Package types (1.19.1)

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.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Request message for MetadataService.AddContextArtifactsAndExecutions .

Response message for MetadataService.AddContextArtifactsAndExecutions .

Request message for MetadataService.AddContextChildren .

Response message for MetadataService.AddContextChildren .

Request message for MetadataService.AddExecutionEvents .

Response message for MetadataService.AddExecutionEvents .

Request message for VizierService.AddTrialMeasurement .

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

Identifies a concept with which DataItems may be annotated with.

Instance of a general artifact.

Attribution that explains a particular prediction output.

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.

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

The storage details for Avro input content.

Details of operations that perform batch create Features.

Request message for FeaturestoreService.BatchCreateFeatures .

Response message for FeaturestoreService.BatchCreateFeatures .

Request message for TensorboardService.BatchCreateTensorboardRuns .

Response message for TensorboardService.BatchCreateTensorboardRuns .

Request message for TensorboardService.BatchCreateTensorboardTimeSeries .

Response message for TensorboardService.BatchCreateTensorboardTimeSeries .

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

Request message for ModelService.BatchImportModelEvaluationSlices

Response message for ModelService.BatchImportModelEvaluationSlices

Runtime operation information for MigrationService.BatchMigrateResources .

Request message for MigrationService.BatchMigrateResources .

Response message for MigrationService.BatchMigrateResources .

A job that uses a 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.

Details of operations that batch reads Feature values.

Request message for FeaturestoreService.BatchReadFeatureValues .

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Response message for FeaturestoreService.BatchReadFeatureValues .

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData .

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData .

The BigQuery location for the output content.

The BigQuery location for the input content.

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

A list of boolean values.

Request message for JobService.CancelBatchPredictionJob .

Request message for JobService.CancelCustomJob .

Request message for JobService.CancelDataLabelingJob .

Request message for JobService.CancelHyperparameterTuningJob .

Request message for PipelineService.CancelPipelineJob .

Request message for PipelineService.CancelTrainingPipeline .

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

Request message for VizierService.CheckTrialEarlyStoppingState .

Response message for VizierService.CheckTrialEarlyStoppingState .

Request message for VizierService.CompleteTrial .

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

The Container Registry location for the container image.

The spec of a Container.

Instance of a general context.

Request message for MetadataService.CreateArtifact .

Request message for JobService.CreateBatchPredictionJob .

Request message for MetadataService.CreateContext .

Request message for JobService.CreateCustomJob .

Request message for JobService.CreateDataLabelingJob .

Runtime operation information for DatasetService.CreateDataset .

Request message for DatasetService.CreateDataset .

Runtime operation information for EndpointService.CreateEndpoint .

Request message for EndpointService.CreateEndpoint .

Details of operations that perform create EntityType.

Request message for FeaturestoreService.CreateEntityType .

Request message for MetadataService.CreateExecution .

Details of operations that perform create Feature.

Request message for FeaturestoreService.CreateFeature .

Details of operations that perform create Featurestore.

Request message for FeaturestoreService.CreateFeaturestore .

Request message for JobService.CreateHyperparameterTuningJob .

Runtime operation information for IndexEndpointService.CreateIndexEndpoint .

Request message for IndexEndpointService.CreateIndexEndpoint .

Runtime operation information for IndexService.CreateIndex .

Request message for IndexService.CreateIndex .

Request message for MetadataService.CreateMetadataSchema .

Details of operations that perform MetadataService.CreateMetadataStore .

Request message for MetadataService.CreateMetadataStore .

Request message for JobService.CreateModelDeploymentMonitoringJob .

Request message for PipelineService.CreatePipelineJob .

Runtime operation information for SpecialistPoolService.CreateSpecialistPool .

Request message for SpecialistPoolService.CreateSpecialistPool .

Request message for VizierService.CreateStudy .

Request message for TensorboardService.CreateTensorboardExperiment .

Details of operations that perform create Tensorboard.

Request message for TensorboardService.CreateTensorboard .

Request message for TensorboardService.CreateTensorboardRun .

Request message for TensorboardService.CreateTensorboardTimeSeries .

Request message for PipelineService.CreateTrainingPipeline .

Request message for VizierService.CreateTrial .

The storage details for CSV output content.

The storage details for CSV input content.

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).

Represents the spec of a CustomJob.

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

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

A collection of DataItems and Annotations on them.

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

Request message for MetadataService.DeleteArtifact .

Request message for JobService.DeleteBatchPredictionJob .

Request message for MetadataService.DeleteContext .

Request message for JobService.DeleteCustomJob .

Request message for JobService.DeleteDataLabelingJob .

Request message for DatasetService.DeleteDataset .

Request message for EndpointService.DeleteEndpoint .

Request message for [FeaturestoreService.DeleteEntityTypes][].

