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, storage_path: Optional[str] = None, storage_filesystem: Optional[pyarrow.fs.FileSystem] = None, _experiment_checkpoint_dir: Optional[str] = None, sync_config: Optional[Union[ray.train.SyncConfig, dict]] = None, checkpoint_config: Optional[Union[ray.train.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, local_dir: Optional[str] = None)

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.

Methods

from_json(name, spec)	Generates an Experiment object from JSON.
get_experiment_checkpoint_dir(run_obj[, ...])	Get experiment checkpoint dir without setting up an experiment.
get_trainable_name(run_object)	Get Trainable name.
register_if_needed(run_object)	Registers Trainable or Function at runtime.

Attributes

PUBLIC_KEYS
checkpoint_config
checkpoint_dir
local_dir
local_path
path
public_spec	Returns the spec dict with only the public-facing keys.
remote_checkpoint_dir
remote_path
run_identifier	Returns a string representing the trainable identifier.
stopper