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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains a logger to push training logs to the Hub, using Tensorboard."""
from pathlib import Path
from typing import TYPE_CHECKING, List, Optional, Union
from huggingface_hub._commit_scheduler import CommitScheduler
from .utils import experimental, is_tensorboard_available
if is_tensorboard_available():
from tensorboardX import SummaryWriter
# TODO: clarify: should we import from torch.utils.tensorboard ?
else:
SummaryWriter = object # Dummy class to avoid failing at import. Will raise on instance creation.
if TYPE_CHECKING:
from tensorboardX import SummaryWriter
class HFSummaryWriter(SummaryWriter):
"""
Wrapper around the tensorboard's `SummaryWriter` to push training logs to the Hub.
Data is logged locally and then pushed to the Hub asynchronously. Pushing data to the Hub is done in a separate
thread to avoid blocking the training script. In particular, if the upload fails for any reason (e.g. a connection
issue), the main script will not be interrupted. Data is automatically pushed to the Hub every `commit_every`
minutes (default to every 5 minutes).
<Tip warning={true}>
`HFSummaryWriter` is experimental. Its API is subject to change in the future without prior notice.
</Tip>
Args:
repo_id (`str`):
The id of the repo to which the logs will be pushed.
logdir (`str`, *optional*):
The directory where the logs will be written. If not specified, a local directory will be created by the
underlying `SummaryWriter` object.
commit_every (`int` or `float`, *optional*):
The frequency (in minutes) at which the logs will be pushed to the Hub. Defaults to 5 minutes.
squash_history (`bool`, *optional*):
Whether to squash the history of the repo after each commit. Defaults to `False`. Squashing commits is
useful to avoid degraded performances on the repo when it grows too large.
repo_type (`str`, *optional*):
The type of the repo to which the logs will be pushed. Defaults to "model".
repo_revision (`str`, *optional*):
The revision of the repo to which the logs will be pushed. Defaults to "main".
repo_private (`bool`, *optional*):
Whether to create a private repo or not. Defaults to False. This argument is ignored if the repo already
exists.
path_in_repo (`str`, *optional*):
The path to the folder in the repo where the logs will be pushed. Defaults to "tensorboard/".
repo_allow_patterns (`List[str]` or `str`, *optional*):
A list of patterns to include in the upload. Defaults to `"*.tfevents.*"`. Check out the
[upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder) for more details.
repo_ignore_patterns (`List[str]` or `str`, *optional*):
A list of patterns to exclude in the upload. Check out the
[upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder) for more details.
token (`str`, *optional*):
Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more
details
kwargs:
Additional keyword arguments passed to `SummaryWriter`.
Examples:
```py
>>> from huggingface_hub import HFSummaryWriter
# Logs are automatically pushed every 15 minutes
>>> logger = HFSummaryWriter(repo_id="test_hf_logger", commit_every=15)
>>> logger.add_scalar("a", 1)
>>> logger.add_scalar("b", 2)
...
# You can also trigger a push manually
>>> logger.scheduler.trigger()
```
```py
>>> from huggingface_hub import HFSummaryWriter
# Logs are automatically pushed every 5 minutes (default) + when exiting the context manager
>>> with HFSummaryWriter(repo_id="test_hf_logger") as logger:
... logger.add_scalar("a", 1)
... logger.add_scalar("b", 2)
```
"""
@experimental
def __new__(cls, *args, **kwargs) -> "HFSummaryWriter":
if not is_tensorboard_available():
raise ImportError(
"You must have `tensorboard` installed to use `HFSummaryWriter`. Please run `pip install --upgrade"
" tensorboardX` first."
)
return super().__new__(cls)
def __init__(
self,
repo_id: str,
*,
logdir: Optional[str] = None,
commit_every: Union[int, float] = 5,
squash_history: bool = False,
repo_type: Optional[str] = None,
repo_revision: Optional[str] = None,
repo_private: bool = False,
path_in_repo: Optional[str] = "tensorboard",
repo_allow_patterns: Optional[Union[List[str], str]] = "*.tfevents.*",
repo_ignore_patterns: Optional[Union[List[str], str]] = None,
token: Optional[str] = None,
**kwargs,
):
# Initialize SummaryWriter
super().__init__(logdir=logdir, **kwargs)
# Check logdir has been correctly initialized and fail early otherwise. In practice, SummaryWriter takes care of it.
if not isinstance(self.logdir, str):
raise ValueError(f"`self.logdir` must be a string. Got '{self.logdir}' of type {type(self.logdir)}.")
# Append logdir name to `path_in_repo`
if path_in_repo is None or path_in_repo == "":
path_in_repo = Path(self.logdir).name
else:
path_in_repo = path_in_repo.strip("/") + "/" + Path(self.logdir).name
# Initialize scheduler
self.scheduler = CommitScheduler(
folder_path=self.logdir,
path_in_repo=path_in_repo,
repo_id=repo_id,
repo_type=repo_type,
revision=repo_revision,
private=repo_private,
token=token,
allow_patterns=repo_allow_patterns,
ignore_patterns=repo_ignore_patterns,
every=commit_every,
squash_history=squash_history,
)
# Exposing some high-level info at root level
self.repo_id = self.scheduler.repo_id
self.repo_type = self.scheduler.repo_type
self.repo_revision = self.scheduler.revision
def __exit__(self, exc_type, exc_val, exc_tb):
"""Push to hub in a non-blocking way when exiting the logger's context manager."""
super().__exit__(exc_type, exc_val, exc_tb)
future = self.scheduler.trigger()
future.result()
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