Request message for MetadataService.DeleteExecution .

Request message for FeaturestoreService.DeleteFeature .

Request message for FeaturestoreService.DeleteFeaturestore .

Request message for JobService.DeleteHyperparameterTuningJob .

Request message for IndexEndpointService.DeleteIndexEndpoint .

Request message for IndexService.DeleteIndex .

Details of operations that perform MetadataService.DeleteMetadataStore .

Request message for MetadataService.DeleteMetadataStore .

Request message for JobService.DeleteModelDeploymentMonitoringJob .

Request message for ModelService.DeleteModel .

Request message for ModelService.DeleteModelVersion .

Details of operations that perform deletes of any entities.

Request message for PipelineService.DeletePipelineJob .

Request message for SpecialistPoolService.DeleteSpecialistPool .

Request message for VizierService.DeleteStudy .

Request message for TensorboardService.DeleteTensorboardExperiment .

Request message for TensorboardService.DeleteTensorboard .

Request message for TensorboardService.DeleteTensorboardRun .

Request message for TensorboardService.DeleteTensorboardTimeSeries .

Request message for PipelineService.DeleteTrainingPipeline .

Request message for VizierService.DeleteTrial .

Runtime operation information for IndexEndpointService.DeployIndex .

Request message for IndexEndpointService.DeployIndex .

Response message for IndexEndpointService.DeployIndex .

Runtime operation information for EndpointService.DeployModel .

Request message for EndpointService.DeployModel .

Response message for EndpointService.DeployModel .

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

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

Points to a DeployedIndex.

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

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Points to a DeployedModel.

Represents the spec of disk options.

A list of double values.

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

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

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.

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

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

Overrides for example-based explanations.

Restrictions namespace for example-based explanations overrides.

Instance of a general execution.

Request message for PredictionService.Explain .

Response message for PredictionService.Explain .

Explanation of a prediction (provided in PredictResponse.predictions ) produced by the Model on a given instance .

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

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

Parameters to configure explaining for Model's predictions.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Specification of Model explanation.

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

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

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Runtime operation information for DatasetService.ExportData .

Request message for DatasetService.ExportData .

Response message for DatasetService.ExportData .

Details of operations that exports Features values.

Request message for FeaturestoreService.ExportFeatureValues .

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Response message for FeaturestoreService.ExportFeatureValues .

Details of ModelService.ExportModel operation.

Request message for ModelService.ExportModel .

Response message of ModelService.ExportModel operation.

Request message for TensorboardService.ExportTensorboardTimeSeriesData .

Response message for TensorboardService.ExportTensorboardTimeSeriesData .

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.

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.

Selector for Features of an EntityType.

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.

Value for a feature.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

A destination location for Feature values and format.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Container for list of values.

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.

Configuration of how features in Featurestore are monitored.

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.

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.

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

The Google Cloud Storage location for the input content.

Generic Metadata shared by all operations.

Request message for DatasetService.GetAnnotationSpec .

Request message for MetadataService.GetArtifact .

Request message for JobService.GetBatchPredictionJob .

Request message for MetadataService.GetContext .

Request message for JobService.GetCustomJob .

Request message for JobService.GetDataLabelingJob .

Request message for DatasetService.GetDataset .

Request message for EndpointService.GetEndpoint

Request message for FeaturestoreService.GetEntityType .

Request message for MetadataService.GetExecution .

Request message for FeaturestoreService.GetFeature .

Request message for FeaturestoreService.GetFeaturestore .

Request message for JobService.GetHyperparameterTuningJob .

Request message for IndexEndpointService.GetIndexEndpoint

Request message for IndexService.GetIndex

Request message for MetadataService.GetMetadataSchema .

Request message for MetadataService.GetMetadataStore .

Request message for JobService.GetModelDeploymentMonitoringJob .

Request message for ModelService.GetModelEvaluation .

Request message for ModelService.GetModelEvaluationSlice .

Request message for ModelService.GetModel .

Request message for PipelineService.GetPipelineJob .

Request message for SpecialistPoolService.GetSpecialistPool .

Request message for VizierService.GetStudy .

Request message for TensorboardService.GetTensorboardExperiment .

Request message for TensorboardService.GetTensorboard .

Request message for TensorboardService.GetTensorboardRun .

Request message for TensorboardService.GetTensorboardTimeSeries .

Request message for PipelineService.GetTrainingPipeline .

Request message for VizierService.GetTrial .

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

Matcher for Features of an EntityType by Feature ID.

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.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Runtime operation information for DatasetService.ImportData .

Request message for DatasetService.ImportData .

Response message for DatasetService.ImportData .

Details of operations that perform import Feature values.

Request message for FeaturestoreService.ImportFeatureValues .

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Response message for FeaturestoreService.ImportFeatureValues .

Request message for ModelService.ImportModelEvaluation

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

A datapoint of Index.

