:py:mod:`airflow.providers.google.cloud.hooks.vertex_ai.hyperparameter_tuning_job`
==================================================================================

.. py:module:: airflow.providers.google.cloud.hooks.vertex_ai.hyperparameter_tuning_job

.. autoapi-nested-parse::

   This module contains a Google Cloud Vertex AI hook.

   .. spelling::

       irreproducible
       codepoints
       Tensorboard
       aiplatform
       myVPC


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.hooks.vertex_ai.hyperparameter_tuning_job.HyperparameterTuningJobHook


.. py:class:: HyperparameterTuningJobHook(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 Cloud Vertex AI Hyperparameter Tuning Job APIs.

   .. py:method:: get_job_service_client(self, region = None)

      Returns JobServiceClient.


   .. py:method:: get_hyperparameter_tuning_job_object(self, display_name, custom_job, metric_spec, parameter_spec, max_trial_count, parallel_trial_count, max_failed_trial_count = 0, search_algorithm = None, measurement_selection = 'best', project = None, location = None, labels = None, encryption_spec_key_name = None)

      Returns HyperparameterTuningJob object


   .. py:method:: get_custom_job_object(self, display_name, worker_pool_specs, base_output_dir = None, project = None, location = None, labels = None, encryption_spec_key_name = None, staging_bucket = None)

      Returns CustomJob object


   .. py:method:: extract_hyperparameter_tuning_job_id(obj)
      :staticmethod:

      Returns unique id of the hyperparameter_tuning_job.


   .. py:method:: wait_for_operation(self, operation, timeout = None)

      Waits for long-lasting operation to complete.


   .. py:method:: cancel_hyperparameter_tuning_job(self)

      Cancel HyperparameterTuningJob


   .. py:method:: create_hyperparameter_tuning_job(self, project_id, region, display_name, metric_spec, parameter_spec, max_trial_count, parallel_trial_count, worker_pool_specs, base_output_dir = None, custom_job_labels = None, custom_job_encryption_spec_key_name = None, staging_bucket = None, max_failed_trial_count = 0, search_algorithm = None, measurement_selection = 'best', hyperparameter_tuning_job_labels = None, hyperparameter_tuning_job_encryption_spec_key_name = None, service_account = None, network = None, timeout = None, restart_job_on_worker_restart = False, enable_web_access = False, tensorboard = None, sync = True)

      Create a HyperparameterTuningJob.

      :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 display_name: Required. The user-defined name of the HyperparameterTuningJob. The name can be
          up to 128 characters long and can be consist of any UTF-8 characters.
      :param metric_spec: Required. Dictionary representing metrics to optimize. The dictionary key is the
          metric_id, which is reported by your training job, and the dictionary value is the optimization
          goal of the metric('minimize' or 'maximize').
          example: metric_spec = {'loss': 'minimize', 'accuracy': 'maximize'}
      :param parameter_spec: Required. Dictionary representing parameters to optimize. The dictionary key
          is the metric_id, which is passed into your training job as a command line key word argument, and
          the dictionary value is the parameter specification of the metric.
      :param max_trial_count: Required. The desired total number of Trials.
      :param parallel_trial_count: Required. The desired number of Trials to run in parallel.
      :param worker_pool_specs: Required. The spec of the worker pools including machine type and Docker
          image. Can provided as a list of dictionaries or list of WorkerPoolSpec proto messages.
      :param base_output_dir: Optional. GCS output directory of job. If not provided a timestamped
          directory in the staging directory will be used.
      :param custom_job_labels: Optional. The labels with user-defined metadata to organize CustomJobs.
          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 custom_job_encryption_spec_key_name: Optional.Customer-managed encryption key name for a
          CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the
          provided encryption key.
      :param staging_bucket: Optional. Bucket for produced custom job artifacts. Overrides staging_bucket
          set in aiplatform.init.
      :param max_failed_trial_count: Optional. The number of failed Trials that need to be seen before
          failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail
          before the whole job fails.
      :param search_algorithm: The search algorithm specified for the Study. Accepts one of the following:
          `None` - If you do not specify an algorithm, your job uses the default Vertex AI algorithm. The
          default algorithm applies Bayesian optimization to arrive at the optimal solution with a more
          effective search over the parameter space.

