvaluation message type.

ModelEvaluation.Types.ModelEvaluationExplanationSpec

ModelEvaluationName

Resource name for the ModelEvaluation resource.

ModelEvaluationSlice

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

ModelEvaluationSlice.Types

Container for nested types declared in the ModelEvaluationSlice message type.

ModelEvaluationSlice.Types.Slice

Definition of a slice.

ModelEvaluationSliceName

Resource name for the ModelEvaluationSlice resource.

ModelExplanation

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

ModelMonitoringAlertConfig

ModelMonitoringAlertConfig.Types

Container for nested types declared in the ModelMonitoringAlertConfig message type.

ModelMonitoringAlertConfig.Types.EmailAlertConfig

The config for email alert.

ModelMonitoringObjectiveConfig

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

ModelMonitoringObjectiveConfig.Types

Container for nested types declared in the ModelMonitoringObjectiveConfig message type.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig

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

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types

Container for nested types declared in the ExplanationConfig message type.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline

Output from [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob] for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline.Types

Container for nested types declared in the ExplanationBaseline message type.

ModelMonitoringObjectiveConfig.Types.PredictionDriftDetectionConfig

The config for Prediction data drift detection.

ModelMonitoringObjectiveConfig.Types.TrainingDataset

Training Dataset information.

ModelMonitoringObjectiveConfig.Types.TrainingPredictionSkewDetectionConfig

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

ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

ModelMonitoringStatsAnomalies.Types

Container for nested types declared in the ModelMonitoringStatsAnomalies message type.

ModelMonitoringStatsAnomalies.Types.FeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific Feature.

ModelName

Resource name for the Model resource.

ModelService

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

ModelService.ModelServiceBase

Base class for server-side implementations of ModelService

ModelService.ModelServiceClient

Client for ModelService

ModelServiceClient

ModelService client wrapper, for convenient use.

ModelServiceClientBuilder

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

ModelServiceClientImpl

ModelService client wrapper implementation, for convenient use.

ModelServiceSettings

Settings for ModelServiceClient instances.

ModelSourceInfo

Detail description of the source information of the model.

ModelSourceInfo.Types

Container for nested types declared in the ModelSourceInfo message type.

MutateDeployedIndexOperationMetadata

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

MutateDeployedIndexRequest

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

MutateDeployedIndexResponse

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

NasJob

Represents a Neural Architecture Search (NAS) job.

NasJobName

Resource name for the NasJob resource.

NasJobOutput

Represents a uCAIP NasJob output.

NasJobOutput.Types

Container for nested types declared in the NasJobOutput message type.

NasJobOutput.Types.MultiTrialJobOutput

The output of a multi-trial Neural Architecture Search (NAS) jobs.

NasJobSpec

Represents the spec of a NasJob.

NasJobSpec.Types

Container for nested types declared in the NasJobSpec message type.

NasJobSpec.Types.MultiTrialAlgorithmSpec

The spec of multi-trial Neural Architecture Search (NAS).

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types

Container for nested types declared in the MultiTrialAlgorithmSpec message type.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MetricSpec

Represents a metric to optimize.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MetricSpec.Types

Container for nested types declared in the MetricSpec message type.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.SearchTrialSpec

Represent spec for search trials.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.TrainTrialSpec

Represent spec for train trials.

NasTrial

Represents a uCAIP NasJob trial.

NasTrial.Types

Container for nested types declared in the NasTrial message type.

NasTrialDetail

Represents a NasTrial details along with it's parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned.

NasTrialDetailName

Resource name for the NasTrialDetail resource.

NearestNeighborSearchOperationMetadata

Runtime operation metadata with regard to Matching Engine Index.

NearestNeighborSearchOperationMetadata.Types

Container for nested types declared in the NearestNeighborSearchOperationMetadata message type.

NearestNeighborSearchOperationMetadata.Types.ContentValidationStats

NearestNeighborSearchOperationMetadata.Types.RecordError

NearestNeighborSearchOperationMetadata.Types.RecordError.Types

Container for nested types declared in the RecordError message type.

