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"""
CLI to run training on a model
"""
import logging
from pathlib import Path
import fire
import transformers
from colorama import Fore
from axolotl.cli import (
check_accelerate_default_config,
check_user_token,
load_cfg,
load_datasets,
print_axolotl_text_art,
)
from axolotl.common.cli import TrainerCliArgs
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
from axolotl.train import train
LOG = logging.getLogger("axolotl.cli.train")
def do_cli(config: Path = Path("examples/"), **kwargs):
# pylint: disable=duplicate-code
print_axolotl_text_art()
parsed_cfg = load_cfg(config, **kwargs)
check_accelerate_default_config()
check_user_token()
parser = transformers.HfArgumentParser((TrainerCliArgs))
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
return_remaining_strings=True
)
if parsed_cli_args.prepare_ds_only and not parsed_cfg.dataset_prepared_path:
msg = (
Fore.RED
+ "--prepare_ds_only called without dataset_prepared_path set."
+ Fore.RESET
)
LOG.warning(msg)
parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
if parsed_cli_args.prepare_ds_only:
return
train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
if __name__ == "__main__":
fire.Fire(do_cli)
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