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Experimental hf logger (#1456)
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* first draft for an hflogger

* move to huggingface_hub

* copyright

* Add default tensorboard logger

* leverage  to push to hub in the background

* Push ot hub on context manager closing

* remove clunky HFLoggerMixin and focus on tensorboard

* mark HFSummaryWriter as experimental

* automatic push in HFSummaryWriter

* simplify logic if only 1 LFS file

* more verbose

* Append logdir to path_in_repo

* add apckage reference

* fix typing

* fix kwargs in doc

* fix type
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Wauplin authored Jun 13, 2023
1 parent b74a44e commit b4e01b0
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2 changes: 2 additions & 0 deletions docs/source/_toctree.yml
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Expand Up @@ -68,5 +68,7 @@
title: Repo Cards and Repo Card Data
- local: package_reference/space_runtime
title: Space runtime
- local: package_reference/tensorboard
title: TensorBoard logger
- local: package_reference/webhooks_server
title: Webhooks server
15 changes: 15 additions & 0 deletions docs/source/package_reference/tensorboard.mdx
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@@ -0,0 +1,15 @@
# TensorBoard logger

TensorBoard is a visualization toolkit for machine learning experimentation. TensorBoard allows tracking and visualizing
metrics such as loss and accuracy, visualizing the model graph, viewing histograms, displaying images and much more.
TensorBoard is well integrated with the Hugging Face Hub. The Hub automatically detects TensorBoard traces (such as
`tfevents`) when pushed to the Hub which starts an instance to visualize them. To get more information about TensorBoard
integration on the Hub, check out [this guide](https://huggingface.co/docs/hub/tensorboard).

To benefit from this integration, `huggingface_hub` provides a custom logger to push logs to the Hub. It works as a
drop-in replacement for [SummaryWriter](https://tensorboardx.readthedocs.io/en/latest/tensorboard.html) with no extra
code needed. Traces are still saved locally and a background job push them to the Hub at regular interval.

## HFSummaryWriter

[[autodoc]] HFSummaryWriter
4 changes: 4 additions & 0 deletions src/huggingface_hub/__init__.py
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Expand Up @@ -77,6 +77,9 @@
"SpaceRuntime",
"SpaceStage",
],
"_tensorboard_logger": [
"HFSummaryWriter",
],
"_webhooks_payload": [
"WebhookPayload",
"WebhookPayloadComment",
Expand Down Expand Up @@ -371,6 +374,7 @@ def __dir__():
SpaceRuntime, # noqa: F401
SpaceStage, # noqa: F401
)
from ._tensorboard_logger import HFSummaryWriter # noqa: F401
from ._webhooks_payload import (
WebhookPayload, # noqa: F401
WebhookPayloadComment, # noqa: F401
Expand Down
5 changes: 4 additions & 1 deletion src/huggingface_hub/_commit_api.py
Original file line number Diff line number Diff line change
Expand Up @@ -386,8 +386,11 @@ def _wrapped_lfs_upload(batch_action) -> None:

if HF_HUB_ENABLE_HF_TRANSFER:
logger.debug(f"Uploading {len(filtered_actions)} LFS files to the Hub using `hf_transfer`.")
for action in filtered_actions:
for action in hf_tqdm(filtered_actions):
_wrapped_lfs_upload(action)
elif len(filtered_actions) == 1:
logger.debug("Uploading 1 LFS file to the Hub")
_wrapped_lfs_upload(filtered_actions[0])
else:
logger.debug(
f"Uploading {len(filtered_actions)} LFS files to the Hub using up to {num_threads} threads concurrently"
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11 changes: 10 additions & 1 deletion src/huggingface_hub/_commit_scheduler.py
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Expand Up @@ -2,6 +2,7 @@
import logging
import os
import time
from concurrent.futures import Future
from dataclasses import dataclass
from io import SEEK_END, SEEK_SET, BytesIO
from pathlib import Path
Expand Down Expand Up @@ -143,11 +144,19 @@ def stop(self) -> None:
def _run_scheduler(self) -> None:
"""Dumb thread waiting between each scheduled push to Hub."""
while True:
self.last_future = self.api.run_as_future(self._push_to_hub)
self.last_future = self.trigger()
time.sleep(self.every * 60)
if self.__stopped:
break

def trigger(self) -> Future:
"""Trigger a `push_to_hub` and return a future.
This method is automatically called every `every` minutes. You can also call it manually to trigger a commit
immediately, without waiting for the next scheduled commit.
"""
return self.api.run_as_future(self._push_to_hub)

def _push_to_hub(self) -> Optional[CommitInfo]:
if self.__stopped: # If stopped, already scheduled commits are ignored
return None
Expand Down
157 changes: 157 additions & 0 deletions src/huggingface_hub/_tensorboard_logger.py
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@@ -0,0 +1,157 @@
# 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.
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,
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,
)

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()
4 changes: 3 additions & 1 deletion src/huggingface_hub/utils/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,6 @@
get_fastai_version,
get_fastcore_version,
get_gradio_version,
is_gradio_available,
get_graphviz_version,
get_hf_hub_version,
get_hf_transfer_version,
Expand All @@ -61,18 +60,21 @@
get_pillow_version,
get_pydot_version,
get_python_version,
get_tensorboard_version,
get_tf_version,
get_torch_version,
is_fastai_available,
is_fastcore_available,
is_numpy_available,
is_google_colab,
is_gradio_available,
is_graphviz_available,
is_hf_transfer_available,
is_jinja_available,
is_notebook,
is_pillow_available,
is_pydot_available,
is_tensorboard_available,
is_tf_available,
is_torch_available,
)
Expand Down
6 changes: 4 additions & 2 deletions src/huggingface_hub/utils/_experimental.py
Original file line number Diff line number Diff line change
Expand Up @@ -48,14 +48,16 @@ def experimental(fn: Callable) -> Callable:
Hello world!
```
"""
# For classes, put the "experimental" around the "__new__" method => __new__ will be removed in warning message
name = fn.__qualname__[: -len(".__new__")] if fn.__qualname__.endswith(".__new__") else fn.__qualname__

@wraps(fn)
def _inner_fn(*args, **kwargs):
if not constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING:
warnings.warn(
(
f"'{fn.__name__}' is experimental and might be subject to breaking changes in the future. You can"
" disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment"
f"'{name}' is experimental and might be subject to breaking changes in the future."
" You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment"
" variable."
),
UserWarning,
Expand Down
11 changes: 11 additions & 0 deletions src/huggingface_hub/utils/_runtime.py
Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@
"numpy": {"numpy"},
"pillow": {"Pillow"},
"pydot": {"pydot"},
"tensorboard": {"tensorboardX"},
"tensorflow": (
"tensorflow",
"tensorflow-cpu",
Expand Down Expand Up @@ -167,6 +168,15 @@ def get_pydot_version() -> str:
return _get_version("pydot")


# Tensorboard
def is_tensorboard_available() -> bool:
return _is_available("tensorboard")


def get_tensorboard_version() -> str:
return _get_version("tensorboard")


# Tensorflow
def is_tf_available() -> bool:
return _is_available("tensorflow")
Expand Down Expand Up @@ -275,6 +285,7 @@ def dump_environment_info() -> Dict[str, Any]:
info["Pillow"] = get_pillow_version()
info["hf_transfer"] = get_hf_transfer_version()
info["gradio"] = get_gradio_version()
info["tensorboard"] = get_tensorboard_version()
info["numpy"] = get_numpy_version()

# Environment variables
Expand Down

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