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

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

.. autoapi-nested-parse::

   This module contains Google Vertex AI operators.

   .. spelling::

       irreproducible
       codepoints
       Tensorboard
       aiplatform
       myVPC


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job.CreateHyperparameterTuningJobOperator
   airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job.GetHyperparameterTuningJobOperator
   airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job.DeleteHyperparameterTuningJobOperator
   airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job.ListHyperparameterTuningJobOperator


.. py:class:: CreateHyperparameterTuningJobOperator(*, 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, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

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

   Create Hyperparameter Tuning job

   :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.
   :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:: GetHyperparameterTuningJobOperator(*, region, project_id, hyperparameter_tuning_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`

   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_id: 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.
   :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', 'hyperparameter_tuning_job_id', '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:: DeleteHyperparameterTuningJobOperator(*, hyperparameter_tuning_job_id, region, project_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 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_id: 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.

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'hyperparameter_tuning_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:: ListHyperparameterTuningJobOperator(*, region, project_id, page_size = None, page_token = None, filter = 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 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: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.