mlos_bench.schedulers.sync_scheduler
====================================

.. py:module:: mlos_bench.schedulers.sync_scheduler

.. autoapi-nested-parse::

   A simple single-threaded synchronous optimization loop implementation.



Classes
-------

.. autoapisummary::

   mlos_bench.schedulers.sync_scheduler.SyncScheduler


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

.. py:class:: SyncScheduler(*, config: dict[str, Any], global_config: dict[str, Any], trial_runners: collections.abc.Iterable[mlos_bench.schedulers.trial_runner.TrialRunner], optimizer: mlos_bench.optimizers.base_optimizer.Optimizer, storage: mlos_bench.storage.base_storage.Storage, root_env_config: str)

   Bases: :py:obj:`mlos_bench.schedulers.base_scheduler.Scheduler`


   A simple single-threaded synchronous optimization loop implementation.

   Create a new instance of the scheduler. The constructor of this and the derived
   classes is called by the persistence service after reading the class JSON
   configuration. Other objects like the TrialRunner(s) and their Environment(s)
   and Optimizer are provided by the Launcher.

   :param config: The configuration for the Scheduler.
   :type config: dict
   :param global_config: The global configuration for the Experiment.
   :type global_config: dict
   :param trial_runner: The set of TrialRunner(s) (and associated Environment(s)) to benchmark/optimize.
   :type trial_runner: Iterable[TrialRunner]
   :param optimizer: The Optimizer to use.
   :type optimizer: Optimizer
   :param storage: The Storage to use.
   :type storage: Storage
   :param root_env_config: Path to the root Environment configuration.
   :type root_env_config: str


   .. py:method:: run_trial(trial: mlos_bench.storage.base_storage.Storage.Trial) -> None

      Set up and run a single trial.

      Save the results in the storage.



   .. py:method:: start() -> None

      Start the optimization loop.