:py:mod:`airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job`
=================================================================================

.. py:module:: airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job

.. autoapi-nested-parse::

   This module contains Google Vertex AI operators.

   .. spelling::

       jsonl
       codepoints
       aiplatform
       gapic


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.CreateBatchPredictionJobOperator
   airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.DeleteBatchPredictionJobOperator
   airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.GetBatchPredictionJobOperator
   airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.ListBatchPredictionJobsOperator


.. py:class:: CreateBatchPredictionJobOperator(*, region, project_id, job_display_name, model_name, instances_format = 'jsonl', predictions_format = 'jsonl', gcs_source = None, bigquery_source = None, gcs_destination_prefix = None, bigquery_destination_prefix = None, model_parameters = None, machine_type = None, accelerator_type = None, accelerator_count = None, starting_replica_count = None, max_replica_count = None, generate_explanation = False, explanation_metadata = None, explanation_parameters = None, labels = None, encryption_spec_key_name = None, sync = True, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start.

   :param project_id: Required. The ID of the Google Cloud project that the service belongs to.
   :param region: Required. The ID of the Google Cloud region that the service belongs to.
   :param batch_prediction_job: Required. The BatchPredictionJob to create.
   :param job_display_name: 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.
   :param model_name: Required. A fully-qualified model resource name or model ID.
   :param instances_format: 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".
   :param predictions_format: 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".
   :param gcs_source: 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.
   :param bigquery_source: BigQuery URI to a table, up to 2000 characters long.
       For example: `bq://projectId.bqDatasetId.bqTableId`
   :param gcs_destination_prefix: 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`` <Status>`__ containing only ``code`` and ``message`` fields.
   :param bigquery_destination_prefix: 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``.
   :param model_parameters: The parameters that govern the predictions. The schema of the parameters may
       be specified via the Model's `parameters_schema_uri`.
   :param machine_type: 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.
   :param accelerator_type: The type of accelerator(s) that may be attached to the machine as per
       `accelerator_count`. Only used if `machine_type` is set.
   :param accelerator_count: The number of accelerators to attach to the `machine_type`. Only used if
       `machine_type` is set.
   :param starting_replica_count: 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.
   :param max_replica_count: The maximum number of machine replicas the batch operation may be scaled
       to. Only used if `machine_type` is set. Default is 10.
   :param generate_explanation: 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.
   :param explanation_metadata: 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>`
   :param explanation_parameters: 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>`
   :param labels: 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.
   :param encryption_spec_key_name: 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.
   :param sync: 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.
   :param retry: Designation of what errors, if any, should be retried.
   :param timeout: The timeout for this request.
   :param metadata: Strings which should be sent along with the request as metadata.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill(self)

      Callback called when the operator is killed.
      Cancel any running job.


.. py:class:: DeleteBatchPredictionJobOperator(*, region, project_id, batch_prediction_job_id, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Deletes a BatchPredictionJob. Can only be called on jobs that already finished.

   :param project_id: Required. The ID of the Google Cloud project that the service belongs to.
   :param region: Required. The ID of the Google Cloud region that the service belongs to.
   :param batch_prediction_job_id: The ID of the BatchPredictionJob resource to be deleted.
   :param retry: Designation of what errors, if any, should be retried.
   :param timeout: The timeout for this request.
   :param metadata: Strings which should be sent along with the request as metadata.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'batch_prediction_job_id', 'impersonation_chain']

      
   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: GetBatchPredictionJobOperator(*, region, project_id, batch_prediction_job, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Gets a BatchPredictionJob

   :param project_id: Required. The ID of the Google Cloud project that the service belongs to.
   :param region: Required. The ID of the Google Cloud region that the service belongs to.
   :param batch_prediction_job: Required. The name of the BatchPredictionJob resource.
   :param retry: Designation of what errors, if any, should be retried.
   :param timeout: The timeout for this request.
   :param metadata: Strings which should be sent along with the request as metadata.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: ListBatchPredictionJobsOperator(*, region, project_id, filter = None, page_size = None, page_token = None, read_mask = None, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Lists BatchPredictionJobs in a Location.

   :param project_id: Required. The ID of the Google Cloud project that the service belongs to.
   :param region: Required. The ID of the Google Cloud region that the service belongs to.
   :param filter: The standard list filter.
       Supported fields:
       -  ``display_name`` supports = and !=.
       -  ``state`` supports = and !=.
       -  ``model_display_name`` supports = and !=
       Some examples of using the filter are:
       -  ``state="JOB_STATE_SUCCEEDED" AND display_name="my_job"``
       -  ``state="JOB_STATE_RUNNING" OR display_name="my_job"``
       -  ``NOT display_name="my_job"``
       -  ``state="JOB_STATE_FAILED"``
   :param page_size: The standard list page size.
   :param page_token: The standard list page token.
   :param read_mask: Mask specifying which fields to read.
   :param retry: Designation of what errors, if any, should be retried.
   :param timeout: The timeout for this request.
   :param metadata: Strings which should be sent along with the request as metadata.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.