ray.tune.Tuner.__init__#

Tuner.__init__(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)[source]#

Configure and construct a tune run.

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.

  • _tuner_kwargs – Internal. Optional kwargs forwarded to TunerInternal.

  • _tuner_internal – Internal. Pre-built TunerInternal instance used when restoring from an existing experiment.

  • _entrypoint – Internal. Marks which user-facing entrypoint constructed the Tuner so that error messages can be tailored.

On this page