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