ray.tune.search.Searcher#

class ray.tune.search.Searcher(metric: Optional[str] = None, mode: Optional[str] = None)[source]#

Bases: object

Abstract class for wrapping suggesting algorithms.

Custom algorithms can extend this class easily by overriding the suggest method provide generated parameters for the trials.

Any subclass that implements __init__ must also call the constructor of this class: super(Subclass, self).__init__(...).

To track suggestions and their corresponding evaluations, the method suggest will be passed a trial_id, which will be used in subsequent notifications.

Not all implementations support multi objectives.

Parameters

  • metric – The training result objective value attribute. If list then list of training result objective value attributes

  • mode – If string One of {min, max}. If list then list of max and min, determines whether objective is minimizing or maximizing the metric attribute. Must match type of metric.

class ExampleSearch(Searcher):
    def __init__(self, metric="mean_loss", mode="min", **kwargs):
        super(ExampleSearch, self).__init__(
            metric=metric, mode=mode, **kwargs)
        self.optimizer = Optimizer()
        self.configurations = {}

    def suggest(self, trial_id):
        configuration = self.optimizer.query()
        self.configurations[trial_id] = configuration

    def on_trial_complete(self, trial_id, result, **kwargs):
        configuration = self.configurations[trial_id]
        if result and self.metric in result:
            self.optimizer.update(configuration, result[self.metric])

tuner = tune.Tuner(
    trainable_function,
    tune_config=tune.TuneConfig(
        search_alg=ExampleSearch()
    )
)
tuner.fit()

DeveloperAPI: This API may change across minor Ray releases.

Methods

add_evaluated_point(parameters, value[, ...])

Pass results from a point that has been evaluated separately.

add_evaluated_trials(trials_or_analysis, metric)

Pass results from trials that have been evaluated separately.

on_trial_complete(trial_id[, result, error])

Notification for the completion of trial.

on_trial_result(trial_id, result)

Optional notification for result during training.

restore(checkpoint_path)

Restore state for this search algorithm

restore_from_dir(checkpoint_dir)

Restores the state of a searcher from a given checkpoint_dir.

save(checkpoint_path)

Save state to path for this search algorithm.

save_to_dir(checkpoint_dir[, session_str])

Automatically saves the given searcher to the checkpoint_dir.

set_max_concurrency(max_concurrent)

Set max concurrent trials this searcher can run.

set_search_properties(metric, mode, config, ...)

Pass search properties to searcher.

suggest(trial_id)

Queries the algorithm to retrieve the next set of parameters.

Attributes

CKPT_FILE_TMPL

FINISHED

metric

The training result objective value attribute.

mode

Specifies if minimizing or maximizing the metric.