Neighbor

Neighbors for example-based explanations.

NetworkName

Resource name for the Network resource.

NfsMount

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

PauseModelDeploymentMonitoringJobRequest

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

PipelineJob

An instance of a machine learning PipelineJob.

PipelineJob.Types

Container for nested types declared in the PipelineJob message type.

PipelineJob.Types.RuntimeConfig

The runtime config of a PipelineJob.

PipelineJob.Types.RuntimeConfig.Types

Container for nested types declared in the RuntimeConfig message type.

PipelineJob.Types.RuntimeConfig.Types.InputArtifact

The type of an input artifact.

PipelineJobDetail

The runtime detail of PipelineJob.

PipelineJobName

Resource name for the PipelineJob resource.

PipelineService

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).

PipelineService.PipelineServiceBase

Base class for server-side implementations of PipelineService

PipelineService.PipelineServiceClient

Client for PipelineService

PipelineServiceClient

PipelineService client wrapper, for convenient use.

PipelineServiceClientBuilder

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

PipelineServiceClientImpl

PipelineService client wrapper implementation, for convenient use.

PipelineServiceSettings

Settings for PipelineServiceClient instances.

PipelineTaskDetail

The runtime detail of a task execution.

PipelineTaskDetail.Types

Container for nested types declared in the PipelineTaskDetail message type.

PipelineTaskDetail.Types.ArtifactList

A list of artifact metadata.

PipelineTaskDetail.Types.PipelineTaskStatus

A single record of the task status.

PipelineTaskExecutorDetail

The runtime detail of a pipeline executor.

PipelineTaskExecutorDetail.Types

Container for nested types declared in the PipelineTaskExecutorDetail message type.

PipelineTaskExecutorDetail.Types.ContainerDetail

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

PipelineTaskExecutorDetail.Types.CustomJobDetail

The detailed info for a custom job executor.

PipelineTemplateMetadata

Pipeline template metadata if [PipelineJob.template_uri][google.cloud.aiplatform.v1.PipelineJob.template_uri] is from supported template registry. Currently, the only supported registry is Artifact Registry.

Port

Represents a network port in a container.

PredefinedSplit

Assigns input data to training, validation, and test sets based on the value of a provided key.

Supported only for tabular Datasets.

PredictionService

A service for online predictions and explanations.

PredictionService.PredictionServiceBase

Base class for server-side implementations of PredictionService

PredictionService.PredictionServiceClient

Client for PredictionService

PredictionServiceClient

PredictionService client wrapper, for convenient use.

PredictionServiceClientBuilder

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

PredictionServiceClientImpl

PredictionService client wrapper implementation, for convenient use.

PredictionServiceSettings

Settings for PredictionServiceClient instances.

PredictRequest

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

PredictRequestResponseLoggingConfig

Configuration for logging request-response to a BigQuery table.

PredictResponse

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

PredictSchemata

Contains the schemata used in Model's predictions and explanations via [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict], [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain] and [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

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.

PrivateServiceConnectConfig

Represents configuration for private service connect.

PurgeArtifactsMetadata

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

PurgeArtifactsRequest

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

PurgeArtifactsResponse

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

PurgeContextsMetadata

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

PurgeContextsRequest

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

PurgeContextsResponse

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

PurgeExecutionsMetadata

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

PurgeExecutionsRequest

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

PurgeExecutionsResponse

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

PythonPackageSpec

The spec of a Python packaged code.

QueryArtifactLineageSubgraphRequest

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

QueryContextLineageSubgraphRequest

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

QueryExecutionInputsAndOutputsRequest

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

RawPredictRequest

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

ReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.ReadFeatureValues].

ReadFeatureValuesResponse

Response message for [FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.ReadFeatureValues].

ReadFeatureValuesResponse.Types

Container for nested types declared in the ReadFeatureValuesResponse message type.

