[Examples] Update with HFApi (#5393)

* update training examples to use HFAPI.

* update training example.

* reflect the changes in the korean version too.

* Empty-Commit

docs/source/en/tutorials/basic_training.md

@@ -284,22 +284,11 @@ Now you can wrap all these components together in a training loop with 🤗 Acce
 
 ```py
 >>> from accelerate import Accelerator
->>> from huggingface_hub import HfFolder, Repository, whoami
+>>> from huggingface_hub import create_repo, upload_folder
 >>> from tqdm.auto import tqdm
 >>> from pathlib import Path
 >>> import os
 
-
->>> def get_full_repo_name(model_id: str, organization: str = None, token: str = None):
-...     if token is None:
-...         token = HfFolder.get_token()
-...     if organization is None:
-...         username = whoami(token)["name"]
-...         return f"{username}/{model_id}"
-...     else:
-...         return f"{organization}/{model_id}"
-
-
 >>> def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
 ...     # Initialize accelerator and tensorboard logging
 ...     accelerator = Accelerator(
@@ -309,11 +298,12 @@ Now you can wrap all these components together in a training loop with 🤗 Acce
 ...         project_dir=os.path.join(config.output_dir, "logs"),
 ...     )
 ...     if accelerator.is_main_process:
-...         if config.push_to_hub:
-...             repo_name = get_full_repo_name(Path(config.output_dir).name)
-...             repo = Repository(config.output_dir, clone_from=repo_name)
-...         elif config.output_dir is not None:
+...         if config.output_dir is not None:
 ...             os.makedirs(config.output_dir, exist_ok=True)
+...         if config.push_to_hub:
+...             repo_id = create_repo(
+...                 repo_id=config.hub_model_id or Path(config.output_dir).name, exist_ok=True
+...             )
 ...         accelerator.init_trackers("train_example")
 
 ...     # Prepare everything
@@ -371,7 +361,12 @@ Now you can wrap all these components together in a training loop with 🤗 Acce
 
 ...             if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:
 ...                 if config.push_to_hub:
-...                     repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=True)
+...                     upload_folder(
+...                         repo_id=repo_id,
+...                         folder_path=config.output_dir,
+...                         commit_message=f"Epoch {epoch}",
+...                         ignore_patterns=["step_*", "epoch_*"],
+...                     )
 ...                 else:
 ...                     pipeline.save_pretrained(config.output_dir)
 ```

docs/source/ko/tutorials/basic_training.md

@@ -283,36 +283,27 @@ TensorBoard에 로깅, 그래디언트 누적 및 혼합 정밀도 학습을 쉽
 
 ```py
 >>> from accelerate import Accelerator
->>> from huggingface_hub import HfFolder, Repository, whoami
+>>> from huggingface_hub import create_repo, upload_folder
 >>> from tqdm.auto import tqdm
 >>> from pathlib import Path
 >>> import os
 
 
->>> def get_full_repo_name(model_id: str, organization: str = None, token: str = None):
-...     if token is None:
-...         token = HfFolder.get_token()
-...     if organization is None:
-...         username = whoami(token)["name"]
-...         return f"{username}/{model_id}"
-...     else:
-...         return f"{organization}/{model_id}"
-
-
 >>> def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
-...     # accelerator와 tensorboard 로깅 초기화
+...     # Initialize accelerator and tensorboard logging
 ...     accelerator = Accelerator(
 ...         mixed_precision=config.mixed_precision,
 ...         gradient_accumulation_steps=config.gradient_accumulation_steps,
 ...         log_with="tensorboard",
-...         logging_dir=os.path.join(config.output_dir, "logs"),
+...         project_dir=os.path.join(config.output_dir, "logs"),
 ...     )
 ...     if accelerator.is_main_process:
-...         if config.push_to_hub:
-...             repo_name = get_full_repo_name(Path(config.output_dir).name)
-...             repo = Repository(config.output_dir, clone_from=repo_name)
-...         elif config.output_dir is not None:
+...         if config.output_dir is not None:
 ...             os.makedirs(config.output_dir, exist_ok=True)
+...         if config.push_to_hub:
+...             repo_id = create_repo(
+...                 repo_id=config.hub_model_id or Path(config.output_dir).name, exist_ok=True
+...             )
 ...         accelerator.init_trackers("train_example")
 
 ...     # 모든 것이 준비되었습니다.
@@ -369,7 +360,12 @@ TensorBoard에 로깅, 그래디언트 누적 및 혼합 정밀도 학습을 쉽
 
 ...             if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:
 ...                 if config.push_to_hub:
-...                     repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=True)
+...                     upload_folder(
+...                         repo_id=repo_id,
+...                         folder_path=config.output_dir,
+...                         commit_message=f"Epoch {epoch}",
+...                         ignore_patterns=["step_*", "epoch_*"],
+...                     )
 ...                 else:
 ...                     pipeline.save_pretrained(config.output_dir)
 ```

examples/dreambooth/train_dreambooth_flax.py

@@ -4,7 +4,6 @@ import logging
 import math
 import os
 from pathlib import Path
-from typing import Optional
 
 import jax
 import jax.numpy as jnp
@@ -16,7 +15,7 @@ import transformers
 from flax import jax_utils
 from flax.training import train_state
 from flax.training.common_utils import shard
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from huggingface_hub import create_repo, upload_folder
 from jax.experimental.compilation_cache import compilation_cache as cc
 from PIL import Image
 from torch.utils.data import Dataset
@@ -318,16 +317,6 @@ class PromptDataset(Dataset):
         return example
 