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

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.

Stats of the Index.

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

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

A list of int64 values.

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

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

Request message for DatasetService.ListAnnotations .

Response message for DatasetService.ListAnnotations .

Request message for MetadataService.ListArtifacts .

Response message for MetadataService.ListArtifacts .

Request message for JobService.ListBatchPredictionJobs .

Response message for JobService.ListBatchPredictionJobs

Request message for MetadataService.ListContexts

Response message for MetadataService.ListContexts .

Request message for JobService.ListCustomJobs .

Response message for JobService.ListCustomJobs

Request message for DatasetService.ListDataItems .

Response message for DatasetService.ListDataItems .

Request message for JobService.ListDataLabelingJobs .

Response message for JobService.ListDataLabelingJobs .

Request message for DatasetService.ListDatasets .

Response message for DatasetService.ListDatasets .

Request message for EndpointService.ListEndpoints .

Response message for EndpointService.ListEndpoints .

Request message for FeaturestoreService.ListEntityTypes .

Response message for FeaturestoreService.ListEntityTypes .

Request message for MetadataService.ListExecutions .

Response message for MetadataService.ListExecutions .

Request message for FeaturestoreService.ListFeatures .

Response message for FeaturestoreService.ListFeatures .

Request message for FeaturestoreService.ListFeaturestores .

Response message for FeaturestoreService.ListFeaturestores .

Request message for JobService.ListHyperparameterTuningJobs .

Response message for JobService.ListHyperparameterTuningJobs

Request message for IndexEndpointService.ListIndexEndpoints .

Response message for IndexEndpointService.ListIndexEndpoints .

Request message for IndexService.ListIndexes .

Response message for IndexService.ListIndexes .

Request message for MetadataService.ListMetadataSchemas .

Response message for MetadataService.ListMetadataSchemas .

Request message for MetadataService.ListMetadataStores .

Response message for MetadataService.ListMetadataStores .

Request message for JobService.ListModelDeploymentMonitoringJobs .

Response message for JobService.ListModelDeploymentMonitoringJobs .

Request message for ModelService.ListModelEvaluationSlices .

Response message for ModelService.ListModelEvaluationSlices .

Request message for ModelService.ListModelEvaluations .

Response message for ModelService.ListModelEvaluations .

Request message for ModelService.ListModelVersions .

Response message for ModelService.ListModelVersions

Request message for ModelService.ListModels .

Response message for ModelService.ListModels

Request message for VizierService.ListOptimalTrials .

Response message for VizierService.ListOptimalTrials .

Request message for PipelineService.ListPipelineJobs .

Response message for PipelineService.ListPipelineJobs

Request message for DatasetService.ListSavedQueries .

Response message for DatasetService.ListSavedQueries .

Request message for SpecialistPoolService.ListSpecialistPools .

Response message for SpecialistPoolService.ListSpecialistPools .

Request message for VizierService.ListStudies .

Response message for VizierService.ListStudies .

Request message for TensorboardService.ListTensorboardExperiments .

Response message for TensorboardService.ListTensorboardExperiments .

Request message for TensorboardService.ListTensorboardRuns .

Response message for TensorboardService.ListTensorboardRuns .

Request message for TensorboardService.ListTensorboardTimeSeries .

Response message for TensorboardService.ListTensorboardTimeSeries .

Request message for TensorboardService.ListTensorboards .

Response message for TensorboardService.ListTensorboards .

Request message for PipelineService.ListTrainingPipelines .

Response message for PipelineService.ListTrainingPipelines

Request message for VizierService.ListTrials .

Response message for VizierService.ListTrials .

Request message for VizierService.LookupStudy .

Specification of a single machine.

Manual batch tuning parameters.

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

Request message for ModelService.MergeVersionAliases .

Instance of a general MetadataSchema.

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

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

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

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

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Describes a successfully migrated resource.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

A trained machine learning Model.

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification <https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core> __.

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

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 contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

The Model Monitoring Objective types.

The config for scheduling monitoring job.

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

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

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

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

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

Statistics and anomalies generated by Model Monitoring.

Detail description of the source information of the model.

Runtime operation information for IndexEndpointService.MutateDeployedIndex .

Request message for IndexEndpointService.MutateDeployedIndex .

Response message for IndexEndpointService.MutateDeployedIndex .

Runtime operation metadata with regard to Matching Engine Index.

Neighbors for example-based explanations.

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

Request message for JobService.PauseModelDeploymentMonitoringJob .

An instance of a machine learning PipelineJob.

The runtime detail of PipelineJob.

The runtime detail of a task execution.

The runtime detail of a pipeline executor.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.

Represents a network port in a container.

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

Supported only for tabular Datasets.

Request message for PredictionService.Predict .

Configuration for logging request-response to a BigQuery table.