ReadFeatureValuesResponse.Types.EntityView

Entity view with Feature values.

ReadFeatureValuesResponse.Types.EntityView.Types

Container for nested types declared in the EntityView message type.

ReadFeatureValuesResponse.Types.EntityView.Types.Data

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

ReadFeatureValuesResponse.Types.FeatureDescriptor

Metadata for requested Features.

Response header with metadata for the requested [ReadFeatureValuesRequest.entity_type][google.cloud.aiplatform.v1.ReadFeatureValuesRequest.entity_type] and Features.

ReadTensorboardBlobDataRequest

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

ReadTensorboardBlobDataResponse

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

ReadTensorboardTimeSeriesDataRequest

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

ReadTensorboardTimeSeriesDataResponse

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

ReadTensorboardUsageRequest

Request message for [TensorboardService.GetTensorboardUsage][].

ReadTensorboardUsageResponse

Response message for [TensorboardService.GetTensorboardUsage][].

ReadTensorboardUsageResponse.Types

Container for nested types declared in the ReadTensorboardUsageResponse message type.

ReadTensorboardUsageResponse.Types.PerMonthUsageData

Per month usage data

ReadTensorboardUsageResponse.Types.PerUserUsageData

Per user usage data.

RemoveContextChildrenRequest

Request message for [MetadataService.DeleteContextChildrenRequest][].

RemoveContextChildrenResponse

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

RemoveDatapointsRequest

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

RemoveDatapointsResponse

Response message for [IndexService.RemoveDatapoints][google.cloud.aiplatform.v1.IndexService.RemoveDatapoints]

ResourcesConsumed

Statistics information about resource consumption.

ResumeModelDeploymentMonitoringJobRequest

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

SampleConfig

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

SampleConfig.Types

Container for nested types declared in the SampleConfig message type.

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.

SamplingStrategy

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

SamplingStrategy.Types

Container for nested types declared in the SamplingStrategy message type.

SamplingStrategy.Types.RandomSampleConfig

Requests are randomly selected.

SavedQuery

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

SavedQueryName

Resource name for the SavedQuery resource.

Scalar

One point viewable on a scalar metric plot.

Scheduling

All parameters related to queuing and scheduling of custom jobs.

SearchDataItemsRequest

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

SearchDataItemsRequest.Types

Container for nested types declared in the SearchDataItemsRequest message type.

SearchDataItemsRequest.Types.OrderByAnnotation

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

SearchDataItemsResponse

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

SearchFeaturesRequest

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

SearchFeaturesResponse

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

SearchMigratableResourcesRequest

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

SearchMigratableResourcesResponse

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

SearchModelDeploymentMonitoringStatsAnomaliesRequest

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

SearchModelDeploymentMonitoringStatsAnomaliesRequest.Types

Container for nested types declared in the SearchModelDeploymentMonitoringStatsAnomaliesRequest message type.

SearchModelDeploymentMonitoringStatsAnomaliesRequest.Types.StatsAnomaliesObjective

Stats requested for specific objective.

SearchModelDeploymentMonitoringStatsAnomaliesResponse

Response message for [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1.JobService.SearchModelDeploymentMonitoringStatsAnomalies].

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

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.

SpecialistPoolName

Resource name for the SpecialistPool resource.

SpecialistPoolService

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.

SpecialistPoolService.SpecialistPoolServiceBase

Base class for server-side implementations of SpecialistPoolService

SpecialistPoolService.SpecialistPoolServiceClient

Client for SpecialistPoolService

SpecialistPoolServiceClient

SpecialistPoolService client wrapper, for convenient use.

SpecialistPoolServiceClientBuilder

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

SpecialistPoolServiceClientImpl

SpecialistPoolService client wrapper implementation, for convenient use.

SpecialistPoolServiceSettings

Settings for SpecialistPoolServiceClient instances.

StopTrialRequest

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

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.

StreamingReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

StringArray

A list of string values.

