ray.tune.Experiment#

class ray.tune.Experiment(name: str, run: Union[str, Callable, Type], *, stop: Optional[Union[Mapping, ray.tune.stopper.stopper.Stopper, Callable[[str, Mapping], bool]]] = None, time_budget_s: Optional[Union[int, float, datetime.timedelta]] = None, config: Optional[Dict[str, Any]] = None, resources_per_trial: Union[None, Mapping[str, Union[float, int, Mapping]], PlacementGroupFactory] = None, num_samples: int = 1, local_dir: Optional[str] = None, _experiment_checkpoint_dir: Optional[str] = None, sync_config: Optional[Union[ray.tune.syncer.SyncConfig, dict]] = None, checkpoint_config: Optional[Union[ray.air.config.CheckpointConfig, dict]] = None, trial_name_creator: Optional[Callable[[Trial], str]] = None, trial_dirname_creator: Optional[Callable[[Trial], str]] = None, log_to_file: bool = False, export_formats: Optional[Sequence] = None, max_failures: int = 0, restore: Optional[str] = None)[source]#

Bases: object

Tracks experiment specifications.

Implicitly registers the Trainable if needed. The args here take the same meaning as the arguments defined tune.py:run.

experiment_spec = Experiment(
    "my_experiment_name",
    my_func,
    stop={"mean_accuracy": 100},
    config={
        "alpha": tune.grid_search([0.2, 0.4, 0.6]),
        "beta": tune.grid_search([1, 2]),
    },
    resources_per_trial={
        "cpu": 1,
        "gpu": 0
    },
    num_samples=10,
    local_dir="~/ray_results",
    checkpoint_freq=10,
    max_failures=2)
Parameters

  • TODO (xwjiang) – Add the whole list.

  • _experiment_checkpoint_dir – Internal use only. If present, use this as the root directory for experiment checkpoint. If not present, the directory path will be deduced from trainable name instead.

DeveloperAPI: This API may change across minor Ray releases.

classmethod from_json(name: str, spec: dict)[source]#

Generates an Experiment object from JSON.

Parameters

  • name – Name of Experiment.

  • spec – JSON configuration of experiment.

classmethod get_trainable_name(run_object: Union[str, Callable, Type])[source]#

Get Trainable name.

Parameters

run_object – Trainable to run. If string, assumes it is an ID and does not modify it. Otherwise, returns a string corresponding to the run_object name.

Returns

A string representing the trainable identifier.

Raises

TuneError – if run_object passed in is invalid.

classmethod register_if_needed(run_object: Union[str, Callable, Type])[source]#

Registers Trainable or Function at runtime.

Assumes already registered if run_object is a string. Also, does not inspect interface of given run_object.

Parameters

run_object – Trainable to run. If string, assumes it is an ID and does not modify it. Otherwise, returns a string corresponding to the run_object name.

Returns

A string representing the trainable identifier.

classmethod get_experiment_checkpoint_dir(run_obj: Union[str, Callable, Type], local_dir: Optional[str] = None, name: Optional[str] = None)[source]#

Get experiment checkpoint dir without setting up an experiment.

This is only used internally for better support of Tuner API.

Parameters

  • run_obj – Trainable to run.

  • local_dir – The local_dir path.

  • name – The name of the experiment specified by user.

Returns

Checkpoint directory for experiment.

property run_identifier#

Returns a string representing the trainable identifier.

property public_spec: Dict[str, Any]#

Returns the spec dict with only the public-facing keys.

Intended to be used for passing information to callbacks, Searchers and Schedulers.