wehub-resource-sync
131 lines
4.0 KiB
Python
131 lines
4.0 KiB
Python
from typing import TYPE_CHECKING, Dict, List, Optional, Union
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from ray.util.annotations import DeveloperAPI
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if TYPE_CHECKING:
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from ray.tune.experiment import Experiment, Trial
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@DeveloperAPI
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class SearchAlgorithm:
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"""Interface of an event handler API for hyperparameter search.
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Unlike TrialSchedulers, SearchAlgorithms will not have the ability
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to modify the execution (i.e., stop and pause trials).
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Trials added manually (i.e., via the Client API) will also notify
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this class upon new events, so custom search algorithms should
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maintain a list of trials ID generated from this class.
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See also: `ray.tune.search.BasicVariantGenerator`.
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"""
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_finished = False
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_metric = None
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@property
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def metric(self):
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return self._metric
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def set_search_properties(
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self, metric: Optional[str], mode: Optional[str], config: Dict, **spec
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) -> bool:
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"""Pass search properties to search algorithm.
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This method acts as an alternative to instantiating search algorithms
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with their own specific search spaces. Instead they can accept a
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Tune config through this method.
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The search algorithm will usually pass this method to their
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``Searcher`` instance.
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Args:
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metric: Metric to optimize
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mode: One of ["min", "max"]. Direction to optimize.
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config: Tune config dict.
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**spec: Any kwargs for forward compatibility.
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Info like Experiment.PUBLIC_KEYS is provided through here.
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Returns:
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True if the search properties were set successfully, False otherwise.
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"""
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if self._metric and metric:
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return False
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if metric:
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self._metric = metric
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return True
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@property
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def total_samples(self):
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"""Get number of total trials to be generated"""
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return 0
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def add_configurations(
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self, experiments: Union["Experiment", List["Experiment"], Dict[str, Dict]]
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):
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"""Tracks given experiment specifications.
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Arguments:
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experiments: Experiments to run.
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"""
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raise NotImplementedError
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def next_trial(self) -> Optional["Trial"]:
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"""Returns single Trial object to be queued into the TrialRunner.
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Returns:
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trial: Returns a Trial object.
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"""
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raise NotImplementedError
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def on_trial_result(self, trial_id: str, result: Dict):
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"""Called on each intermediate result returned by a trial.
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This will only be called when the trial is in the RUNNING state.
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Arguments:
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trial_id: Identifier for the trial.
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result: Result dictionary.
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"""
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pass
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def on_trial_complete(
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self, trial_id: str, result: Optional[Dict] = None, error: bool = False
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):
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"""Notification for the completion of trial.
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Arguments:
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trial_id: Identifier for the trial.
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result: Defaults to None. A dict will
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be provided with this notification when the trial is in
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the RUNNING state AND either completes naturally or
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by manual termination.
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error: Defaults to False. True if the trial is in
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the RUNNING state and errors.
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"""
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pass
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def is_finished(self) -> bool:
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"""Returns True if no trials left to be queued into TrialRunner.
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Can return True before all trials have finished executing.
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"""
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return self._finished
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def set_finished(self):
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"""Marks the search algorithm as finished."""
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self._finished = True
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def has_checkpoint(self, dirpath: str) -> bool:
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"""Should return False if restoring is not implemented."""
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return False
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def save_to_dir(self, dirpath: str, **kwargs):
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"""Saves a search algorithm."""
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pass
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def restore_from_dir(self, dirpath: str):
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"""Restores a search algorithm along with its wrapped state."""
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pass
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