ray.tune.Tuner

class ray.tune.Tuner(trainable: str | Callable | Type[Trainable] | BaseTrainer | None = None, *, param_space: Dict[str, Any] | None = None, tune_config: TuneConfig | None = None, run_config: RunConfig | None = None, _tuner_kwargs: Dict | None = None, _tuner_internal: TunerInternal | None = None, _entrypoint: AirEntrypoint = AirEntrypoint.TUNER)

Tuner is the recommended way of launching hyperparameter tuning jobs with Ray Tune.

Parameters:

• trainable – The trainable to be tuned.

• param_space – Search space of the tuning job. See Working with Tune Search Spaces.

• tune_config – Tuning specific configs, such as setting custom search algorithms and trial scheduling algorithms.

• run_config – Job-level run configuration, which includes configs for persistent storage, checkpointing, fault tolerance, etc.

Usage pattern:

import ray.tune

def trainable(config):
    # Your training logic here
    ray.tune.report({"accuracy": 0.8})

tuner = Tuner(
    trainable=trainable,
    param_space={"lr": ray.tune.grid_search([0.001, 0.01])},
    run_config=ray.tune.RunConfig(name="my_tune_run"),
)
results = tuner.fit()

To retry a failed Tune run, you can then do

tuner = Tuner.restore(results.experiment_path, trainable=trainable)
tuner.fit()

results.experiment_path can be retrieved from the ResultGrid object. It can also be easily seen in the log output from your first run.

PublicAPI (beta): This API is in beta and may change before becoming stable.

Methods

__init__	Configure and construct a tune run.
can_restore	Checks whether a given directory contains a restorable Tune experiment.
fit	Executes hyperparameter tuning job as configured and returns result.
get_results	Get results of a hyperparameter tuning run.
restore	Restores Tuner after a previously failed run.