- class ray.train.RunConfig(name: Optional[str] = None, storage_path: Optional[str] = None, storage_filesystem: Optional[pyarrow.fs.FileSystem] = None, failure_config: Optional[ray.train.FailureConfig] = None, checkpoint_config: Optional[ray.train.CheckpointConfig] = None, sync_config: Optional[SyncConfig] = None, verbose: Optional[Union[int, AirVerbosity, Verbosity]] = None, stop: Optional[Union[Mapping, Stopper, Callable[[str, Mapping], bool]]] = None, callbacks: Optional[List[Callback]] = None, progress_reporter: Optional[ProgressReporter] = None, log_to_file: Union[bool, str, Tuple[str, str]] = False, local_dir: Optional[str] = None)#
Runtime configuration for training and tuning runs.
Upon resuming from a training or tuning run checkpoint, Ray Train/Tune will automatically apply the RunConfig from the previously checkpointed run.
name – Name of the trial or experiment. If not provided, will be deduced from the Trainable.
storage_path – [Beta] Path to store results at. Can be a local directory or a destination on cloud storage. If Ray storage is set up, defaults to the storage location. Otherwise, this defaults to the local
failure_config – Failure mode configuration.
checkpoint_config – Checkpointing configuration.
sync_config – Configuration object for syncing. See train.SyncConfig.
verbose – 0, 1, or 2. Verbosity mode. 0 = silent, 1 = default, 2 = verbose. Defaults to 1. If the
RAY_AIR_NEW_OUTPUT=1environment variable is set, uses the old verbosity settings: 0 = silent, 1 = only status updates, 2 = status and brief results, 3 = status and detailed results.
stop – Stop conditions to consider. Refer to ray.tune.stopper.Stopper for more info. Stoppers should be serializable.
callbacks – [DeveloperAPI] Callbacks to invoke. Refer to ray.tune.callback.Callback for more info. Callbacks should be serializable. Currently only stateless callbacks are supported for resumed runs. (any state of the callback will not be checkpointed by Tune and thus will not take effect in resumed runs).
progress_reporter – [DeveloperAPI] Progress reporter for reporting intermediate experiment progress. Defaults to CLIReporter if running in command-line, or JupyterNotebookReporter if running in a Jupyter notebook.
log_to_file – [DeveloperAPI] Log stdout and stderr to files in trial directories. If this is
False(default), no files are written. If
true, outputs are written to
trialdir/stderr, respectively. If this is a single string, this is interpreted as a file relative to the trialdir, to which both streams are written. If this is a Sequence (e.g. a Tuple), it has to have length 2 and the elements indicate the files to which stdout and stderr are written, respectively.