Response message for PredictionService.Predict .

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict , PredictionService.Explain and BatchPredictionJob .

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.

Details of operations that perform MetadataService.PurgeArtifacts .

Request message for MetadataService.PurgeArtifacts .

Response message for MetadataService.PurgeArtifacts .

Details of operations that perform MetadataService.PurgeContexts .

Request message for MetadataService.PurgeContexts .

Response message for MetadataService.PurgeContexts .

Details of operations that perform MetadataService.PurgeExecutions .

Request message for MetadataService.PurgeExecutions .

Response message for MetadataService.PurgeExecutions .

The spec of a Python packaged code.

Request message for MetadataService.QueryArtifactLineageSubgraph .

Request message for MetadataService.QueryContextLineageSubgraph .

Request message for MetadataService.QueryExecutionInputsAndOutputs .

Request message for PredictionService.RawPredict .

Request message for FeaturestoreOnlineServingService.ReadFeatureValues .

Response message for FeaturestoreOnlineServingService.ReadFeatureValues .

Request message for TensorboardService.ReadTensorboardBlobData .

Response message for TensorboardService.ReadTensorboardBlobData .

Request message for TensorboardService.ReadTensorboardTimeSeriesData .

Response message for TensorboardService.ReadTensorboardTimeSeriesData .

Request message for [MetadataService.DeleteContextChildrenRequest][].

Response message for MetadataService.RemoveContextChildren .

Request message for IndexService.RemoveDatapoints

Response message for IndexService.RemoveDatapoints

Statistics information about resource consumption.

Request message for JobService.ResumeModelDeploymentMonitoringJob .

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

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

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.

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

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

One point viewable on a scalar metric plot.

All parameters related to queuing and scheduling of custom jobs.

Request message for FeaturestoreService.SearchFeatures .

Response message for FeaturestoreService.SearchFeatures .

Request message for MigrationService.SearchMigratableResources .

Response message for MigrationService.SearchMigratableResources .

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies .

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies .

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

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

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.

Request message for VizierService.StopTrial .

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.

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

A list of string values.

A message representing a Study.

Represents specification of a Study.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Details of operations that perform Trials suggestion.

Request message for VizierService.SuggestTrials .

Response message for VizierService.SuggestTrials .

The storage details for TFRecord output content.

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.

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

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.

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

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

One point viewable on a tensor metric plot.

TensorboardTimeSeries maps to times series produced in training runs

The config for feature monitoring threshold.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

All the data stored in a TensorboardTimeSeries.

A TensorboardTimeSeries data point.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

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.

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.

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.

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.

Runtime operation information for IndexEndpointService.UndeployIndex .

Request message for IndexEndpointService.UndeployIndex .

Response message for IndexEndpointService.UndeployIndex .

Runtime operation information for EndpointService.UndeployModel .

Request message for EndpointService.UndeployModel .

Response message for EndpointService.UndeployModel .

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

Request message for MetadataService.UpdateArtifact .

Request message for MetadataService.UpdateContext .

Request message for DatasetService.UpdateDataset .

Request message for EndpointService.UpdateEndpoint .

Request message for FeaturestoreService.UpdateEntityType .

Request message for MetadataService.UpdateExecution .

Request message for FeaturestoreService.UpdateFeature .

Details of operations that perform update Featurestore.

Request message for FeaturestoreService.UpdateFeaturestore .

Request message for IndexEndpointService.UpdateIndexEndpoint .

Runtime operation information for IndexService.UpdateIndex .

Request message for IndexService.UpdateIndex .

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob .

Request message for JobService.UpdateModelDeploymentMonitoringJob .

Request message for ModelService.UpdateModel .

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool .

Request message for SpecialistPoolService.UpdateSpecialistPool .

Request message for TensorboardService.UpdateTensorboardExperiment .

Details of operations that perform update Tensorboard.

Request message for TensorboardService.UpdateTensorboard .

Request message for TensorboardService.UpdateTensorboardRun .

Request message for TensorboardService.UpdateTensorboardTimeSeries .

Details of ModelService.UploadModel operation.

Request message for ModelService.UploadModel .

Response message of ModelService.UploadModel operation.

Request message for IndexService.UpsertDatapoints

Response message for IndexService.UpsertDatapoints

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

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Value is the value of the field.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Represents the spec of a worker pool in a job.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Contains Feature values to be written for a specific entity.

Request message for FeaturestoreOnlineServingService.WriteFeatureValues .

Response message for FeaturestoreOnlineServingService.WriteFeatureValues .

Request message for TensorboardService.WriteTensorboardExperimentData .

Response message for TensorboardService.WriteTensorboardExperimentData .

Request message for TensorboardService.WriteTensorboardRunData .

Response message for TensorboardService.WriteTensorboardRunData .

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.