syne_tune.optimizer.schedulers.scheduler_searcher module

class syne_tune.optimizer.schedulers.scheduler_searcher.TrialSchedulerWithSearcher(config_space, **kwargs)

Bases: LegacyTrialScheduler

Base class for trial schedulers which have a BaseSearcher member searcher. This searcher has a method configure_scheduler() which has to be called before the searcher is first used.

We also collect common code here:

• Determine max_resource_level if not explicitly given

• Master seed, random_seed_generator

property searcher: LegacyBaseSearcher | None

suggest(trial_id)

Returns a suggestion for a new trial, or one to be resumed

This method returns suggestion of type TrialSuggestion (unless there is no config left to explore, and None is returned).

If suggestion.spawn_new_trial_id is True, a new trial is to be started with config suggestion.config. Typically, this new trial is started from scratch. But if suggestion.checkpoint_trial_id is given, the trial is to be (warm)started from the checkpoint written for the trial with this ID. The new trial has ID trial_id.

If suggestion.spawn_new_trial_id is False, an existing and currently paused trial is to be resumed, whose ID is suggestion.checkpoint_trial_id. If this trial has a checkpoint, we start from there. In this case, suggestion.config is optional. If not given (default), the config of the resumed trial does not change. Otherwise, its config is overwritten by suggestion.config (see HyperbandScheduler with type="promotion" for an example why this can be useful).

Apart from the HP config, additional fields can be appended to the dict, these are passed to the trial function as well.

Parameters:

trial_id (int) – ID for new trial to be started (ignored if existing trial to be resumed)

Return type:

Optional[TrialSuggestion]

Returns:

Suggestion for a trial to be started or to be resumed, see above. If no suggestion can be made, None is returned

on_trial_error(trial)

Called when a trial has failed.

Parameters:

trial (Trial) – Trial for which error is reported.

on_trial_complete(trial, result)

Notification for the completion of trial.

Note that on_trial_result() is called with the same result before. However, if the scheduler only uses one final report from each trial, it may ignore on_trial_result() and just use result here.

Parameters:

• trial (Trial) – Trial which is completing

• result (Dict[str, Any]) – Result dictionary