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API documentation for aiplatform_v1beta1.types
package.
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.
AddContextArtifactsAndExecutionsResponse
Response message for MetadataService.AddContextArtifactsAndExecutions.
AddContextChildrenRequest
Request message for MetadataService.AddContextChildren.
AddContextChildrenResponse
Response message for MetadataService.AddContextChildren.
AddExecutionEventsRequest
Request message for MetadataService.AddExecutionEvents.
AddExecutionEventsResponse
Response message for MetadataService.AddExecutionEvents.
AddTrialMeasurementRequest
Request message for VizierService.AddTrialMeasurement.
Annotation
Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.
AnnotationSpec
Identifies a concept with which DataItems may be annotated with.
Artifact
Instance of a general artifact. .. attribute:: name
Output only. The resource name of the Artifact.
:type: str
Attribution
Attribution that explains a particular prediction output. .. attribute:: baseline_output_value
Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs. The field name of the output is determined by the key in ExplanationMetadata.outputs.
If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index.
If there are multiple baselines, their output values are averaged.
:type: float
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.
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. .. attribute:: gcs_source
Required. Google Cloud Storage location.
BatchCreateFeaturesOperationMetadata
Details of operations that perform batch create Features. .. attribute:: generic_metadata
Operation metadata for Feature.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
BatchCreateFeaturesRequest
Request message for FeaturestoreService.BatchCreateFeatures.
BatchCreateFeaturesResponse
Response message for FeaturestoreService.BatchCreateFeatures.
BatchCreateTensorboardRunsRequest
Request message for TensorboardService.BatchCreateTensorboardRuns.
BatchCreateTensorboardRunsResponse
Response message for TensorboardService.BatchCreateTensorboardRuns.
BatchCreateTensorboardTimeSeriesRequest
Request message for TensorboardService.BatchCreateTensorboardTimeSeries.
BatchCreateTensorboardTimeSeriesResponse
Response message for TensorboardService.BatchCreateTensorboardTimeSeries.
BatchDedicatedResources
A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.
BatchMigrateResourcesOperationMetadata
Runtime operation information for MigrationService.BatchMigrateResources.
BatchMigrateResourcesRequest
Request message for MigrationService.BatchMigrateResources.
BatchMigrateResourcesResponse
Response message for MigrationService.BatchMigrateResources.
BatchPredictionJob
A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1beta1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.
BatchReadFeatureValuesOperationMetadata
Details of operations that batch reads Feature values. .. attribute:: generic_metadata
Operation metadata for Featurestore batch read Features values.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
BatchReadFeatureValuesRequest
Request message for FeaturestoreService.BatchReadFeatureValues. (- Next Id: 6 -)
BatchReadFeatureValuesResponse
Response message for FeaturestoreService.BatchReadFeatureValues.
BigQueryDestination
The BigQuery location for the output content. .. attribute:: output_uri
Required. BigQuery URI to a project or table, up to 2000 characters long.
When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist.
Accepted forms:
BigQuery path. For example:
bq://projectId
orbq://projectId.bqDatasetId
orbq://projectId.bqDatasetId.bqTableId
.:type: str
BigQuerySource
The BigQuery location for the input content. .. attribute:: input_uri
Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms:
BigQuery path. For example:
bq://projectId.bqDatasetId.bqTableId
.:type: str
BoolArray
A list of boolean values. .. attribute:: values
A list of bool values.
:type: Sequence[bool]
CancelBatchPredictionJobRequest
Request message for JobService.CancelBatchPredictionJob.
CancelCustomJobRequest
Request message for JobService.CancelCustomJob.
CancelDataLabelingJobRequest
Request message for JobService.CancelDataLabelingJob.
CancelHyperparameterTuningJobRequest
Request message for JobService.CancelHyperparameterTuningJob.
CancelPipelineJobRequest
Request message for PipelineService.CancelPipelineJob.
CancelTrainingPipelineRequest
Request message for 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.
CheckTrialEarlyStoppingStateResponse
Response message for VizierService.CheckTrialEarlyStoppingState.
CompleteTrialRequest
Request message for 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. .. attribute:: output_uri
Required. Container Registry URI of a container image. Only Google Container Registry and Artifact Registry are supported now. Accepted forms:
Google Container Registry path. For example:
gcr.io/projectId/imageName:tag
.Artifact Registry path. For example:
us-central1-docker.pkg.dev/projectId/repoName/imageName:tag
.If a tag is not specified, "latest" will be used as the default tag.
:type: str
ContainerSpec
The spec of a Container. .. attribute:: image_uri
Required. The URI of a container image in the Container Registry that is to be run on each worker replica.
:type: str
Context
Instance of a general context. .. attribute:: name
Output only. The resource name of the Context.
