- classmethod Tuner.can_restore(path: str | PathLike, storage_filesystem: pyarrow.fs.FileSystem | None = None) bool #
Checks whether a given directory contains a restorable Tune experiment.
Use this utility to switch between starting a new Tune experiment and restoring when possible. This is useful for experiment fault-tolerance when re-running a failed tuning script.
import os from ray.tune import Tuner from ray.train import RunConfig def train_fn(config): # Make sure to implement checkpointing so that progress gets # saved on restore. pass name = "exp_name" storage_path = os.path.expanduser("~/ray_results") exp_dir = os.path.join(storage_path, name) if Tuner.can_restore(exp_dir): tuner = Tuner.restore(exp_dir, trainable=train_fn, resume_errored=True) else: tuner = Tuner( train_fn, run_config=RunConfig(name=name, storage_path=storage_path), ) tuner.fit()
path – The path to the experiment directory of the Tune experiment. This can be either a local directory or a remote URI (e.g. s3://bucket/exp_name).
True if this path exists and contains the Tuner state to resume from
- Return type: