ray.tune.run_experiments
ray.tune.run_experiments#
- ray.tune.run_experiments(experiments: Union[ray.tune.experiment.experiment.Experiment, Mapping, Sequence[Union[ray.tune.experiment.experiment.Experiment, Mapping]]], scheduler: Optional[ray.tune.schedulers.trial_scheduler.TrialScheduler] = None, server_port: Optional[int] = None, verbose: Optional[Union[int, ray.tune.experimental.output.AirVerbosity, ray.tune.utils.log.Verbosity]] = None, progress_reporter: Optional[ray.tune.progress_reporter.ProgressReporter] = None, resume: Union[bool, str] = False, reuse_actors: Optional[bool] = None, raise_on_failed_trial: bool = True, concurrent: bool = True, callbacks: Optional[Sequence[ray.tune.callback.Callback]] = None, _remote: Optional[bool] = None)[source]#
Runs and blocks until all trials finish.
Example
>>> from ray.tune.experiment import Experiment >>> from ray.tune.tune import run_experiments >>> def my_func(config): return {"score": 0} >>> experiment_spec = Experiment("experiment", my_func) >>> run_experiments(experiments=experiment_spec) >>> experiment_spec = {"experiment": {"run": my_func}} >>> run_experiments(experiments=experiment_spec)
- Returns
List of Trial objects, holding data for each executed trial.
PublicAPI: This API is stable across Ray releases.