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API documentation for aiplatform_v1.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.
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.
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.v1.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. .. 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 are 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.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
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 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
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. .. attribute:: worker_pool_specs
Required. The spec of the worker pools including machine type and Docker image.
:type: Sequence[google.cloud.aiplatform_v1.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.
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. .. attribute:: generic_metadata
The common part of the operation metadata.
:type: google.cloud.aiplatform_v1.types.GenericOperationMetadata
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. .. attribute:: endpoint
Immutable. A resource name of an Endpoint.
:type: str
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
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.
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.
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. .. 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.
GetBatchPredictionJobRequest
Request message for JobService.GetBatchPredictionJob.
GetCustomJobRequest
Request message for JobService.GetCustomJob.
GetDataLabelingJobRequest
Request message for [DataLabelingJobService.GetDataLabelingJob][]. .. attribute:: name
Required. The name of the DataLabelingJob. Format:
projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}
:type: str
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.
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][]. .. attribute:: parent
Required. The parent of the DataLabelingJob. Format:
projects/{project}/locations/{location}
:type: str
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. .. attribute:: machine_type
Immutable. The type of the machine. For the machine types supported for prediction, see https://tinyurl.com/aip-docs/predictions/machine-types. For machine types supported for creating a custom training job, see https://tinyurl.com/aip-docs/training/configure-compute.
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.
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. .. 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. 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.
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.
PythonPackageSpec
The spec of a Python packaged code. .. attribute:: executor_image_uri
Required. The URI of a container image in the Container Registry that will run the provided python package. AI Platform provides wide range of executor images with pre-installed packages to meet users' various use cases. Only one of the provided images can be set here.
:type: str
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
SampleConfig
Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
Scheduling
All parameters related to queuing and scheduling of custom jobs.
SearchMigratableResourcesRequest
Request message for MigrationService.SearchMigratableResources.
SearchMigratableResourcesResponse
Response message for MigrationService.SearchMigratableResources.
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. .. attribute:: metrics
Required. Metric specs for the Study.
:type: Sequence[google.cloud.aiplatform_v1.types.StudySpec.MetricSpec]
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.
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.
UndeployModelOperationMetadata
Runtime operation information for EndpointService.UndeployModel.
UndeployModelRequest
Request message for EndpointService.UndeployModel.
UndeployModelResponse
Response message for EndpointService.UndeployModel.
UpdateDatasetRequest
Request message for DatasetService.UpdateDataset.
UpdateEndpointRequest
Request message for EndpointService.UpdateEndpoint.
UpdateModelRequest
Request message for ModelService.UpdateModel.
UpdateSpecialistPoolOperationMetadata
Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.
UpdateSpecialistPoolRequest
Request message for SpecialistPoolService.UpdateSpecialistPool.
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.
WorkerPoolSpec
Represents the spec of a worker pool in a job. .. attribute:: container_spec
The custom container task.