:type: str
CreateArtifactRequest
Request message for MetadataService.CreateArtifact.
CreateBatchPredictionJobRequest
Request message for JobService.CreateBatchPredictionJob.
CreateContextRequest
Request message for MetadataService.CreateContext.
CreateCustomJobRequest
Request message for JobService.CreateCustomJob.
CreateDataLabelingJobRequest
Request message for JobService.CreateDataLabelingJob.
CreateDatasetOperationMetadata
Runtime operation information for DatasetService.CreateDataset.
CreateDatasetRequest
Request message for DatasetService.CreateDataset.
CreateEndpointOperationMetadata
Runtime operation information for EndpointService.CreateEndpoint.
CreateEndpointRequest
Request message for EndpointService.CreateEndpoint.
CreateEntityTypeOperationMetadata
Details of operations that perform create EntityType. .. attribute:: generic_metadata
Operation metadata for EntityType.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
CreateEntityTypeRequest
Request message for FeaturestoreService.CreateEntityType.
CreateExecutionRequest
Request message for MetadataService.CreateExecution.
CreateFeatureOperationMetadata
Details of operations that perform create Feature. .. attribute:: generic_metadata
Operation metadata for Feature.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
CreateFeatureRequest
Request message for FeaturestoreService.CreateFeature.
CreateFeaturestoreOperationMetadata
Details of operations that perform create Featurestore. .. attribute:: generic_metadata
Operation metadata for Featurestore.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
CreateFeaturestoreRequest
Request message for FeaturestoreService.CreateFeaturestore.
CreateHyperparameterTuningJobRequest
Request message for JobService.CreateHyperparameterTuningJob.
CreateIndexEndpointOperationMetadata
Runtime operation information for IndexEndpointService.CreateIndexEndpoint.
CreateIndexEndpointRequest
Request message for IndexEndpointService.CreateIndexEndpoint.
CreateIndexOperationMetadata
Runtime operation information for IndexService.CreateIndex.
CreateIndexRequest
Request message for IndexService.CreateIndex.
CreateMetadataSchemaRequest
Request message for MetadataService.CreateMetadataSchema.
CreateMetadataStoreOperationMetadata
Details of operations that perform MetadataService.CreateMetadataStore.
CreateMetadataStoreRequest
Request message for MetadataService.CreateMetadataStore.
CreateModelDeploymentMonitoringJobRequest
Request message for JobService.CreateModelDeploymentMonitoringJob.
CreatePipelineJobRequest
Request message for PipelineService.CreatePipelineJob.
CreateSpecialistPoolOperationMetadata
Runtime operation information for SpecialistPoolService.CreateSpecialistPool.
CreateSpecialistPoolRequest
Request message for SpecialistPoolService.CreateSpecialistPool.
CreateStudyRequest
Request message for VizierService.CreateStudy.
CreateTensorboardExperimentRequest
Request message for TensorboardService.CreateTensorboardExperiment.
CreateTensorboardOperationMetadata
Details of operations that perform create Tensorboard. .. attribute:: generic_metadata
Operation metadata for Tensorboard.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
CreateTensorboardRequest
Request message for TensorboardService.CreateTensorboard.
CreateTensorboardRunRequest
Request message for TensorboardService.CreateTensorboardRun.
CreateTensorboardTimeSeriesRequest
Request message for TensorboardService.CreateTensorboardTimeSeries.
CreateTrainingPipelineRequest
Request message for PipelineService.CreateTrainingPipeline.
CreateTrialRequest
Request message for VizierService.CreateTrial.
CsvDestination
The storage details for CSV output content. .. attribute:: gcs_destination
Required. Google Cloud Storage location.
CsvSource
The storage details for CSV input content. .. attribute:: gcs_source
Required. Google Cloud Storage location.
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).
CustomJobSpec
Represents the spec of a CustomJob. .. attribute:: worker_pool_specs
Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
:type: Sequence[google.cloud.aiplatform_v1beta1.types.WorkerPoolSpec]
DataItem
A piece of data in a Dataset. Could be an image, a video, a document or plain text.
DataLabelingJob
DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:
Dataset
A collection of DataItems and Annotations on them. .. attribute:: name
Output only. The resource name of the Dataset.
:type: str
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.
DeleteBatchPredictionJobRequest
Request message for JobService.DeleteBatchPredictionJob.
DeleteContextRequest
Request message for MetadataService.DeleteContext.
DeleteCustomJobRequest
Request message for JobService.DeleteCustomJob.
DeleteDataLabelingJobRequest
Request message for JobService.DeleteDataLabelingJob.
DeleteDatasetRequest
Request message for DatasetService.DeleteDataset.
DeleteEndpointRequest
Request message for EndpointService.DeleteEndpoint.
