Class BatchPredictionJob (1.17.0)

Version 1.17.0

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BatchPredictionJob(
    batch_prediction_job_name: str,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
)

Retrieves a BatchPredictionJob resource and instantiates its representation.

Parameter

Name	Description
batch_prediction_job_name	str Required. A fully-qualified BatchPredictionJob resource name or ID. Example: "projects/.../locations/.../batchPredictionJobs/456" or "456" when project and location are initialized or passed.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > builtins.object > abc.ABC > google.cloud.aiplatform.base.DoneMixin > google.cloud.aiplatform.base.StatefulResource > google.cloud.aiplatform.base.VertexAiStatefulResource > google.cloud.aiplatform.jobs._Job > BatchPredictionJob

Properties

completion_stats

Statistics on completed and failed prediction instances.

output_info

Information describing the output of this job, including output location into which prediction output is written.

This is only available for batch prediction jobs that have run successfully.

partial_failures

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.

Methods

create

create(
    job_display_name: str,
    model_name: Union[str, aiplatform.Model],
    instances_format: str = "jsonl",
    predictions_format: str = "jsonl",
    gcs_source: Optional[Union[str, Sequence[str]]] = None,
    bigquery_source: Optional[str] = None,
    gcs_destination_prefix: Optional[str] = None,
    bigquery_destination_prefix: Optional[str] = None,
    model_parameters: Optional[Dict] = None,
    machine_type: Optional[str] = None,
    accelerator_type: Optional[str] = None,
    accelerator_count: Optional[int] = None,
    starting_replica_count: Optional[int] = None,
    max_replica_count: Optional[int] = None,
    generate_explanation: Optional[bool] = False,
    explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None,
    explanation_parameters: Optional[aiplatform.explain.ExplanationParameters] = None,
    labels: Optional[Dict[str, str]] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: Optional[str] = None,
    sync: bool = True,
    create_request_timeout: Optional[float] = None,
    batch_size: Optional[int] = None,
    model_monitoring_objective_config: Optional[
        aiplatform.model_monitoring.ObjectiveConfig
    ] = None,
    model_monitoring_alert_config: Optional[
        aiplatform.model_monitoring.AlertConfig
    ] = None,
    analysis_instance_schema_uri: Optional[str] = None,
)

Create a batch prediction job.

Parameters

Name	Description
job_display_name	str Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.
model_name	Union[str, aiplatform.Model] Required. A fully-qualified model resource name or model ID. Example: "projects/123/locations/us-central1/models/456" or "456" when project and location are initialized or passed. May optionally contain a version ID or alias in {model_name}@{version} form. Or an instance of aiplatform.Model.
instances_format	str Required. The format in which instances are provided. Must be one of the formats listed in Model.supported_input_storage_formats. Default is "jsonl" when using gcs_source. If a bigquery_source is provided, this is overridden to "bigquery".
predictions_format	str Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in Model.supported_output_storage_formats. Default is "jsonl" when using gcs_destination_prefix. If a bigquery_destination_prefix is provided, this is overridden to "bigquery".
gcs_source	Optional[Sequence[str]] Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match instances_format.
bigquery_source	Optional[str] BigQuery URI to a table, up to 2000 characters long. For example: bq://projectId.bqDatasetId.bqTableId
gcs_destination_prefix	Optional[str] The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-<model-display-name>-<job-create-time>, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.<extension>, predictions_0002.<extension>, ..., predictions_N.<extension> are created where <extension> depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001.<extension>, errors_0002.<extension>,..., errors_N.<extension> files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has `google.rpc.Status
bigquery_destination_prefix	Optional[str] The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction_<model-display-name>_<job-create-time> where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status][google.rpc.Status] represented as a STRUCT, and containing only code and message.
model_parameters	Optional[Dict] The parameters that govern the predictions. The schema of the parameters may be specified via the Model's parameters_schema_uri.
machine_type	Optional[str] The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources.
accelerator_type	Optional[str] The type of accelerator(s) that may be attached to the machine as per accelerator_count. Only used if machine_type is set.
accelerator_count	Optional[int] The number of accelerators to attach to the machine_type. Only used if machine_type is set.
starting_replica_count	Optional[int] The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count. Only used if machine_type is set.
max_replica_count	Optional[int] The maximum number of machine replicas the batch operation may be scaled to. Only used if machine_type is set. Default is 10.
generate_explanation	bool Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on the prediction_format: - bigquery: output includes a column named explanation. The value is a struct that conforms to the [aiplatform.gapic.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 [aiplatform.gapic.Explanation] object. - csv: Generating explanations for CSV format is not supported.
explanation_metadata	aiplatform.explain.ExplanationMetadata Optional. Explanation metadata configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to True. This value overrides the value of Model.explanation_metadata. All fields of explanation_metadata are optional in the request. If a field of the explanation_metadata object is not populated, the corresponding field of the Model.explanation_metadata object is inherited. For more details, see Ref docs <http://tinyurl.com/1igh60kt>
explanation_parameters	aiplatform.explain.ExplanationParameters Optional. Parameters to configure explaining for Model's predictions. Can be specified only if generate_explanation is set to True. This value overrides the value of Model.explanation_parameters. All fields of explanation_parameters are optional in the request. If a field of the explanation_parameters object is not populated, the corresponding field of the Model.explanation_parameters object is inherited. For more details, see Ref docs <http://tinyurl.com/1an4zake>
labels	Dict[str, str] Optional. The labels with user-defined metadata to organize your 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.
credentials	Optional[auth_credentials.Credentials] Custom credentials to use to create this batch prediction job. Overrides credentials set in aiplatform.init.
encryption_spec_key_name	Optional[str] Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key. Overrides encryption_spec_key_name set in aiplatform.init.
sync	bool Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.
create_request_timeout	float Optional. The timeout for the create request in seconds.
batch_size	int Optional. 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 64.
model_monitoring_objective_config	aiplatform.model_monitoring.ObjectiveConfig Optional. The objective config for model monitoring. Passing this parameter enables monitoring on the model associated with this batch prediction job.
model_monitoring_alert_config	aiplatform.model_monitoring.EmailAlertConfig Optional. Configures how model monitoring alerts are sent to the user. Right now only email alert is supported.
analysis_instance_schema_uri	str Optional. Only applicable if model_monitoring_objective_config is also passed. This parameter specifies the YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

Returns

Type	Description
(jobs.BatchPredictionJob)	Instantiated representation of the created batch prediction job.

iter_outputs

iter_outputs(bq_max_results: Optional[int] = 100)

Returns an Iterable object to traverse the output files, either a list of GCS Blobs or a BigQuery RowIterator depending on the output config set when the BatchPredictionJob was created.

Exceptions

Type	Description
RuntimeError	If BatchPredictionJob is in a JobState other than SUCCEEDED, since outputs cannot be retrieved until the Job has finished.
NotImplementedError	If BatchPredictionJob succeeded and output_info does not have a GCS or BQ output provided.

Returns

Type	Description
Union[Iterable[storage.Blob], Iterable[bigquery.table.RowIterator]]	Either a list of GCS Blob objects within the prediction output directory or an iterable BigQuery RowIterator with predictions.

wait_for_resource_creation

wait_for_resource_creation()

Waits until resource has been created.