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Add push_to_hub to no_trainer examples (#13659)

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* Add push_to_hub to no_trainer examples

* Quality

* Document integration

* Roll out to other examples
This commit is contained in:
Sylvain Gugger
2021-09-21 13:13:30 -04:00
committed by GitHub
parent a722c301bf
commit b7d264be0d
10 changed files with 319 additions and 27 deletions
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38
examples/pytorch/multiple-choice/run_swag_no_trainer.py
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@@ -24,6 +24,7 @@ import math
import os
import random
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Union
import datasets
@@ -34,6 +35,7 @@ from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator
from huggingface_hub import Repository
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -47,7 +49,7 @@ from transformers import (
get_scheduler,
set_seed,
)
from transformers.file_utils import PaddingStrategy
from transformers.file_utils import PaddingStrategy, get_full_repo_name
logger = logging.getLogger(__name__)
@@ -169,9 +171,15 @@ def parse_args():
action="store_true",
help="Activate debug mode and run training only with a subset of data.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
args = parser.parse_args()
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
@@ -260,6 +268,18 @@ def main():
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
@@ -478,10 +498,22 @@ def main():
eval_metric = metric.compute()
accelerator.print(f"epoch {epoch}: {eval_metric}")
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
repo.push_to_hub(commit_message=f"Training in progress epoch {epoch}", blocking=False)
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training")
if __name__ == "__main__":