:mod:`airflow.contrib.hooks.gcp_mlengine_hook`
==============================================

.. py:module:: airflow.contrib.hooks.gcp_mlengine_hook


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

.. function:: _poll_with_exponential_delay(request, max_n, is_done_func, is_error_func)

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

   Bases: :class:`airflow.contrib.hooks.gcp_api_base_hook.GoogleCloudBaseHook`

   
   .. method:: get_conn(self)

      Returns a Google MLEngine service object.



   
   .. method:: create_job(self, project_id, job, 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.
      :type project_id: str

      :param job: MLEngine Job object that should be provided to the MLEngine
          API, such as: ::

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

      :type job: dict

      :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.
      :type use_existing_job_fn: function

      :return: The MLEngine job object if the job successfully reach a
          terminal state (which might be FAILED or CANCELLED state).
      :rtype: dict



   
   .. method:: _get_job(self, project_id, job_id)

      Gets a MLEngine job based on the job name.

      :return: MLEngine job object if succeed.
      :rtype: dict

      Raises:
          googleapiclient.errors.HttpError: if HTTP error is returned from server



   
   .. method:: _wait_for_job_done(self, project_id, job_id, interval=30)

      Waits for the Job to reach a terminal state.

      This method will periodically check the job state until the job reach
      a terminal state.

      Raises:
          googleapiclient.errors.HttpError: if HTTP error is returned when getting
          the job



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

      Creates the Version on Google Cloud ML Engine.

      Returns the operation if the version was created successfully and
      raises an error otherwise.



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

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



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

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



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

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



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

      Create a Model. Blocks until finished.



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

      Gets a Model. Blocks until finished.