          'grid' - A simple grid search within the feasible space. This option is particularly useful if
          you want to specify a quantity of trials that is greater than the number of points in the
          feasible space. In such cases, if you do not specify a grid search, the Vertex AI default
          algorithm may generate duplicate suggestions. To use grid search, all parameter specs must be of
          type `IntegerParameterSpec`, `CategoricalParameterSpace`, or `DiscreteParameterSpec`.

          'random' - A simple random search within the feasible space.
      :param measurement_selection: This indicates which measurement to use if/when the service
          automatically selects the final measurement from previously reported intermediate measurements.
          Accepts: 'best', 'last'
          Choose this based on two considerations:
          A) Do you expect your measurements to monotonically improve? If so, choose 'last'. On the other
          hand, if you're in a situation where your system can "over-train" and you expect the performance
          to get better for a while but then start declining, choose 'best'.
          B) Are your measurements significantly noisy and/or irreproducible? If so, 'best' will tend to be
          over-optimistic, and it may be better to choose 'last'.
          If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen.
      :param hyperparameter_tuning_job_labels: Optional. The labels with user-defined metadata to organize
          HyperparameterTuningJobs. 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 hyperparameter_tuning_job_encryption_spec_key_name: Optional. Customer-managed encryption key
          options for a HyperparameterTuningJob. If this is set, then all resources created by the
          HyperparameterTuningJob will be encrypted with the provided encryption key.
      :param service_account: Optional. Specifies the service account for workload run-as account. Users
          submitting jobs must have act-as permission on this run-as account.
      :param network: Optional. The full name of the Compute Engine network to which the job should be
          peered. For example, projects/12345/global/networks/myVPC. Private services access must already
          be configured for the network. If left unspecified, the job is not peered with any network.
      :param timeout: The maximum job running time in seconds. The default is 7 days.
      :param restart_job_on_worker_restart: Restarts the entire CustomJob if a worker gets restarted. This
          feature can be used by distributed training jobs that are not resilient to workers leaving and
          joining a job.
      :param enable_web_access: Whether you want Vertex AI to enable interactive shell access to training
          containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell
      :param tensorboard: Optional. The name of a Vertex AI
          [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to which this CustomJob will
          upload Tensorboard logs. Format:
          ``projects/{project}/locations/{location}/tensorboards/{tensorboard}`` The training script should
          write Tensorboard to following Vertex AI environment variable: AIP_TENSORBOARD_LOG_DIR
          `service_account` is required with provided `tensorboard`. For more information on configuring
          your service account please visit:
          https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training
      :param sync: Whether to execute this method synchronously. If False, this method will unblock and it
          will be executed in a concurrent Future.


   .. py:method:: get_hyperparameter_tuning_job(self, project_id, region, hyperparameter_tuning_job, retry = DEFAULT, timeout = None, metadata = ())

      Gets a HyperparameterTuningJob

      :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 hyperparameter_tuning_job: Required. The name of the HyperparameterTuningJob 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.


   .. py:method:: list_hyperparameter_tuning_jobs(self, project_id, region, filter = None, page_size = None, page_token = None, read_mask = None, retry = DEFAULT, timeout = None, metadata = ())

      Lists HyperparameterTuningJobs 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.


   .. py:method:: delete_hyperparameter_tuning_job(self, project_id, region, hyperparameter_tuning_job, retry = DEFAULT, timeout = None, metadata = ())

      Deletes a HyperparameterTuningJob.

      :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 hyperparameter_tuning_job: Required. The name of the HyperparameterTuningJob 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.