Class BatchPredictionJob (1.79.0) 

BatchPredictionJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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.

Attributes
Name Description
name str
Output only. Resource name of the BatchPredictionJob.
display_name str
Required. The user-defined name of this BatchPredictionJob.
model str
The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
model_version_id str
Output only. The version ID of the Model that produces the predictions via this job.
unmanaged_container_model google.cloud.aiplatform_v1.types.UnmanagedContainerModel
Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
input_config google.cloud.aiplatform_v1.types.BatchPredictionJob.InputConfig
Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.
instance_config google.cloud.aiplatform_v1.types.BatchPredictionJob.InstanceConfig
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
model_parameters google.protobuf.struct_pb2.Value
The parameters that govern the predictions. The schema of the parameters may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.
output_config google.cloud.aiplatform_v1.types.BatchPredictionJob.OutputConfig
Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri and prediction_schema_uri.
dedicated_resources google.cloud.aiplatform_v1.types.BatchDedicatedResources
The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
service_account str
The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
manual_batch_tuning_parameters google.cloud.aiplatform_v1.types.ManualBatchTuningParameters
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
generate_explanation bool
Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: - bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. - jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. - csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
explanation_spec google.cloud.aiplatform_v1.types.ExplanationSpec
Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
output_info google.cloud.aiplatform_v1.types.BatchPredictionJob.OutputInfo
Output only. Information further describing the output of this job.
state google.cloud.aiplatform_v1.types.JobState
Output only. The detailed state of the job.
error google.rpc.status_pb2.Status
Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
partial_failures MutableSequence[google.rpc.status_pb2.Status]
Output only. Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resources_consumed google.cloud.aiplatform_v1.types.ResourcesConsumed
Output only. Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
completion_stats google.cloud.aiplatform_v1.types.CompletionStats
Output only. Statistics on completed and failed prediction instances.
create_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the BatchPredictionJob was created.
start_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
end_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
update_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the BatchPredictionJob was most recently updated.
labels MutableMapping[str, str]
The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
encryption_spec google.cloud.aiplatform_v1.types.EncryptionSpec
Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
disable_container_logging bool
For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to `Cloud Logging pricing
satisfies_pzs bool
Output only. Reserved for future use.
satisfies_pzi bool
Output only. Reserved for future use.

Classes

InputConfig

InputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

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

InstanceConfig

InstanceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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

LabelsEntry

LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

Parameters
Name Description
kwargs dict

Keys and values corresponding to the fields of the message.
mapping Union[dict, .Message]

A dictionary or message to be used to determine the values for this message.
ignore_unknown_fields Optional(bool)

If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

OutputConfig

OutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

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

OutputInfo

OutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Further describes this job's output. Supplements output_config.

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

Methods

BatchPredictionJob

BatchPredictionJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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.