Source code for ax.utils.testing.backend_scheduler
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from __future__ import annotations
from dataclasses import replace as dataclass_replace
from logging import Logger
from typing import Dict, Optional, Set
from ax.core.experiment import Experiment
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.runners.simulated_backend import SimulatedBackendRunner
from ax.service.scheduler import Scheduler, SchedulerOptions
from ax.utils.common.logger import get_logger
from ax.utils.testing.backend_simulator import BackendSimulator
logger: Logger = get_logger(__name__)
[docs]class AsyncSimulatedBackendScheduler(Scheduler):
"""A Scheduler that uses a simulated backend for Ax asynchronous benchmarks."""
def __init__(
self,
experiment: Experiment,
generation_strategy: GenerationStrategy,
max_pending_trials: int,
options: SchedulerOptions,
) -> None:
"""A Scheduler for Ax asynchronous benchmarks.
Args:
experiment: Experiment, in which results of the optimization
will be recorded.
generation_strategy: Generation strategy for the optimization,
describes models that will be used in optimization.
max_pending_trials: The maximum number of pending trials allowed.
options: `SchedulerOptions` for this Scheduler instance.
"""
if not isinstance(experiment.runner, SimulatedBackendRunner):
raise ValueError(
"experiment must have runner of type SimulatedBackendRunner attached"
)
if (
options.max_pending_trials is not None
and options.max_pending_trials != max_pending_trials
):
raise ValueError(
f"`SchedulerOptions.max_pending_trials`: {options.max_pending_trials} "
f"does not match argument to `Scheduler`: {max_pending_trials}."
)
if options.max_pending_trials is None:
options = dataclass_replace(options, max_pending_trials=max_pending_trials)
super().__init__(
experiment=experiment,
generation_strategy=generation_strategy,
options=options,
_skip_experiment_save=True,
)
@property
def backend_simulator(self) -> BackendSimulator:
"""Get the ``BackendSimulator`` stored on the runner of the experiment.
Returns:
The backend simulator.
"""
return self.runner.simulator # pyre-ignore[16]
[docs] def should_stop_trials_early(
self, trial_indices: Set[int]
) -> Dict[int, Optional[str]]:
"""Given a set of trial indices, decide whether or not to early-stop
running trials using the ``early_stopping_strategy``.
Args:
trial_indices: Indices of trials to consider for early stopping.
Returns:
Dict with new suggested ``TrialStatus`` as keys and a set of
indices of trials to update (subset of initially-passed trials) as values.
"""
# TODO: The status on the experiment does not distinguish between
# running and queued trials, so here we check status on the
# ``backend_simulator`` directly to make sure it is running.
running_trials = set()
skipped_trials = set()
for trial_index in trial_indices:
sim_trial = self.backend_simulator.get_sim_trial_by_index(trial_index)
if sim_trial.sim_start_time is not None and ( # pyre-ignore[16]
self.backend_simulator.time - sim_trial.sim_start_time > 0
):
running_trials.add(trial_index)
else:
skipped_trials.add(trial_index)
if len(skipped_trials) > 0:
logger.info(
f"Not sending {skipped_trials} to base `should_stop_trials_early` "
"because they have not been running for a positive amount of time "
"on the backend simulator."
)
return super().should_stop_trials_early(trial_indices=running_trials)