DeleteEntityTypeRequest
Request message for [FeaturestoreService.DeleteEntityTypes][]. .. attribute:: name
Required. The name of the EntityType to be deleted. Format:
projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}
:type: str
DeleteExecutionRequest
Request message for MetadataService.DeleteExecution.
DeleteFeatureRequest
Request message for FeaturestoreService.DeleteFeature.
DeleteFeaturestoreRequest
Request message for FeaturestoreService.DeleteFeaturestore.
DeleteHyperparameterTuningJobRequest
Request message for JobService.DeleteHyperparameterTuningJob.
DeleteIndexEndpointRequest
Request message for IndexEndpointService.DeleteIndexEndpoint.
DeleteIndexRequest
Request message for IndexService.DeleteIndex.
DeleteMetadataStoreOperationMetadata
Details of operations that perform MetadataService.DeleteMetadataStore.
DeleteMetadataStoreRequest
Request message for MetadataService.DeleteMetadataStore.
DeleteModelDeploymentMonitoringJobRequest
Request message for JobService.DeleteModelDeploymentMonitoringJob.
DeleteModelRequest
Request message for ModelService.DeleteModel.
DeleteOperationMetadata
Details of operations that perform deletes of any entities. .. attribute:: generic_metadata
The common part of the operation metadata.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
DeletePipelineJobRequest
Request message for PipelineService.DeletePipelineJob.
DeleteSpecialistPoolRequest
Request message for SpecialistPoolService.DeleteSpecialistPool.
DeleteStudyRequest
Request message for VizierService.DeleteStudy.
DeleteTensorboardExperimentRequest
Request message for TensorboardService.DeleteTensorboardExperiment.
DeleteTensorboardRequest
Request message for TensorboardService.DeleteTensorboard.
DeleteTensorboardRunRequest
Request message for TensorboardService.DeleteTensorboardRun.
DeleteTensorboardTimeSeriesRequest
Request message for TensorboardService.DeleteTensorboardTimeSeries.
DeleteTrainingPipelineRequest
Request message for PipelineService.DeleteTrainingPipeline.
DeleteTrialRequest
Request message for VizierService.DeleteTrial.
DeployIndexOperationMetadata
Runtime operation information for IndexEndpointService.DeployIndex.
DeployIndexRequest
Request message for IndexEndpointService.DeployIndex.
DeployIndexResponse
Response message for IndexEndpointService.DeployIndex.
DeployModelOperationMetadata
Runtime operation information for EndpointService.DeployModel.
DeployModelRequest
Request message for EndpointService.DeployModel.
DeployModelResponse
Response message for EndpointService.DeployModel.
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.
DeployedIndexRef
Points to a DeployedIndex. .. attribute:: index_endpoint
Immutable. A resource name of the IndexEndpoint.
:type: str
DeployedModel
A deployment of a Model. Endpoints contain one or more DeployedModels.
DeployedModelRef
Points to a DeployedModel. .. attribute:: endpoint
Immutable. A resource name of an Endpoint.
:type: str
DestinationFeatureSetting
DiskSpec
Represents the spec of disk options. .. attribute:: boot_disk_type
Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
:type: str
DoubleArray
A list of double values. .. attribute:: values
A list of bool values.
:type: Sequence[float]
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.
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.
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.
Execution
Instance of a general execution. .. attribute:: name
Output only. The resource name of the Execution.
:type: str
ExplainRequest
Request message for PredictionService.Explain.
ExplainResponse
Response message for PredictionService.Explain.
Explanation
Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.
ExplanationMetadata
Metadata describing the Model's input and output for explanation.
ExplanationMetadataOverride
The ExplanationMetadata entries that can be overridden at [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time.
ExplanationParameters
Parameters to configure explaining for Model's predictions. .. attribute:: sampled_shapley_attribution
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. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
:type: google.cloud.aiplatform_v1beta1.types.SampledShapleyAttribution
ExplanationSpec
Specification of Model explanation. .. attribute:: parameters
Required. Parameters that configure explaining of the Model's predictions.
:type: google.cloud.aiplatform_v1beta1.types.ExplanationParameters
ExplanationSpecOverride
The ExplanationSpec entries that can be overridden at [online explanation][google.cloud.aiplatform.v1beta1.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.
ExportDataRequest
Request message for DatasetService.ExportData.
ExportDataResponse
Response message for DatasetService.ExportData.
ExportFeatureValuesOperationMetadata
Details of operations that exports Features values. .. attribute:: generic_metadata
Operation metadata for Featurestore export Feature values.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
ExportFeatureValuesRequest
Request message for FeaturestoreService.ExportFeatureValues.
ExportFeatureValuesResponse
Response message for FeaturestoreService.ExportFeatureValues.
