mlos_bench.environments.local.local_env

Scheduler-side benchmark environment to run scripts locally.

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

Classes

LocalEnv

Scheduler-side Environment that runs scripts locally.

Module Contents

class mlos_bench.environments.local.local_env.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: mlos_bench.environments.script_env.ScriptEnv

Scheduler-side Environment that runs scripts locally.

Create a new environment for local execution.

Parameters:

• name (str) – Human-readable name of the environment.

• config (dict) – 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}

• global_config (dict) – Free-format dictionary of global parameters (e.g., security credentials) to be mixed in into the “const_args” section of the local config.

• tunables (TunableGroups) – A collection of tunable parameters for all environments.

• service (Service) – An optional service object (e.g., providing methods to deploy or reboot a VM, etc.).

__enter__() → mlos_bench.environments.base_environment.Environment

Enter the environment’s benchmarking context.

Return type:

mlos_bench.environments.base_environment.Environment

__exit__(ex_type: Type[BaseException] | None, ex_val: BaseException | None, ex_tb: types.TracebackType | None) → Literal[False]

Exit the context of the benchmarking environment.

Parameters:

• ex_type (Optional[Type[BaseException]])

• ex_val (Optional[BaseException])

• ex_tb (Optional[types.TracebackType])

Return type:

Literal[False]

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.

Return type:

(Status, datetime.datetime, dict)

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.

Parameters:

• tunables (TunableGroups) – 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.

• global_config (dict) – Free-format dictionary of global parameters of the environment that are not used in the optimization process.

Returns:

is_success – True if operation is successful, false otherwise.

Return type:

bool

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.

Return type:

(Status, datetime.datetime, list)

teardown() → None

Clean up the local environment.

Return type:

None