syne_tune.optimizer.schedulers.ray_scheduler module

class syne_tune.optimizer.schedulers.ray_scheduler.RayTuneScheduler(config_space, ray_scheduler=None, ray_searcher=None, points_to_evaluate=None)

Bases: TrialScheduler

Allow using Ray scheduler and searcher. Any searcher/scheduler should work, except such which need access to TrialRunner (e.g., PBT), this feature is not implemented in Syne Tune.

If ray_searcher is not given (defaults to random searcher), initial configurations to evaluate can be passed in points_to_evaluate. If ray_searcher is given, this argument is ignored (needs to be passed to ray_searcher at construction). Note: Use impute_points_to_evaluate() in order to preprocess points_to_evaluate specified by the user or the benchmark.

Parameters:

• config_space (Dict) – Configuration space

• ray_scheduler – Ray scheduler, defaults to FIFO scheduler

• ray_searcher (Optional[Searcher]) – Ray searcher, defaults to random search

• points_to_evaluate (Optional[List[Dict]]) – See above

RT_FIFOScheduler

alias of FIFOScheduler

RT_Searcher

alias of Searcher

class RandomSearch(config_space, points_to_evaluate, mode)

Bases: Searcher

suggest(trial_id)

Queries the algorithm to retrieve the next set of parameters.

Return type:

Optional[Dict]

Arguments:

trial_id: Trial ID used for subsequent notifications.

Returns:

dict | FINISHED | None: Configuration for a trial, if possible.

If FINISHED is returned, Tune will be notified that no more suggestions/configurations will be provided. If None is returned, Tune will skip the querying of the searcher for this step.

on_trial_complete(trial_id, result=None, error=False)

Notification for the completion of trial.

Typically, this method is used for notifying the underlying optimizer of the result.

Args:

trial_id: A unique string ID for the trial. result: Dictionary of metrics for current training progress.

Note that the result dict may include NaNs or may not include the optimization metric. It is up to the subclass implementation to preprocess the result to avoid breaking the optimization process. Upon errors, this may also be None.

error: True if the training process raised an error.

on_trial_add(trial)

Called when a new trial is added to the trial runner.

Additions are normally triggered by suggest.

Parameters:

trial (Trial) – Trial to be added

on_trial_error(trial)

Called when a trial has failed.

Parameters:

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

on_trial_result(trial, result)

Called on each intermediate result reported by a trial.

At this point, the trial scheduler can make a decision by returning one of SchedulerDecision.CONTINUE, SchedulerDecision.PAUSE, or SchedulerDecision.STOP. This will only be called when the trial is currently running.

Parameters:

• trial (Trial) – Trial for which results are reported

• result (Dict) – Result dictionary

Return type:

str

Returns:

Decision what to do with the trial

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) – Result dictionary

on_trial_remove(trial)

Called to remove trial.

This is called when the trial is in PAUSED or PENDING state. Otherwise, call on_trial_complete().

Parameters:

trial (Trial) – Trial to be removed

metric_names()

Return type:

List[str]

Returns:

List of metric names. The first one is the target metric optimized over, unless the scheduler is a genuine multi-objective metric (for example, for sampling the Pareto front)

metric_mode()

Return type:

str

Returns:

“min” if target metric is minimized, otherwise “max”. Here, “min” should be the default. For a genuine multi-objective scheduler, a list of modes is returned

static convert_config_space(config_space)

Converts config_space from our type to the one of Ray Tune.

Note: randint(lower, upper) in Ray Tune has exclusive upper, while this is inclusive for us. On the other hand, lograndint(lower, upper) has inclusive upper in Ray Tune as well.

Parameters:

config_space – Configuration space

Returns:

config_space converted into Ray Tune type