ExportModelOperationMetadata
Details of ModelService.ExportModel operation.
ExportModelRequest
Request message for ModelService.ExportModel.
ExportModelResponse
Response message of ModelService.ExportModel operation.
ExportTensorboardTimeSeriesDataRequest
Request message for TensorboardService.ExportTensorboardTimeSeriesData.
ExportTensorboardTimeSeriesDataResponse
Response message for 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.
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.
FeatureSelector
Selector for Features of an EntityType. .. attribute:: id_matcher
Required. Matches Features based on ID.
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.
FeatureValue
Value for a feature. NEXT ID: 15
FeatureValueDestination
A destination location for Feature values and format. .. attribute:: bigquery_destination
Output in BigQuery format. BigQueryDestination.output_uri in FeatureValueDestination.bigquery_destination must refer to a table.
:type: google.cloud.aiplatform_v1beta1.types.BigQueryDestination
FeatureValueList
Container for list of values. .. attribute:: values
A list of feature values. All of them should be the same data type.
:type: Sequence[google.cloud.aiplatform_v1beta1.types.FeatureValue]
Featurestore
Featurestore configuration information on how the Featurestore is configured.
FeaturestoreMonitoringConfig
Configuration of how features in Featurestore are monitored. .. attribute:: snapshot_analysis
The config for Snapshot Analysis Based Feature Monitoring.
:type: google.cloud.aiplatform_v1beta1.types.FeaturestoreMonitoringConfig.SnapshotAnalysis
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. .. attribute:: uris
Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
:type: Sequence[str]
GenericOperationMetadata
Generic Metadata shared by all operations. .. attribute:: partial_failures
Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard GCP error details.
:type: Sequence[google.rpc.status_pb2.Status]
GetAnnotationSpecRequest
Request message for DatasetService.GetAnnotationSpec.
GetArtifactRequest
Request message for MetadataService.GetArtifact.
GetBatchPredictionJobRequest
Request message for JobService.GetBatchPredictionJob.
GetContextRequest
Request message for MetadataService.GetContext.
GetCustomJobRequest
Request message for JobService.GetCustomJob.
GetDataLabelingJobRequest
Request message for JobService.GetDataLabelingJob.
GetDatasetRequest
Request message for DatasetService.GetDataset.
GetEndpointRequest
Request message for EndpointService.GetEndpoint
GetEntityTypeRequest
Request message for FeaturestoreService.GetEntityType.
GetExecutionRequest
Request message for MetadataService.GetExecution.
GetFeatureRequest
Request message for FeaturestoreService.GetFeature.
GetFeaturestoreRequest
Request message for FeaturestoreService.GetFeaturestore.
GetHyperparameterTuningJobRequest
Request message for JobService.GetHyperparameterTuningJob.
GetIndexEndpointRequest
Request message for IndexEndpointService.GetIndexEndpoint
GetIndexRequest
Request message for IndexService.GetIndex
GetMetadataSchemaRequest
Request message for MetadataService.GetMetadataSchema.
GetMetadataStoreRequest
Request message for MetadataService.GetMetadataStore.
GetModelDeploymentMonitoringJobRequest
Request message for JobService.GetModelDeploymentMonitoringJob.
GetModelEvaluationRequest
Request message for ModelService.GetModelEvaluation.
GetModelEvaluationSliceRequest
Request message for ModelService.GetModelEvaluationSlice.
GetModelRequest
Request message for ModelService.GetModel.
GetPipelineJobRequest
Request message for PipelineService.GetPipelineJob.
GetSpecialistPoolRequest
Request message for SpecialistPoolService.GetSpecialistPool.
GetStudyRequest
Request message for VizierService.GetStudy.
GetTensorboardExperimentRequest
Request message for TensorboardService.GetTensorboardExperiment.
GetTensorboardRequest
Request message for TensorboardService.GetTensorboard.
GetTensorboardRunRequest
Request message for TensorboardService.GetTensorboardRun.
GetTensorboardTimeSeriesRequest
Request message for TensorboardService.GetTensorboardTimeSeries.
GetTrainingPipelineRequest
Request message for PipelineService.GetTrainingPipeline.
GetTrialRequest
Request message for VizierService.GetTrial.
HyperparameterTuningJob
Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.
IdMatcher
Matcher for Features of an EntityType by Feature ID. .. attribute:: ids
Required. The following are accepted as ids
:
- A single-element list containing only
*
, which selects all Features in the target EntityType, or A list containing only Feature IDs, which selects only Features with those IDs in the target EntityType.
:type: Sequence[str]
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.
ImportDataRequest
Request message for DatasetService.ImportData.
ImportDataResponse
Response message for DatasetService.ImportData.
