mlos_bench.environments.local.local_env
=======================================

.. py:module:: mlos_bench.environments.local.local_env

.. autoapi-nested-parse::

   Scheduler-side benchmark environment to run scripts locally.

   TODO: Reference the script_env.py file for the base class.



Classes
-------

.. autoapisummary::

   mlos_bench.environments.local.local_env.LocalEnv


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

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

   Bases: :py:obj:`mlos_bench.environments.script_env.ScriptEnv`


   Scheduler-side Environment that runs scripts locally.

   Create a new environment for local execution.

   :param name: Human-readable name of the environment.
   :type name: str
   :param config: Free-format dictionary that contains the benchmark environment
                  configuration. Each config must have at least the "tunable_params"
                  and the "const_args" sections.
                  `LocalEnv` must also have at least some of the following parameters:
                  {setup, run, teardown, dump_params_file, read_results_file}
   :type config: dict
   :param global_config: Free-format dictionary of global parameters (e.g., security credentials)
                         to be mixed in into the "const_args" section of the local config.
   :type global_config: dict
   :param tunables: A collection of tunable parameters for *all* environments.
   :type tunables: TunableGroups
   :param service: An optional service object (e.g., providing methods to
                   deploy or reboot a VM, etc.).
   :type service: Service


   .. py:method:: __enter__() -> mlos_bench.environments.base_environment.Environment

      Enter the environment's benchmarking context.



   .. py:method:: __exit__(ex_type: type[BaseException] | None, ex_val: BaseException | None, ex_tb: types.TracebackType | None) -> Literal[False]

      Exit the context of the benchmarking environment.



   .. py:method:: run() -> tuple[mlos_bench.environments.status.Status, datetime.datetime, dict[str, mlos_bench.tunables.tunable.TunableValue] | None]

      Run a script in the local scheduler environment.

      :returns: **(status, timestamp, output)** -- 3-tuple of (Status, timestamp, output) values, where `output` is a dict
                with the results or None if the status is not COMPLETED.
                If run script is a benchmark, then the score is usually expected to
                be in the `score` field.
      :rtype: (Status, datetime.datetime, dict)



   .. py:method:: setup(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, global_config: dict | None = None) -> bool

      Check if the environment is ready and set up the application and benchmarks, if
      necessary.

      :param tunables: A collection of tunable OS and application parameters along with their
                       values. In a local environment these could be used to prepare a config
                       file on the scheduler prior to transferring it to the remote environment,
                       for instance.
      :type tunables: TunableGroups
      :param global_config: Free-format dictionary of global parameters of the environment
                            that are not used in the optimization process.
      :type global_config: dict

      :returns: **is_success** -- True if operation is successful, false otherwise.
      :rtype: bool



   .. py:method:: status() -> tuple[mlos_bench.environments.status.Status, datetime.datetime, list[tuple[datetime.datetime, str, Any]]]

      Check the status of the benchmark environment.

      :returns: **(benchmark_status, timestamp, telemetry)** -- 3-tuple of (benchmark status, timestamp, telemetry) values.
                `timestamp` is UTC time stamp of the status; it's current time by default.
                `telemetry` is a list (maybe empty) of (timestamp, metric, value) triplets.
      :rtype: (Status, datetime.datetime, list)



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

      Clean up the local environment.