wehub-resource-sync
100 lines
3.0 KiB
Python
100 lines
3.0 KiB
Python
import abc
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from typing import Any, Dict
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from ray.util.annotations import PublicAPI
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@PublicAPI
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class Stopper(abc.ABC):
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"""Base class for implementing a Tune experiment stopper.
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Allows users to implement experiment-level stopping via ``stop_all``. By
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default, this class does not stop any trials. Subclasses need to
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implement ``__call__`` and ``stop_all``.
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Examples:
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>>> import time
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>>> from ray import tune
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>>> from ray.tune import Stopper
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>>>
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>>> class TimeStopper(Stopper):
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... def __init__(self):
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... self._start = time.time()
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... self._deadline = 2 # Stop all trials after 2 seconds
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...
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... def __call__(self, trial_id, result):
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... return False
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...
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... def stop_all(self):
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... return time.time() - self._start > self._deadline
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...
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>>> def train_fn(config):
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... for i in range(100):
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... time.sleep(1)
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... tune.report({"iter": i})
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...
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>>> tuner = tune.Tuner(
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... train_fn,
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... tune_config=tune.TuneConfig(num_samples=2),
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... run_config=tune.RunConfig(stop=TimeStopper()),
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... )
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>>> print("[ignore]"); result_grid = tuner.fit() # doctest: +ELLIPSIS
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[ignore]...
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"""
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def __call__(self, trial_id: str, result: Dict[str, Any]) -> bool:
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"""Returns true if the trial should be terminated given the result."""
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raise NotImplementedError
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def stop_all(self) -> bool:
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"""Returns true if the experiment should be terminated."""
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raise NotImplementedError
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@PublicAPI
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class CombinedStopper(Stopper):
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"""Combine several stoppers via 'OR'.
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Args:
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*stoppers: Stoppers to be combined.
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Examples:
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>>> import numpy as np
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>>> from ray import tune
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>>> from ray.tune.stopper import (
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... CombinedStopper,
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... MaximumIterationStopper,
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... TrialPlateauStopper,
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... )
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>>>
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>>> stopper = CombinedStopper(
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... MaximumIterationStopper(max_iter=10),
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... TrialPlateauStopper(metric="my_metric"),
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... )
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>>> def train_fn(config):
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... for i in range(15):
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... tune.report({"my_metric": np.random.normal(0, 1 - i / 15)})
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...
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>>> tuner = tune.Tuner(
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... train_fn,
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... run_config=tune.RunConfig(stop=stopper),
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... )
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>>> print("[ignore]"); result_grid = tuner.fit() # doctest: +ELLIPSIS
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[ignore]...
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>>> all(result.metrics["training_iteration"] <= 20 for result in result_grid)
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True
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"""
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def __init__(self, *stoppers: Stopper):
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self._stoppers = stoppers
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def __call__(self, trial_id: str, result: Dict[str, Any]) -> bool:
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return any(s(trial_id, result) for s in self._stoppers)
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def stop_all(self) -> bool:
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return any(s.stop_all() for s in self._stoppers)
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