ImportFeatureValuesOperationMetadata
Details of operations that perform import feature values. .. attribute:: generic_metadata
Operation metadata for Featurestore import feature values.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
ImportFeatureValuesRequest
Request message for FeaturestoreService.ImportFeatureValues.
ImportFeatureValuesResponse
Response message for FeaturestoreService.ImportFeatureValues.
Index
A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.
IndexEndpoint
Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.
IndexPrivateEndpoints
IndexPrivateEndpoints proto is used to provide paths for users to send requests via private services access.
InputDataConfig
Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.
Int64Array
A list of int64 values. .. attribute:: values
A list of int64 values.
:type: Sequence[int]
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
LineageSubgraph
A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.
ListAnnotationsRequest
Request message for DatasetService.ListAnnotations.
ListAnnotationsResponse
Response message for DatasetService.ListAnnotations.
ListArtifactsRequest
Request message for MetadataService.ListArtifacts.
ListArtifactsResponse
Response message for MetadataService.ListArtifacts.
ListBatchPredictionJobsRequest
Request message for JobService.ListBatchPredictionJobs.
ListBatchPredictionJobsResponse
Response message for JobService.ListBatchPredictionJobs
ListContextsRequest
Request message for MetadataService.ListContexts
ListContextsResponse
Response message for MetadataService.ListContexts.
ListCustomJobsRequest
Request message for JobService.ListCustomJobs.
ListCustomJobsResponse
Response message for JobService.ListCustomJobs
ListDataItemsRequest
Request message for DatasetService.ListDataItems.
ListDataItemsResponse
Response message for DatasetService.ListDataItems.
ListDataLabelingJobsRequest
Request message for JobService.ListDataLabelingJobs.
ListDataLabelingJobsResponse
Response message for JobService.ListDataLabelingJobs.
ListDatasetsRequest
Request message for DatasetService.ListDatasets.
ListDatasetsResponse
Response message for DatasetService.ListDatasets.
ListEndpointsRequest
Request message for EndpointService.ListEndpoints.
ListEndpointsResponse
Response message for EndpointService.ListEndpoints.
ListEntityTypesRequest
Request message for FeaturestoreService.ListEntityTypes.
ListEntityTypesResponse
Response message for FeaturestoreService.ListEntityTypes.
ListExecutionsRequest
Request message for MetadataService.ListExecutions.
ListExecutionsResponse
Response message for MetadataService.ListExecutions.
ListFeaturesRequest
Request message for FeaturestoreService.ListFeatures.
ListFeaturesResponse
Response message for FeaturestoreService.ListFeatures.
ListFeaturestoresRequest
Request message for FeaturestoreService.ListFeaturestores.
ListFeaturestoresResponse
Response message for FeaturestoreService.ListFeaturestores.
ListHyperparameterTuningJobsRequest
Request message for JobService.ListHyperparameterTuningJobs.
ListHyperparameterTuningJobsResponse
Response message for JobService.ListHyperparameterTuningJobs
ListIndexEndpointsRequest
Request message for IndexEndpointService.ListIndexEndpoints.
ListIndexEndpointsResponse
Response message for IndexEndpointService.ListIndexEndpoints.
ListIndexesRequest
Request message for IndexService.ListIndexes.
ListIndexesResponse
Response message for IndexService.ListIndexes.
ListMetadataSchemasRequest
Request message for MetadataService.ListMetadataSchemas.
ListMetadataSchemasResponse
Response message for MetadataService.ListMetadataSchemas.
ListMetadataStoresRequest
Request message for MetadataService.ListMetadataStores.
ListMetadataStoresResponse
Response message for MetadataService.ListMetadataStores.
ListModelDeploymentMonitoringJobsRequest
Request message for JobService.ListModelDeploymentMonitoringJobs.
ListModelDeploymentMonitoringJobsResponse
Response message for JobService.ListModelDeploymentMonitoringJobs.
ListModelEvaluationSlicesRequest
Request message for ModelService.ListModelEvaluationSlices.
ListModelEvaluationSlicesResponse
Response message for ModelService.ListModelEvaluationSlices.
ListModelEvaluationsRequest
Request message for ModelService.ListModelEvaluations.
ListModelEvaluationsResponse
Response message for ModelService.ListModelEvaluations.
ListModelsRequest
Request message for ModelService.ListModels.
ListModelsResponse
Response message for ModelService.ListModels
ListOptimalTrialsRequest
Request message for VizierService.ListOptimalTrials.
ListOptimalTrialsResponse
Response message for VizierService.ListOptimalTrials.
ListPipelineJobsRequest
Request message for PipelineService.ListPipelineJobs.
ListPipelineJobsResponse
Response message for PipelineService.ListPipelineJobs
ListSpecialistPoolsRequest
Request message for SpecialistPoolService.ListSpecialistPools.
