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Adapt repository creation to latest hf_hub (#21158)

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* Adapt repository creation to latest hf_hub

* Update all examples

* Fix other tests, add Flax examples

* Address review comments
This commit is contained in:
Sylvain Gugger
2023-01-18 17:14:00 +01:00
committed by GitHub
parent 32525428e1
commit 05e72aa0c4
30 changed files with 83 additions and 73 deletions
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5
examples/flax/image-captioning/run_image_captioning_flax.py
View File

@@ -45,7 +45,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
@@ -430,7 +430,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# Get the datasets: you can either provide your own CSV/JSON 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/

5
examples/flax/language-modeling/run_bart_dlm_flax.py
View File

@@ -45,7 +45,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -502,7 +502,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# 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/

5
examples/flax/language-modeling/run_clm_flax.py
View File

@@ -46,7 +46,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
@@ -376,7 +376,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# 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/

5
examples/flax/language-modeling/run_mlm_flax.py
View File

@@ -46,7 +46,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -416,7 +416,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# 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/

5
examples/flax/language-modeling/run_t5_mlm_flax.py
View File

@@ -45,7 +45,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -542,7 +542,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# 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/

5
examples/flax/question-answering/run_qa.py
View File

@@ -44,7 +44,7 @@ from flax import struct, traverse_util
from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AutoConfig,
AutoTokenizer,
@@ -467,7 +467,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# region Load Data
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)

5
examples/flax/summarization/run_summarization_flax.py
View File

@@ -46,7 +46,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
@@ -450,7 +450,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# Get the datasets: you can either provide your own CSV/JSON 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/

5
examples/flax/text-classification/run_flax_glue.py
View File

@@ -39,7 +39,7 @@ from flax import struct, traverse_util
from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AutoConfig,
AutoTokenizer,
@@ -350,7 +350,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).

5
examples/flax/token-classification/run_flax_ner.py
View File

@@ -41,7 +41,7 @@ from flax import struct, traverse_util
from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AutoConfig,
AutoTokenizer,
@@ -406,7 +406,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# 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 for token classification task available on the hub at https://huggingface.co/datasets/

5
examples/flax/vision/run_image_classification.py
View File

@@ -43,7 +43,7 @@ from flax import jax_utils
from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
@@ -298,7 +298,8 @@ def main():
)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# Initialize datasets and pre-processing transforms
# We use torchvision here for faster pre-processing

5
examples/pytorch/image-classification/run_image_classification_no_trainer.py
View File

@@ -40,7 +40,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AutoConfig,
AutoFeatureExtractor,
@@ -246,7 +246,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/language-modeling/run_clm_no_trainer.py
View File

@@ -41,7 +41,7 @@ import transformers
from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -282,7 +282,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/language-modeling/run_mlm_no_trainer.py
View File

@@ -41,7 +41,7 @@ import transformers
from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -291,7 +291,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/multiple-choice/run_swag_no_trainer.py
View File

@@ -40,7 +40,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -317,7 +317,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
View File

@@ -38,7 +38,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AdamW,
DataCollatorWithPadding,
@@ -332,7 +332,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/question-answering/run_qa_no_trainer.py
View File

@@ -38,7 +38,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -370,7 +370,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
View File

@@ -36,7 +36,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository, hf_hub_download
from huggingface_hub import Repository, create_repo, hf_hub_download
from transformers import (
AutoConfig,
AutoFeatureExtractor,
@@ -354,7 +354,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
View File

@@ -31,7 +31,7 @@ from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AdamW,
SchedulerType,
@@ -422,7 +422,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()

5
examples/pytorch/summarization/run_summarization_no_trainer.py
View File

@@ -40,7 +40,7 @@ from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from filelock import FileLock
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -373,7 +373,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/text-classification/run_glue_no_trainer.py
View File

@@ -32,7 +32,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
@@ -244,7 +244,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/token-classification/run_ner_no_trainer.py
View File

@@ -37,7 +37,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -298,7 +298,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:

5
examples/pytorch/translation/run_translation_no_trainer.py
View File

@@ -38,7 +38,7 @@ import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from huggingface_hub import Repository, create_repo
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
@@ -345,7 +345,8 @@ def main():
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)
create_repo(repo_name, exist_ok=True, token=args.hub_token)
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore: