mlos_bench.environments.remote.remote_env
Remotely executed benchmark/script environment.
e.g. Application Environment
TODO: Documentat how variable propogation works in the remote environments.
Classes
Environment to run benchmarks and scripts on a remote host OS. |
Module Contents
- class mlos_bench.environments.remote.remote_env.RemoteEnv(*, 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)[source]
Bases:
mlos_bench.environments.script_env.ScriptEnvEnvironment to run benchmarks and scripts on a remote host OS.
e.g. Application Environment
Create a new environment for remote 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. RemoteEnv must also have at least some of the following parameters: {setup, run, teardown, wait_boot}
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 Host, VM, OS, etc.).
- run() Tuple[mlos_bench.environments.status.Status, datetime.datetime, Dict[str, mlos_bench.tunables.tunable.TunableValue] | None][source]
Runs the run script on the remote environment.
This can be used to, for instance, submit a new experiment to the remote application environment by (re)configuring an application and launching the benchmark, or run a script that collects the results.
- 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:
- setup(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, global_config: dict | None = None) bool[source]
Check if the environment is ready and set up the application and benchmarks on a remote host.
- Parameters:
tunables (TunableGroups) – A collection of tunable OS and application parameters along with their values. Setting these parameters should not require an OS reboot.
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: