Class BatchPredictionJob (1.2.0)

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

NameDescription
name str
Output only. Resource name of the BatchPredictionJob.
display_name str
Required. The user-defined name of this BatchPredictionJob.
model str
Required. The name of the Model 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.
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.
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.
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).
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 Sequence[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 GCP 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 Sequence[google.cloud.aiplatform_v1.types.BatchPredictionJob.LabelsEntry]
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.

Inheritance

builtins.object > proto.message.Message > BatchPredictionJob

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.

LabelsEntry

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

The abstract base class for a message.

Parameters
NameDescription
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.

OutputInfo

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

Further describes this job's output. Supplements output_config.