ListSpecialistPoolsResponse
Response message for SpecialistPoolService.ListSpecialistPools.
ListStudiesRequest
Request message for VizierService.ListStudies.
ListStudiesResponse
Response message for VizierService.ListStudies.
ListTensorboardExperimentsRequest
Request message for TensorboardService.ListTensorboardExperiments.
ListTensorboardExperimentsResponse
Response message for TensorboardService.ListTensorboardExperiments.
ListTensorboardRunsRequest
Request message for TensorboardService.ListTensorboardRuns.
ListTensorboardRunsResponse
Response message for TensorboardService.ListTensorboardRuns.
ListTensorboardTimeSeriesRequest
Request message for TensorboardService.ListTensorboardTimeSeries.
ListTensorboardTimeSeriesResponse
Response message for TensorboardService.ListTensorboardTimeSeries.
ListTensorboardsRequest
Request message for TensorboardService.ListTensorboards.
ListTensorboardsResponse
Response message for TensorboardService.ListTensorboards.
ListTrainingPipelinesRequest
Request message for PipelineService.ListTrainingPipelines.
ListTrainingPipelinesResponse
Response message for PipelineService.ListTrainingPipelines
ListTrialsRequest
Request message for VizierService.ListTrials.
ListTrialsResponse
Response message for VizierService.ListTrials.
LookupStudyRequest
Request message for VizierService.LookupStudy.
MachineSpec
Specification of a single machine. .. attribute:: machine_type
Immutable. The type of the machine.
See the list of machine types supported for
prediction <https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types>
__
See the list of machine types supported for custom
training <https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types>
__.
For
DeployedModel
this field is optional, and the default value is
n1-standard-2
. For
BatchPredictionJob
or as part of
WorkerPoolSpec
this field is required.
:type: str
ManualBatchTuningParameters
Manual batch tuning parameters. .. attribute:: batch_size
Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 4.
:type: int
Measurement
A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
MetadataSchema
Instance of a general MetadataSchema. .. attribute:: name
Output only. The resource name of the MetadataSchema.
:type: str
MetadataStore
Instance of a metadata store. Contains a set of metadata that can be queried.
MigratableResource
Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.
MigrateResourceRequest
Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.
MigrateResourceResponse
Describes a successfully migrated resource. .. attribute:: dataset
Migrated Dataset's resource name.
:type: str
Model
A trained machine learning Model. .. attribute:: name
The resource name of the Model.
:type: str
ModelContainerSpec
Specification of a container for serving predictions. Some fields in
this message correspond to fields in the Kubernetes Container v1
core
specification <https://v1-18.docs.kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core>
__.
ModelDeploymentMonitoringBigQueryTable
ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.
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.
ModelDeploymentMonitoringObjectiveConfig
ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.
ModelDeploymentMonitoringObjectiveType
The Model Monitoring Objective types.
ModelDeploymentMonitoringScheduleConfig
The config for scheduling monitoring job. .. attribute:: monitor_interval
Required. The model monitoring job running interval. It will be rounded up to next full hour.
:type: google.protobuf.duration_pb2.Duration
ModelEvaluation
A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.
ModelEvaluationSlice
A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.
ModelExplanation
Aggregated explanation metrics for a Model over a set of instances.
ModelMonitoringAlertConfig
Next ID: 2 .. attribute:: email_alert_config
Email alert config.
:type: google.cloud.aiplatform_v1beta1.types.ModelMonitoringAlertConfig.EmailAlertConfig
ModelMonitoringObjectiveConfig
Next ID: 6 .. attribute:: training_dataset
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
:type: google.cloud.aiplatform_v1beta1.types.ModelMonitoringObjectiveConfig.TrainingDataset
ModelMonitoringStatsAnomalies
Statistics and anomalies generated by Model Monitoring. .. attribute:: objective
Model Monitoring Objective those stats and anomalies belonging to.
:type: google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringObjectiveType
NearestNeighborSearchOperationMetadata
Runtime operation metadata with regard to Matching Engine Index.
PauseModelDeploymentMonitoringJobRequest
Request message for JobService.PauseModelDeploymentMonitoringJob.
PipelineJob
An instance of a machine learning PipelineJob. .. attribute:: name
Output only. The resource name of the PipelineJob.
:type: str
PipelineJobDetail
The runtime detail of PipelineJob. .. attribute:: pipeline_context
Output only. The context of the pipeline.
PipelineTaskDetail
The runtime detail of a task execution. .. attribute:: task_id
Output only. The system generated ID of the task.
:type: int
PipelineTaskExecutorDetail
The runtime detail of a pipeline executor. .. attribute:: container_detail
Output only. The detailed info for a container executor.
