:py:mod:`airflow.providers.google.cloud.hooks.mlengine`
=======================================================

.. py:module:: airflow.providers.google.cloud.hooks.mlengine

.. autoapi-nested-parse::

   This module contains a Google ML Engine Hook.


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.hooks.mlengine.MLEngineHook


Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.hooks.mlengine.log


.. py:data:: log
   

.. py:class:: MLEngineHook(gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None)

   Bases: :py:obj:`airflow.providers.google.common.hooks.base_google.GoogleBaseHook`

   Hook for Google ML Engine APIs.

   All the methods in the hook where project_id is used must be called with
   keyword arguments rather than positional.

   .. py:method:: get_conn(self)

      Retrieves the connection to MLEngine.

      :return: Google MLEngine services object.


   .. py:method:: create_job(self, job, project_id, use_existing_job_fn = None)

      Launches a MLEngine job and wait for it to reach a terminal state.

      :param project_id: The Google Cloud project id within which MLEngine
          job will be launched. If set to None or missing, the default project_id from the Google Cloud
          connection is used.
      :param job: MLEngine Job object that should be provided to the MLEngine
          API, such as: ::

              {
                'jobId': 'my_job_id',
                'trainingInput': {
                  'scaleTier': 'STANDARD_1',
                  ...
                }
              }

      :param use_existing_job_fn: In case that a MLEngine job with the same
          job_id already exist, this method (if provided) will decide whether
          we should use this existing job, continue waiting for it to finish
          and returning the job object. It should accepts a MLEngine job
          object, and returns a boolean value indicating whether it is OK to
          reuse the existing job. If 'use_existing_job_fn' is not provided,
          we by default reuse the existing MLEngine job.
      :return: The MLEngine job object if the job successfully reach a
          terminal state (which might be FAILED or CANCELLED state).
      :rtype: dict


   .. py:method:: cancel_job(self, job_id, project_id)

      Cancels a MLEngine job.

      :param project_id: The Google Cloud project id within which MLEngine
          job will be cancelled. If set to None or missing, the default project_id from the Google Cloud
          connection is used.
      :param job_id: A unique id for the want-to-be cancelled Google MLEngine training job.

      :return: Empty dict if cancelled successfully
      :rtype: dict
      :raises: googleapiclient.errors.HttpError


   .. py:method:: create_version(self, model_name, version_spec, project_id)

      Creates the Version on Google Cloud ML Engine.

      :param version_spec: A dictionary containing the information about the version. (templated)
      :param model_name: The name of the Google Cloud ML Engine model that the version belongs to.
          (templated)
      :param project_id: The Google Cloud project name to which MLEngine model belongs.
          If set to None or missing, the default project_id from the Google Cloud connection is used.
          (templated)
      :return: If the version was created successfully, returns the operation.
          Otherwise raises an error .
      :rtype: dict


   .. py:method:: set_default_version(self, model_name, version_name, project_id)

      Sets a version to be the default. Blocks until finished.

      :param model_name: The name of the Google Cloud ML Engine model that the version belongs to.
          (templated)
      :param version_name: A name to use for the version being operated upon. (templated)
      :param project_id: The Google Cloud project name to which MLEngine model belongs. If set to None
          or missing, the default project_id from the Google Cloud connection is used. (templated)
      :return: If successful, return an instance of Version.
          Otherwise raises an error.
      :rtype: dict
      :raises: googleapiclient.errors.HttpError


   .. py:method:: list_versions(self, model_name, project_id)

      Lists all available versions of a model. Blocks until finished.

      :param model_name: The name of the Google Cloud ML Engine model that the version
          belongs to. (templated)
      :param project_id: The Google Cloud project name to which MLEngine model belongs. If set to None or
          missing, the default project_id from the Google Cloud connection is used. (templated)
      :return: return an list of instance of Version.
      :rtype: List[Dict]
      :raises: googleapiclient.errors.HttpError


   .. py:method:: delete_version(self, model_name, version_name, project_id)

      Deletes the given version of a model. Blocks until finished.

      :param model_name: The name of the Google Cloud ML Engine model that the version
          belongs to. (templated)
      :param project_id: The Google Cloud project name to which MLEngine
          model belongs.
      :return: If the version was deleted successfully, returns the operation.
          Otherwise raises an error.
      :rtype: Dict


   .. py:method:: create_model(self, model, project_id)

      Create a Model. Blocks until finished.

      :param model: A dictionary containing the information about the model.
      :param project_id: The Google Cloud project name to which MLEngine model belongs. If set to None or
          missing, the default project_id from the Google Cloud connection is used. (templated)
      :return: If the version was created successfully, returns the instance of Model.
          Otherwise raises an error.
      :rtype: Dict
      :raises: googleapiclient.errors.HttpError


   .. py:method:: get_model(self, model_name, project_id)

      Gets a Model. Blocks until finished.

      :param model_name: The name of the model.
      :param project_id: The Google Cloud project name to which MLEngine model belongs. If set to None
          or missing, the default project_id from the Google Cloud connection is used. (templated)
      :return: If the model exists, returns the instance of Model.
          Otherwise return None.
      :rtype: Dict
      :raises: googleapiclient.errors.HttpError


   .. py:method:: delete_model(self, model_name, project_id, delete_contents = False)

      Delete a Model. Blocks until finished.

      :param model_name: The name of the model.
      :param delete_contents: Whether to force the deletion even if the models is not empty.
          Will delete all version (if any) in the dataset if set to True.
          The default value is False.
      :param project_id: The Google Cloud project name to which MLEngine model belongs. If set to None
          or missing, the default project_id from the Google Cloud connection is used. (templated)
      :raises: googleapiclient.errors.HttpError