mlos_bench.environments.remote.RemoteEnv

class mlos_bench.environments.remote.RemoteEnv(*, name: str, config: dict, global_config: dict | None = None, tunables: TunableGroups | None = None, service: Service | None = None)

Environment to run benchmarks and scripts on a remote host OS.

e.g. Application Environment

Attributes:

parameters

Key/value pairs of all environment parameters (i.e., const_args and tunable_params).

tunable_params

Get the configuration space of the given environment.

Methods

new(*, env_name, class_name, config[, ...])	Factory method for a new environment with a given config.
pprint([indent, level])	Pretty-print the environment configuration.
run()	Runs the run script on the remote environment.
setup(tunables[, global_config])	Check if the environment is ready and set up the application and benchmarks on a remote host.
status()	Check the status of the benchmark environment.
teardown()	Clean up and shut down the remote environment.

__init__(*, name: str, config: dict, global_config: dict | None = None, tunables: TunableGroups | None = None, service: Service | None = None)

Create a new environment for remote execution.

Parameters:

name: str

Human-readable name of the environment.

configdict

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_configdict

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

tunablesTunableGroups

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[Status, datetime, Dict[str, int | float | str | None] | None]

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)(Status, datetime, dict)

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.

setup(tunables: TunableGroups, global_config: dict | None = None) → bool

Check if the environment is ready and set up the application and benchmarks on a remote host.

Parameters:

tunablesTunableGroups

A collection of tunable OS and application parameters along with their values. Setting these parameters should not require an OS reboot.

global_configdict

Free-format dictionary of global parameters of the environment that are not used in the optimization process.

Returns:

is_successbool

True if operation is successful, false otherwise.

teardown() → None

Clean up and shut down the remote environment.