ray.tune.search.Searcher — Ray 2.46.0 (original) (raw)

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

Abstract class for wrapping suggesting algorithms.

Custom algorithms can extend this class easily by overriding thesuggest 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 methodsuggest will be passed a trial_id, which will be used in subsequent notifications.

Not all implementations support multi objectives.

Note to Tune developers: If a new searcher is added, please updateair/_internal/usage.py.

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

Attributes