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#!/usr/bin/env python
description = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def get_user_input():
compute_environment = _ask_options(
"In which compute environment are you running?",
["This machine", "AWS (Amazon SageMaker)"],
_convert_compute_environment,
)
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
config = get_sagemaker_input()
else:
config = get_cluster_input()
return config
def config_command_parser(subparsers=None):
if subparsers is not None:
parser = subparsers.add_parser("config", description=description)
else:
parser = argparse.ArgumentParser("Accelerate config command", description=description)
parser.add_argument(
"--config_file",
default=None,
help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
),
)
if subparsers is not None:
parser.set_defaults(func=config_command)
return parser
def config_command(args):
config = get_user_input()
if args.config_file is not None:
config_file = args.config_file
else:
if not os.path.isdir(cache_dir):
os.makedirs(cache_dir)
config_file = default_yaml_config_file
if config_file.endswith(".json"):
config.to_json_file(config_file)
else:
config.to_yaml_file(config_file)
print(f"accelerate configuration saved at {config_file}")
def main():
parser = config_command_parser()
args = parser.parse_args()
config_command(args)
if __name__ == "__main__":
main()
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