 
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
 def get_params_to_save(params):
     return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
 
@@ -392,22 +381,14 @@ def main():
 
     # Handle the repository creation
     if jax.process_index() == 0:
-        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
-            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:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
+        if args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
 
+        if args.push_to_hub:
+            repo_id = create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
+            ).repo_id
+
     # Load the tokenizer and add the placeholder token as a additional special token
     if args.tokenizer_name:
         tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
@@ -668,7 +649,12 @@ def main():
 
         if args.push_to_hub:
             message = f"checkpoint-{step}" if step is not None else "End of training"
-            repo.push_to_hub(commit_message=message, blocking=False, auto_lfs_prune=True)
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message=message,
+                ignore_patterns=["step_*", "epoch_*"],
+            )
 
     global_step = 0
 

examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py

@@ -4,7 +4,7 @@ import math
 import os
 import random
 from pathlib import Path
-from typing import Iterable, Optional
+from typing import Iterable
 
 import numpy as np
 import PIL
@@ -13,7 +13,7 @@ import torch.nn.functional as F
 import torch.utils.checkpoint
 from accelerate import Accelerator
 from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import HfFolder, Repository, whoami
+from huggingface_hub import create_repo, upload_folder
 from neural_compressor.utils import logger
 from packaging import version
 from PIL import Image
@@ -413,16 +413,6 @@ class TextualInversionDataset(Dataset):
         return example
 
 
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
 def freeze_params(params):
     for param in params:
         param.requires_grad = False
@@ -461,21 +451,14 @@ def main():
 
     # 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)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
+        if args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
 
+        if args.push_to_hub:
+            repo_id = create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
+            ).repo_id
+
     # Load the tokenizer and add the placeholder token as a additional special token
     if args.tokenizer_name:
         tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
@@ -982,7 +965,12 @@ def main():
             )
 
         if args.push_to_hub:
-            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
 
     accelerator.end_training()
 

examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py

@@ -4,7 +4,6 @@ import logging
 import math
 import os
 from pathlib import Path
-from typing import Optional
 
 import accelerate
 import datasets
@@ -14,7 +13,7 @@ from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration
 from datasets import load_dataset
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from huggingface_hub import create_repo, upload_folder
 from onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer
 from onnxruntime.training.ortmodule import ORTModule
 from packaging import version
@@ -277,16 +276,6 @@ def parse_args():
     return args
 
 
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
 def main(args):
     logging_dir = os.path.join(args.output_dir, args.logging_dir)
     accelerator_project_config = ProjectConfiguration(
@@ -360,22 +349,14 @@ def main(args):
 
     # 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
-            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:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
+        if args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
 
+        if args.push_to_hub:
+            repo_id = create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
+            ).repo_id
+
     # Initialize the model
     if args.model_config_name_or_path is None:
         model = UNet2DModel(
@@ -691,7 +672,12 @@ def main(args):
                     ema_model.restore(unet.parameters())
 
                 if args.push_to_hub:
-                    repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
+                    upload_folder(
+                        repo_id=repo_id,
+                        folder_path=args.output_dir,
+                        commit_message=f"Epoch {epoch}",
+                        ignore_patterns=["step_*", "epoch_*"],
+                    )
 
     accelerator.end_training()
 

examples/unconditional_image_generation/train_unconditional.py

@@ -6,7 +6,6 @@ import os
 import shutil
 from datetime import timedelta
 from pathlib import Path
-from typing import Optional
 
 import accelerate
 import datasets
@@ -16,7 +15,7 @@ from accelerate import Accelerator, InitProcessGroupKwargs
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration
 from datasets import load_dataset
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from torchvision import transforms
 from tqdm.auto import tqdm
@@ -273,16 +272,6 @@ def parse_args():
     return args
 
 
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
 def main(args):
     logging_dir = os.path.join(args.output_dir, args.logging_dir)
     accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
@@ -356,22 +345,14 @@ def main(args):
 
     # 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
-            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:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
+        if args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
 
+        if args.push_to_hub:
+            repo_id = create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
+            ).repo_id
+
     # Initialize the model
     if args.model_config_name_or_path is None:
         model = UNet2DModel(
@@ -708,7 +689,12 @@ def main(args):
                     ema_model.restore(unet.parameters())
 
                 if args.push_to_hub:
-                    repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
+                    upload_folder(
+                        repo_id=repo_id,
+                        folder_path=args.output_dir,
+                        commit_message=f"Epoch {epoch}",
+                        ignore_patterns=["step_*", "epoch_*"],
+                    )
 
     accelerator.end_training()