Class RunConfig

python/ray/train/v2/api/config.py:395–511

Runtime configuration for training runs. Args: name: Name of the trial or experiment. If not provided, will be deduced from the Trainable. storage_path: Path where all results and checkpoints are persisted. Can be a local directory or a destination on clo

@dataclass
@PublicAPI(stability="stable")
class RunConfig:
    """Runtime configuration for training runs.

    Args:
        name: Name of the trial or experiment. If not provided, will be deduced
            from the Trainable.
        storage_path: Path where all results and checkpoints are persisted.
            Can be a local directory or a destination on cloud storage.
            For multi-node training/tuning runs, this must be set to a
            shared storage location (e.g., S3, NFS).
            This defaults to the local ``~/ray_results`` directory.
        storage_filesystem: A custom filesystem to use for storage.
            If this is provided, `storage_path` should be a path with its
            prefix stripped (e.g., `s3://bucket/path` -> `bucket/path`).
        failure_config: Failure mode configuration.
        checkpoint_config: Checkpointing configuration.
        callbacks: [DeveloperAPI] A list of callbacks that the Ray Train controller
            will invoke during training.
        worker_runtime_env: [DeveloperAPI] Runtime environment configuration
            for all Ray Train worker actors.
        logging_config: Configuration for Ray Train's logging behavior.
            See :class:`LoggingConfig` for details.
    """

    name: Optional[str] = None
    storage_path: Optional[str] = None
    storage_filesystem: Optional[pyarrow.fs.FileSystem] = None
    failure_config: Optional[FailureConfig] = None
    checkpoint_config: Optional[CheckpointConfig] = None
    callbacks: Optional[List["UserCallback"]] = None
    worker_runtime_env: Optional[Union[dict, RuntimeEnv]] = None
    logging_config: Optional[LoggingConfig] = None

    sync_config: str = _DEPRECATED
    verbose: str = _DEPRECATED
    stop: str = _DEPRECATED
    progress_reporter: str = _DEPRECATED
    log_to_file: str = _DEPRECATED

    def __post_init__(self):
        from ray.train.constants import DEFAULT_STORAGE_PATH

        if self.storage_path is None:
            self.storage_path = DEFAULT_STORAGE_PATH

        if not self.failure_config:
            self.failure_config = FailureConfig()

        if not self.checkpoint_config:
            self.checkpoint_config = CheckpointConfig()

        if not self.logging_config:
            self.logging_config = LoggingConfig()

        if isinstance(self.storage_path, Path):
            self.storage_path = self.storage_path.as_posix()

        run_config_deprecation_message = (