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https://github.com/wassname/ray.git
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Richard Liaw
62d0698097
This PR introduces the following changes: * Ray Tune -> Tune * [breaking] Creation of `schedulers/`, moving PBT, HyperBand into a submodule * [breaking] Search Algorithms now must take in experiment configurations via `add_configurations` rather through initialization * Support `"run": (function | class | str)` with automatic registering of trainable * Documentation Changes
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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class SearchAlgorithm(object):
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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.suggest.BasicVariantGenerator`.
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"""
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def add_configurations(self, experiments):
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"""Tracks given experiment specifications.
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Arguments:
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experiments (Experiment | list | dict): Experiments to run.
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"""
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raise NotImplementedError
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def next_trials(self):
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"""Provides Trial objects to be queued into the TrialRunner.
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Returns:
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trials (list): Returns a list of trials.
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"""
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raise NotImplementedError
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def on_trial_result(self, trial_id, result):
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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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"""
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pass
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def on_trial_complete(self,
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trial_id,
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result=None,
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error=False,
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early_terminated=False):
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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 (dict): 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 (bool): Defaults to False. True if the trial is in
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the RUNNING state and errors.
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early_terminated (bool): Defaults to False. True if the trial
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is stopped while in PAUSED or PENDING state.
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"""
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pass
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def is_finished(self):
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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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raise NotImplementedError
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