Study

A message representing a Study.

Study.Types

Container for nested types declared in the Study message type.

StudyName

Resource name for the Study resource.

StudySpec

Represents specification of a Study.

StudySpec.Types

Container for nested types declared in the StudySpec message type.

StudySpec.Types.ConvexAutomatedStoppingSpec

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

StudySpec.Types.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.

StudySpec.Types.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.

StudySpec.Types.MetricSpec

Represents a metric to optimize.

StudySpec.Types.MetricSpec.Types

Container for nested types declared in the MetricSpec message type.

StudySpec.Types.MetricSpec.Types.SafetyMetricConfig

Used in safe optimization to specify threshold levels and risk tolerance.

StudySpec.Types.ParameterSpec

Represents a single parameter to optimize.

StudySpec.Types.ParameterSpec.Types

Container for nested types declared in the ParameterSpec message type.

StudySpec.Types.ParameterSpec.Types.CategoricalValueSpec

Value specification for a parameter in CATEGORICAL type.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec

Represents a parameter spec with condition from its parent parameter.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types

Container for nested types declared in the ConditionalParameterSpec message type.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types.CategoricalValueCondition

Represents the spec to match categorical values from parent parameter.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types.DiscreteValueCondition

Represents the spec to match discrete values from parent parameter.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types.IntValueCondition

Represents the spec to match integer values from parent parameter.

StudySpec.Types.ParameterSpec.Types.DiscreteValueSpec

Value specification for a parameter in DISCRETE type.

StudySpec.Types.ParameterSpec.Types.DoubleValueSpec

Value specification for a parameter in DOUBLE type.

StudySpec.Types.ParameterSpec.Types.IntegerValueSpec

Value specification for a parameter in INTEGER type.

SuggestTrialsMetadata

Details of operations that perform Trials suggestion.

SuggestTrialsRequest

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

SuggestTrialsResponse

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

Tensorboard

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

TensorboardBlob

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

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.

TensorboardExperiment

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

TensorboardExperimentName

Resource name for the TensorboardExperiment resource.

TensorboardName

Resource name for the Tensorboard resource.

TensorboardRun

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

TensorboardRunName

Resource name for the TensorboardRun resource.

TensorboardService

TensorboardService.TensorboardServiceBase

Base class for server-side implementations of TensorboardService

TensorboardService.TensorboardServiceClient

Client for TensorboardService

TensorboardServiceClient

TensorboardService client wrapper, for convenient use.

TensorboardServiceClient.ReadTensorboardBlobDataStream

Server streaming methods for ReadTensorboardBlobData(ReadTensorboardBlobDataRequest, CallSettings).

TensorboardServiceClientBuilder

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

TensorboardServiceClientImpl

TensorboardService client wrapper implementation, for convenient use.

TensorboardServiceSettings

Settings for TensorboardServiceClient instances.

TensorboardTensor

One point viewable on a tensor metric plot.

TensorboardTimeSeries

TensorboardTimeSeries maps to times series produced in training runs

TensorboardTimeSeries.Types

Container for nested types declared in the TensorboardTimeSeries message type.

TensorboardTimeSeries.Types.Metadata

Describes metadata for a TensorboardTimeSeries.

TensorboardTimeSeriesName

Resource name for the TensorboardTimeSeries resource.

TFRecordDestination

The storage details for TFRecord output content.

ThresholdConfig

The config for feature monitoring threshold.

TimeSeriesData

All the data stored in a TensorboardTimeSeries.

TimeSeriesDataPoint

A TensorboardTimeSeries data point.

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.

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.

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][google.cloud.aiplatform.v1.ModelService.UploadModel] the Model to Vertex AI, and evaluate the Model.

TrainingPipelineName

Resource name for the TrainingPipeline resource.

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.

Trial.Types

Container for nested types declared in the Trial message type.

Trial.Types.Parameter

A message representing a parameter to be tuned.

TrialName

Resource name for the Trial resource.

UndeployIndexOperationMetadata

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

UndeployIndexRequest

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

UndeployIndexResponse

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

UndeployModelOperationMetadata

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

UndeployModelRequest

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

UndeployModelResponse

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

UnmanagedContainerModel

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

UpdateArtifactRequest

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

UpdateContextRequest

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

UpdateDatasetRequest

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

UpdateEndpointRequest

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

UpdateEntityTypeRequest

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

UpdateExecutionRequest

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

UpdateFeatureRequest

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

UpdateFeaturestoreOperationMetadata

Details of operations that perform update Featurestore.

UpdateFeaturestoreRequest

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

UpdateIndexEndpointRequest

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

UpdateIndexOperationMetadata

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

UpdateIndexRequest

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

UpdateModelDeploymentMonitoringJobOperationMetadata

Runtime operation information for [JobService.UpdateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.UpdateModelDeploymentMonitoringJob].

UpdateModelDeploymentMonitoringJobRequest

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

UpdateModelRequest

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

UpdateSpecialistPoolOperationMetadata

Runtime operation metadata for [SpecialistPoolService.UpdateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.UpdateSpecialistPool].

UpdateSpecialistPoolRequest

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

UpdateTensorboardExperimentRequest

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

UpdateTensorboardOperationMetadata

Details of operations that perform update Tensorboard.

UpdateTensorboardRequest

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

UpdateTensorboardRunRequest

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

UpdateTensorboardTimeSeriesRequest

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

UploadModelOperationMetadata

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

UploadModelRequest

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

UploadModelResponse

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

UpsertDatapointsRequest

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

UpsertDatapointsResponse

Response message for [IndexService.UpsertDatapoints][google.cloud.aiplatform.v1.IndexService.UpsertDatapoints]

UserActionReference

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

Value

Value is the value of the field.

ValueConverter

Utility methods for working with Value and Struct.

VersionName

Resource name for the Version resource.

VizierService

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.

VizierService.VizierServiceBase

Base class for server-side implementations of VizierService

VizierService.VizierServiceClient

Client for VizierService

VizierServiceClient

VizierService client wrapper, for convenient use.

VizierServiceClientBuilder

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

VizierServiceClientImpl

VizierService client wrapper implementation, for convenient use.

VizierServiceSettings

Settings for VizierServiceClient instances.

WorkerPoolSpec

Represents the spec of a worker pool in a job.

WriteFeatureValuesPayload

Contains Feature values to be written for a specific entity.

WriteFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.WriteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.WriteFeatureValues].

WriteFeatureValuesResponse

Response message for [FeaturestoreOnlineServingService.WriteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.WriteFeatureValues].

WriteTensorboardExperimentDataRequest

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

WriteTensorboardExperimentDataResponse

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

WriteTensorboardRunDataRequest

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

WriteTensorboardRunDataResponse

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

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.

Enums

AcceleratorType

Represents a hardware accelerator type.

ActiveLearningConfig.HumanLabelingBudgetOneofCase

Enum of possible cases for the "human_labeling_budget" oneof.

AnnotatedDatasetName.ResourceNameType

The possible contents of AnnotatedDatasetName.

AnnotationName.ResourceNameType

The possible contents of AnnotationName.

AnnotationSpecName.ResourceNameType

The possible contents of AnnotationSpecName.

Artifact.Types.State

Describes the state of the Artifact.

ArtifactName.ResourceNameType

The possible contents of ArtifactName.

AutoMLDatasetName.ResourceNameType

The possible contents of AutoMLDatasetName.

AutoMLModelName.ResourceNameType

The possible contents of AutoMLModelName.

BatchMigrateResourcesOperationMetadata.Types.PartialResult.ResultOneofCase

Enum of possible cases for the "result" oneof.

BatchPredictionJob.Types.InputConfig.SourceOneofCase

Enum of possible cases for the "source" oneof.