:type: google.cloud.aiplatform_v1beta1.types.PipelineTaskExecutorDetail.ContainerDetail
Port
Represents a network port in a container. .. attribute:: container_port
The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
:type: int
PredefinedSplit
Assigns input data to training, validation, and test sets based on the value of a provided key.
Supported only for tabular Datasets.
PredictRequest
Request message for PredictionService.Predict.
PredictResponse
Response message for PredictionService.Predict.
PredictSchemata
Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.
PrivateEndpoints
PrivateEndpoints is used to provide paths for users to send requests via private services access.
PurgeArtifactsMetadata
Details of operations that perform MetadataService.PurgeArtifacts.
PurgeArtifactsRequest
Request message for MetadataService.PurgeArtifacts.
PurgeArtifactsResponse
Response message for MetadataService.PurgeArtifacts.
PurgeContextsMetadata
Details of operations that perform MetadataService.PurgeContexts.
PurgeContextsRequest
Request message for MetadataService.PurgeContexts.
PurgeContextsResponse
Response message for MetadataService.PurgeContexts.
PurgeExecutionsMetadata
Details of operations that perform MetadataService.PurgeExecutions.
PurgeExecutionsRequest
Request message for MetadataService.PurgeExecutions.
PurgeExecutionsResponse
Response message for MetadataService.PurgeExecutions.
PythonPackageSpec
The spec of a Python packaged code. .. attribute:: executor_image_uri
Required. The URI of a container image in Artifact Registry
that will run the provided Python package. Vertex AI
provides a wide range of executor images with pre-installed
packages to meet users' various use cases. See the list of
pre-built containers for
training <https://cloud.google.com/vertex-ai/docs/training/pre-built-containers>
__.
You must use an image from this list.
:type: str
QueryArtifactLineageSubgraphRequest
Request message for MetadataService.QueryArtifactLineageSubgraph.
QueryContextLineageSubgraphRequest
Request message for MetadataService.QueryContextLineageSubgraph.
QueryExecutionInputsAndOutputsRequest
Request message for MetadataService.QueryExecutionInputsAndOutputs.
RawPredictRequest
Request message for PredictionService.RawPredict.
ReadFeatureValuesRequest
Request message for FeaturestoreOnlineServingService.ReadFeatureValues.
ReadFeatureValuesResponse
Response message for FeaturestoreOnlineServingService.ReadFeatureValues.
ReadTensorboardBlobDataRequest
Request message for TensorboardService.ReadTensorboardBlobData.
ReadTensorboardBlobDataResponse
Response message for TensorboardService.ReadTensorboardBlobData.
ReadTensorboardTimeSeriesDataRequest
Request message for TensorboardService.ReadTensorboardTimeSeriesData.
ReadTensorboardTimeSeriesDataResponse
Response message for TensorboardService.ReadTensorboardTimeSeriesData.
ResourcesConsumed
Statistics information about resource consumption. .. attribute:: replica_hours
Output only. The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
:type: float
ResumeModelDeploymentMonitoringJobRequest
Request message for 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.
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. Next ID: 2
Scalar
One point viewable on a scalar metric plot. .. attribute:: value
Value of the point at this step / timestamp.
:type: float
Scheduling
All parameters related to queuing and scheduling of custom jobs.
SearchFeaturesRequest
Request message for FeaturestoreService.SearchFeatures.
SearchFeaturesResponse
Response message for FeaturestoreService.SearchFeatures.
SearchMigratableResourcesRequest
Request message for MigrationService.SearchMigratableResources.
SearchMigratableResourcesResponse
Response message for MigrationService.SearchMigratableResources.
SearchModelDeploymentMonitoringStatsAnomaliesRequest
Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.
SearchModelDeploymentMonitoringStatsAnomaliesResponse
Response message for 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.
StopTrialRequest
Request message for VizierService.StopTrial.
StreamingReadFeatureValuesRequest
Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].
StringArray
A list of string values. .. attribute:: values
A list of string values.
:type: Sequence[str]
Study
A message representing a Study. .. attribute:: name
Output only. The name of a study. The study's globally
unique identifier. Format:
projects/{project}/locations/{location}/studies/{study}
:type: str
StudySpec
Represents specification of a Study. .. attribute:: decay_curve_stopping_spec
The automated early stopping spec using decay curve rule.
:type: google.cloud.aiplatform_v1beta1.types.StudySpec.DecayCurveAutomatedStoppingSpec
SuggestTrialsMetadata
Details of operations that perform Trials suggestion. .. attribute:: generic_metadata
Operation metadata for suggesting Trials.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
SuggestTrialsRequest
Request message for VizierService.SuggestTrials.
SuggestTrialsResponse
Response message for VizierService.SuggestTrials.
