google_cloud_pipeline_components.v1.batch_predict_job module

Core modules for AI Platform Pipeline Components.

google_cloud_pipeline_components.v1.batch_predict_job.ModelBatchPredictOp(project: str, job_display_name: str, location: str = 'us-central1', model: google.VertexModel = None, unmanaged_container_model: google.UnmanagedContainerModel = None, instances_format: str = 'jsonl', gcs_source_uris: list = '[]', bigquery_source_input_uri: str = '', model_parameters: dict = '{}', predictions_format: str = 'jsonl', gcs_destination_output_uri_prefix: str = '', bigquery_destination_output_uri: str = '', machine_type: str = '', accelerator_type: str = '', accelerator_count: int = 0, starting_replica_count: int = 0, max_replica_count: int = 0, manual_batch_tuning_parameters_batch_size: int = 0, generate_explanation: bool = False, explanation_metadata: dict = '{}', explanation_parameters: dict = '{}', labels: dict = '{}', encryption_spec_key_name: str = '')

model_batch_predict Creates a Google Cloud Vertex BatchPredictionJob and waits for it to complete. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs/create.

Args:
project (str):

Required. Project to create the BatchPredictionJob.

location (Optional[str]):

Location for creating the BatchPredictionJob. If not set, default to us-central1.

job_display_name (str):

Required. The user-defined name of this BatchPredictionJob.

model (Optional[google.VertexModel]):

The Model used to get 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. Either this or unmanaged_container_model must be specified.

unmanaged_container_model (Optional[google.UnmanagedContainerModel]):

The unmanaged container model used to get predictions via this job. This should be used for models that are not uploaded to Vertex. Either this or model must be specified.

gcs_source_uris (Optional[Sequence[str]]):

Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match instances_format. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.

bigquery_source_input_uri (Optional[str]):

BigQuery URI to a table, up to 2000 characters long. For example: projectId.bqDatasetId.bqTableId

For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.

instances_format (Optional[str]):

The format in which instances are given, must be one of the Model’s supportedInputStorageFormats. If not set, default to “jsonl”.

For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.

gcs_destination_output_uri_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 containing only code and message fields.

For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.

bigquery_destination_output_uri (Optional[str]):

The BigQuery project location where the output is to be written to. In the given project 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 <Status>`__ represented as a STRUCT, and containing only code and message.

For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.

predictions_format (Optional[str]):

The format in which Vertex AI gives the predictions. Must be one of the Model’s supportedOutputStorageFormats. If not set, default to “jsonl”.

For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.

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. 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.

For more details about the BatchDedicatedResources, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#BatchDedicatedResources.

For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec

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.

For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec

accelerator_count (Optional[int]):

The number of accelerators to attach to the machine_type. Only used if machine_type is set.

For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec

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.

manual_batch_tuning_parameters_batch_size (Optional[int]):

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.

generate_explanation (Optional[bool]):

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.

If this field is set to true, either the Model.explanation_spec or explanation_metadata and explanation_parameters must be populated.

explanation_metadata (Optional[dict]):

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 https://cloud.google.com/vertex-ai/docs/reference/rest/v1/ExplanationSpec#explanationmetadata.

explanation_parameters (Optional[dict]):

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 https://cloud.google.com/vertex-ai/docs/reference/rest/v1/ExplanationSpec#ExplanationParameters.

labels (Optional[dict]):

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.

encryption_spec_key_name (Optional[str]):

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.

Has the form: projects/my-project/locations/my-location/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

Returns:
batchpredictionjob (google.VertexBatchPredictionJob):

[Deprecated. Use gcs_output_directory and bigquery_output_table instead.] Artifact representation of the created batch prediction job.

gcs_output_directory (system.Artifact):

Artifact tracking the batch prediction job output. This is only available if gcs_destination_output_uri_prefix is specified.

bigquery_output_table (google.BQTable):

Artifact tracking the batch prediction job output. This is only available if bigquery_output_table is specified.

gcp_resources (str):

Serialized gcp_resources proto tracking the batch prediction job.

For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.