BatchPredictionJob.Types.OutputConfig.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

BatchPredictionJob.Types.OutputInfo.OutputLocationOneofCase

Enum of possible cases for the "output_location" oneof.

BatchPredictionJobName.ResourceNameType

The possible contents of BatchPredictionJobName.

BatchReadFeatureValuesRequest.ReadOptionOneofCase

Enum of possible cases for the "read_option" oneof.

ContextName.ResourceNameType

The possible contents of ContextName.

CopyModelRequest.DestinationModelOneofCase

Enum of possible cases for the "destination_model" oneof.

CustomJobName.ResourceNameType

The possible contents of CustomJobName.

DataItemName.ResourceNameType

The possible contents of DataItemName.

DataLabelingDatasetName.ResourceNameType

The possible contents of DataLabelingDatasetName.

DataLabelingJobName.ResourceNameType

The possible contents of DataLabelingJobName.

DatasetName.ResourceNameType

The possible contents of DatasetName.

DeployedModel.PredictionResourcesOneofCase

Enum of possible cases for the "prediction_resources" oneof.

EndpointName.ResourceNameType

The possible contents of EndpointName.

EntityTypeName.ResourceNameType

The possible contents of EntityTypeName.

Event.Types.Type

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

ExamplesOverride.Types.DataFormat

Data format enum.

Execution.Types.State

Describes the state of the Execution.

ExecutionName.ResourceNameType

The possible contents of ExecutionName.

ExplanationMetadata.Types.InputMetadata.Types.Encoding

Defines how a feature is encoded. Defaults to IDENTITY.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.ColorMap

The color scheme used for highlighting areas.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.OverlayType

How the original image is displayed in the visualization.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.Polarity

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

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.Type

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].

ExplanationMetadata.Types.OutputMetadata.DisplayNameMappingOneofCase

Enum of possible cases for the "display_name_mapping" oneof.

ExplanationParameters.MethodOneofCase

Enum of possible cases for the "method" oneof.

ExportDataConfig.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

ExportFeatureValuesRequest.ModeOneofCase

Enum of possible cases for the "mode" oneof.

Feature.Types.MonitoringStatsAnomaly.Types.Objective

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

Feature.Types.ValueType

An enum representing the value type of a feature.

FeatureName.ResourceNameType

The possible contents of FeatureName.

Featurestore.Types.State

Possible states a featurestore can have.

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis.Types.Baseline

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

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis.Types.State

The state defines whether to enable ImportFeature analysis.

FeaturestoreMonitoringConfig.Types.ThresholdConfig.ThresholdOneofCase

Enum of possible cases for the "threshold" oneof.

FeaturestoreName.ResourceNameType

The possible contents of FeaturestoreName.

FeatureValue.ValueOneofCase

Enum of possible cases for the "value" oneof.

FeatureValueDestination.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

HyperparameterTuningJobName.ResourceNameType

The possible contents of HyperparameterTuningJobName.

ImportDataConfig.SourceOneofCase

Enum of possible cases for the "source" oneof.

ImportFeatureValuesRequest.FeatureTimeSourceOneofCase

Enum of possible cases for the "feature_time_source" oneof.

ImportFeatureValuesRequest.SourceOneofCase

Enum of possible cases for the "source" oneof.

Index.Types.IndexUpdateMethod

The update method of an Index.

IndexEndpointName.ResourceNameType

The possible contents of IndexEndpointName.

IndexName.ResourceNameType

The possible contents of IndexName.

InputDataConfig.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

InputDataConfig.SplitOneofCase

Enum of possible cases for the "split" oneof.

JobState

Describes the state of a job.

MetadataSchema.Types.MetadataSchemaType

Describes the type of the MetadataSchema.

MetadataSchemaName.ResourceNameType

The possible contents of MetadataSchemaName.

MetadataStoreName.ResourceNameType

The possible contents of MetadataStoreName.

MigratableResource.ResourceOneofCase

Enum of possible cases for the "resource" oneof.

MigrateResourceRequest.RequestOneofCase

Enum of possible cases for the "request" oneof.

