Configuration Overview

Run Configuration in Train (RunConfig)

RunConfig is a configuration object used in Ray Train to define the experiment spec that corresponds to a call to trainer.fit().

It includes settings such as the experiment name, storage path for results, stopping conditions, custom callbacks, checkpoint configuration, verbosity level, and logging options.

Many of these settings are configured through other config objects and passed through the RunConfig. The following sub-sections contain descriptions of these configs.

The properties of the run configuration are not tunable.

import os

from ray.train import RunConfig
from ray.air.integrations.wandb import WandbLoggerCallback

run_config = RunConfig(
    # Name of the training run (directory name).
    name="my_train_run",
    # The experiment results will be saved to: storage_path/name
    storage_path=os.path.expanduser("~/ray_results"),
    # storage_path="s3://my_bucket/tune_results",
    # Custom and built-in callbacks
    callbacks=[WandbLoggerCallback()],
    # Stopping criteria
    stop={"training_iteration": 10},
)

See also

See the RunConfig API reference.

See Configuring Persistent Storage for storage configuration examples (related to storage_path).