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: Union[int, ray.tune.utils.log.Verbosity] = Verbosity.V3_TRIAL_DETAILS, progress_reporter: Optional[ray.tune.progress_reporter.ProgressReporter] = None, resume: Union[bool, str] = False, reuse_actors: Optional[bool] = None, trial_executor: Optional[ray.tune.execution.ray_trial_executor.RayTrialExecutor] = None, raise_on_failed_trial: bool = True, concurrent: bool = True, callbacks: Optional[Sequence[ray.tune.callback.Callback]] = None, _remote: Optional[bool] = None)

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.