MigrateResourceResponse.MigratedResourceOneofCase

Enum of possible cases for the "migrated_resource" oneof.

Model.Types.DeploymentResourcesType

Identifies a type of Model's prediction resources.

Model.Types.ExportFormat.Types.ExportableContent

The Model content that can be exported.

ModelDeploymentMonitoringBigQueryTable.Types.LogSource

Indicates where does the log come from.

ModelDeploymentMonitoringBigQueryTable.Types.LogType

Indicates what type of traffic does the log belong to.

ModelDeploymentMonitoringJob.Types.MonitoringScheduleState

The state to Specify the monitoring pipeline.

ModelDeploymentMonitoringJobName.ResourceNameType

The possible contents of ModelDeploymentMonitoringJobName.

ModelDeploymentMonitoringObjectiveType

The Model Monitoring Objective types.

ModelEvaluationName.ResourceNameType

The possible contents of ModelEvaluationName.

ModelEvaluationSliceName.ResourceNameType

The possible contents of ModelEvaluationSliceName.

ModelMonitoringAlertConfig.AlertOneofCase

Enum of possible cases for the "alert" oneof.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline.Types.PredictionFormat

The storage format of the predictions generated BatchPrediction job.

ModelMonitoringObjectiveConfig.Types.TrainingDataset.DataSourceOneofCase

Enum of possible cases for the "data_source" oneof.

ModelName.ResourceNameType

The possible contents of ModelName.

ModelSourceInfo.Types.ModelSourceType

Source of the model.

NasJobName.ResourceNameType

The possible contents of NasJobName.

NasJobOutput.OutputOneofCase

Enum of possible cases for the "output" oneof.

NasJobSpec.NasAlgorithmSpecOneofCase

Enum of possible cases for the "nas_algorithm_spec" oneof.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MetricSpec.Types.GoalType

The available types of optimization goals.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MultiTrialAlgorithm

The available types of multi-trial algorithms.

NasTrial.Types.State

Describes a NasTrial state.

NasTrialDetailName.ResourceNameType

The possible contents of NasTrialDetailName.

NearestNeighborSearchOperationMetadata.Types.RecordError.Types.RecordErrorType

NetworkName.ResourceNameType

The possible contents of NetworkName.

PipelineFailurePolicy

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

PipelineJob.Types.RuntimeConfig.Types.InputArtifact.KindOneofCase

Enum of possible cases for the "kind" oneof.

PipelineJobName.ResourceNameType

The possible contents of PipelineJobName.

PipelineState

Describes the state of a pipeline.

PipelineTaskDetail.Types.State

Specifies state of TaskExecution

PipelineTaskExecutorDetail.DetailsOneofCase

Enum of possible cases for the "details" oneof.

ReadFeatureValuesResponse.Types.EntityView.Types.Data.DataOneofCase

Enum of possible cases for the "data" oneof.

SampleConfig.FollowingBatchSampleSizeOneofCase

Enum of possible cases for the "following_batch_sample_size" oneof.

SampleConfig.InitialBatchSampleSizeOneofCase

Enum of possible cases for the "initial_batch_sample_size" oneof.

SampleConfig.Types.SampleStrategy

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

SavedQueryName.ResourceNameType

The possible contents of SavedQueryName.

SearchDataItemsRequest.OrderOneofCase

Enum of possible cases for the "order" oneof.

SmoothGradConfig.GradientNoiseSigmaOneofCase

Enum of possible cases for the "GradientNoiseSigma" oneof.

SpecialistPoolName.ResourceNameType

The possible contents of SpecialistPoolName.

Study.Types.State

Describes the Study state.

StudyName.ResourceNameType

The possible contents of StudyName.

StudySpec.AutomatedStoppingSpecOneofCase

Enum of possible cases for the "automated_stopping_spec" oneof.

StudySpec.Types.Algorithm

The available search algorithms for the Study.

StudySpec.Types.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.