TFRecordDestination
The storage details for TFRecord output content. .. attribute:: gcs_destination
Required. Google Cloud Storage location.
Tensorboard
Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a GCP project. If needed users can also create extra Tensorboards in their projects.
TensorboardBlob
One blob (e.g, image, graph) viewable on a blob metric plot. .. attribute:: id
Output only. A URI safe key uniquely identifying a blob. Can be used to locate the blob stored in the Cloud Storage bucket of the consumer project.
:type: str
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.
TensorboardRun
TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc
TensorboardTensor
One point viewable on a tensor metric plot. .. attribute:: value
Required. Serialized form of https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto
:type: bytes
TensorboardTimeSeries
TensorboardTimeSeries maps to times series produced in training runs
ThresholdConfig
The config for feature monitoring threshold. Next ID: 3
TimeSeriesData
All the data stored in a TensorboardTimeSeries. .. attribute:: tensorboard_time_series_id
Required. The ID of the TensorboardTimeSeries, which will become the final component of the TensorboardTimeSeries' resource name
:type: str
TimeSeriesDataPoint
A TensorboardTimeSeries data point. .. attribute:: scalar
A scalar value.
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 the Model to Vertex AI, and evaluate the Model.
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.
UndeployIndexOperationMetadata
Runtime operation information for IndexEndpointService.UndeployIndex.
UndeployIndexRequest
Request message for IndexEndpointService.UndeployIndex.
UndeployIndexResponse
Response message for IndexEndpointService.UndeployIndex.
UndeployModelOperationMetadata
Runtime operation information for EndpointService.UndeployModel.
UndeployModelRequest
Request message for EndpointService.UndeployModel.
UndeployModelResponse
Response message for EndpointService.UndeployModel.
UpdateArtifactRequest
Request message for MetadataService.UpdateArtifact.
UpdateContextRequest
Request message for MetadataService.UpdateContext.
UpdateDatasetRequest
Request message for DatasetService.UpdateDataset.
UpdateEndpointRequest
Request message for EndpointService.UpdateEndpoint.
UpdateEntityTypeRequest
Request message for FeaturestoreService.UpdateEntityType.
UpdateExecutionRequest
Request message for MetadataService.UpdateExecution.
UpdateFeatureRequest
Request message for FeaturestoreService.UpdateFeature.
UpdateFeaturestoreOperationMetadata
Details of operations that perform update Featurestore. .. attribute:: generic_metadata
Operation metadata for Featurestore.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
UpdateFeaturestoreRequest
Request message for FeaturestoreService.UpdateFeaturestore.
UpdateIndexEndpointRequest
Request message for IndexEndpointService.UpdateIndexEndpoint.
UpdateIndexOperationMetadata
Runtime operation information for IndexService.UpdateIndex.
UpdateIndexRequest
Request message for IndexService.UpdateIndex.
UpdateModelDeploymentMonitoringJobOperationMetadata
Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.
UpdateModelDeploymentMonitoringJobRequest
Request message for JobService.UpdateModelDeploymentMonitoringJob.
UpdateModelRequest
Request message for ModelService.UpdateModel.
UpdateSpecialistPoolOperationMetadata
Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.
UpdateSpecialistPoolRequest
Request message for SpecialistPoolService.UpdateSpecialistPool.
UpdateTensorboardExperimentRequest
Request message for TensorboardService.UpdateTensorboardExperiment.
UpdateTensorboardOperationMetadata
Details of operations that perform update Tensorboard. .. attribute:: generic_metadata
Operation metadata for Tensorboard.
:type: google.cloud.aiplatform_v1beta1.types.GenericOperationMetadata
UpdateTensorboardRequest
Request message for TensorboardService.UpdateTensorboard.
UpdateTensorboardRunRequest
Request message for TensorboardService.UpdateTensorboardRun.
UpdateTensorboardTimeSeriesRequest
Request message for TensorboardService.UpdateTensorboardTimeSeries.
UploadModelOperationMetadata
Details of ModelService.UploadModel operation.
UploadModelRequest
Request message for ModelService.UploadModel.
UploadModelResponse
Response message of ModelService.UploadModel operation.
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. .. attribute:: int_value
An integer value.
:type: int
WorkerPoolSpec
Represents the spec of a worker pool in a job. .. attribute:: container_spec
The custom container task.
WriteTensorboardExperimentDataRequest
Request message for TensorboardService.WriteTensorboardExperimentData.
WriteTensorboardExperimentDataResponse
Response message for TensorboardService.WriteTensorboardExperimentData.
WriteTensorboardRunDataRequest
Request message for TensorboardService.WriteTensorboardRunData.
WriteTensorboardRunDataResponse
Response message for 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.