mlos_bench.optimizers.one_shot_optimizer
========================================

.. py:module:: mlos_bench.optimizers.one_shot_optimizer

.. autoapi-nested-parse::

   No-op optimizer for mlos_bench that proposes a single configuration.



Classes
-------

.. autoapisummary::

   mlos_bench.optimizers.one_shot_optimizer.OneShotOptimizer


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

.. py:class:: OneShotOptimizer(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, config: dict, global_config: dict | None = None, service: mlos_bench.services.base_service.Service | None = None)

   Bases: :py:obj:`mlos_bench.optimizers.mock_optimizer.MockOptimizer`


   No-op optimizer that proposes a single configuration and returns.

   Explicit configs (partial or full) are possible using configuration files.

   Create a new optimizer for the given configuration space defined by the
   tunables.

   :param tunables: The tunables to optimize.
   :type tunables: TunableGroups
   :param config: Free-format key/value pairs of configuration parameters to pass to the optimizer.
   :type config: dict
   :param global_config:
   :type global_config: dict | None
   :param service:
   :type service: Service | None


   .. py:method:: suggest() -> mlos_bench.tunables.tunable_groups.TunableGroups

      Always produce the same (initial) suggestion.



   .. py:property:: supports_preload
      :type: bool


      Return True if the optimizer supports pre-loading the data from previous
      experiments.