Package types (0.5.1)

API documentation for aiplatform_v1beta1.types package.

Classes

ActiveLearningConfig

Parameters that configure 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.

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.

Attribution

Attribution that explains a particular prediction output.

AutomaticResources

A description of resources that to large degree are decided by AI Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

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.

BigQueryDestination

The BigQuery location for the output content.

BigQuerySource

The BigQuery location for the input content.

CancelBatchPredictionJobRequest

Request message for JobService.CancelBatchPredictionJob.

CancelCustomJobRequest

Request message for JobService.CancelCustomJob.

CancelDataLabelingJobRequest

Request message for [DataLabelingJobService.CancelDataLabelingJob][].

CancelHyperparameterTuningJobRequest

Request message for JobService.CancelHyperparameterTuningJob.

CancelTrainingPipelineRequest

Request message for PipelineService.CancelTrainingPipeline.

CompletionStats

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

ContainerRegistryDestination

The Container Regsitry location for the container image.

ContainerSpec

The spec of a Container.

CreateBatchPredictionJobRequest

Request message for JobService.CreateBatchPredictionJob.

CreateCustomJobRequest

Request message for JobService.CreateCustomJob.

CreateDataLabelingJobRequest

Request message for [DataLabelingJobService.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.

CreateHyperparameterTuningJobRequest

Request message for JobService.CreateHyperparameterTuningJob.

CreateSpecialistPoolOperationMetadata

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

CreateSpecialistPoolRequest

Request message for SpecialistPoolService.CreateSpecialistPool.

CreateTrainingPipelineRequest

Request message for PipelineService.CreateTrainingPipeline.

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.

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.

DedicatedResources

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

DeleteBatchPredictionJobRequest

Request message for JobService.DeleteBatchPredictionJob.

DeleteCustomJobRequest

Request message for JobService.DeleteCustomJob.

DeleteDataLabelingJobRequest

Request message for JobService.DeleteDataLabelingJob.

DeleteDatasetRequest

Request message for DatasetService.DeleteDataset.

DeleteEndpointRequest

Request message for EndpointService.DeleteEndpoint.

DeleteHyperparameterTuningJobRequest

Request message for JobService.DeleteHyperparameterTuningJob.

DeleteModelRequest

Request message for ModelService.DeleteModel.

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

DeleteSpecialistPoolRequest

Request message for SpecialistPoolService.DeleteSpecialistPool.

DeleteTrainingPipelineRequest

Request message for PipelineService.DeleteTrainingPipeline.

DeployModelOperationMetadata

Runtime operation information for EndpointService.DeployModel.

DeployModelRequest

Request message for EndpointService.DeployModel.

DeployModelResponse

Response message for EndpointService.DeployModel.

DeployedModel

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

DeployedModelRef

Points to a DeployedModel.

DiskSpec

Represents the spec of disk options.

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.

EnvVar

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

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.

ExplanationSpec

Specification of Model explanation.

ExplanationSpecOverride

The ExplanationSpec entries that can be overridden at [online explanation]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.

ExportModelOperationMetadata

Details of ModelService.ExportModel operation.

ExportModelRequest

Request message for ModelService.ExportModel.

ExportModelResponse

Response message of ModelService.ExportModel operation.

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.

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 AI Platform. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

GcsDestination

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

GcsSource

The Google Cloud Storage location for the input content.

GenericOperationMetadata

Generic Metadata shared by all operations.

GetAnnotationSpecRequest

Request message for DatasetService.GetAnnotationSpec.

GetBatchPredictionJobRequest

Request message for JobService.GetBatchPredictionJob.

GetCustomJobRequest

Request message for JobService.GetCustomJob.

GetDataLabelingJobRequest

Request message for [DataLabelingJobService.GetDataLabelingJob][].

GetDatasetRequest

Request message for DatasetService.GetDataset.

GetEndpointRequest

Request message for EndpointService.GetEndpoint

GetHyperparameterTuningJobRequest

Request message for JobService.GetHyperparameterTuningJob.

GetModelEvaluationRequest

Request message for ModelService.GetModelEvaluation.

GetModelEvaluationSliceRequest

Request message for ModelService.GetModelEvaluationSlice.

GetModelRequest

Request message for ModelService.GetModel.

GetSpecialistPoolRequest

Request message for SpecialistPoolService.GetSpecialistPool.

GetTrainingPipelineRequest

Request message for PipelineService.GetTrainingPipeline.

HyperparameterTuningJob

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

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.

InputDataConfig

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

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

ListAnnotationsRequest

Request message for DatasetService.ListAnnotations.

ListAnnotationsResponse

Response message for DatasetService.ListAnnotations.

ListBatchPredictionJobsRequest

Request message for JobService.ListBatchPredictionJobs.

ListBatchPredictionJobsResponse

Response message for JobService.ListBatchPredictionJobs

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 [DataLabelingJobService.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.

ListHyperparameterTuningJobsRequest

Request message for JobService.ListHyperparameterTuningJobs.

ListHyperparameterTuningJobsResponse

Response message for JobService.ListHyperparameterTuningJobs

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

ListSpecialistPoolsRequest

Request message for SpecialistPoolService.ListSpecialistPools.

ListSpecialistPoolsResponse

Response message for SpecialistPoolService.ListSpecialistPools.

ListTrainingPipelinesRequest

Request message for PipelineService.ListTrainingPipelines.

ListTrainingPipelinesResponse

Response message for PipelineService.ListTrainingPipelines

MachineSpec

Specification of a single machine.

ManualBatchTuningParameters

Manual batch tuning parameters.

Measurement

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

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 AI Platform.

MigrateResourceResponse

Describes a successfully migrated resource.

Model

A trained machine learning Model.

ModelContainerSpec

Specification of a container for serving predictions. This message is a subset of the Kubernetes Container v1 core specification <https://tinyurl.com/k8s-io-api/v1.18/#container-v1-core>__.

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.

Port

Represents a network port in a container.

PredefinedSplit

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

Supported only for tabular Datasets.

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.

PythonPackageSpec

The spec of a Python packaged code.

ResourcesConsumed

Statistics information about resource consumption.

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.

Scheduling

All parameters related to queuing and scheduling of custom jobs.

SearchMigratableResourcesRequest

Request message for MigrationService.SearchMigratableResources.

SearchMigratableResourcesResponse

Response message for MigrationService.SearchMigratableResources.

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 who are responsible for managing the labelers 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 labelers work with the jobs using CrowdCompute console.

StudySpec

Represents specification of a Study.

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 AI Platform's Dataset which becomes the training input, upload the Model to AI Platform, and evaluate the Model.