#
TensorBoard 로거

TensorBoard는 기계학습 실험을 위한 시각화 도구입니다. 주로 손실 및 정확도와 같은 지표를 추적 및 시각화하고, 모델 그래프와 히스토그램을 보여주고, 이미지를 표시하는 등 다양한 기능을 제공합니다. 또한 TensorBoard는 Hugging Face Hub와 잘 통합되어 있습니다. `tfevents` 같은 TensorBoard 추적을 Hub에 푸시하면 Hub는 이를 자동으로 감지하여 시각화 인스턴스를 시작합니다. TensorBoard와 Hub의 통합에 대한 자세한 정보는 가이드를 확인하세요.

이 통합을 위해, `huggingface_hub`는 로그를 Hub로 푸시하기 위한 사용자 정의 로거를 제공합니다. 이 로거는 추가적인 코드 없이 SummaryWriter의 대체제로 사용될 수 있습니다. 추적은 계속해서 로컬에 저장되며 백그라운드 작업이 일정한 시간마다 Hub에 푸시하는 형태로 동작합니다.

##
HFSummaryWriter

###
class huggingface_hub.HFSummaryWriter

< source >

( *args **kwargs )

Parameters

- 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 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 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`.

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).

`HFSummaryWriter` is experimental. Its API is subject to change in the future without prior notice.

Examples:

    # Taken from https://pytorch.org/docs/stable/tensorboard.html
    - from torch.utils.tensorboard import SummaryWriter
    + from huggingface_hub import HFSummaryWriter

    import numpy as np

    - writer = SummaryWriter()
    + writer = HFSummaryWriter(repo_id="username/my-trained-model")

    for n_iter in range(100):
        writer.add_scalar('Loss/train', np.random.random(), n_iter)
        writer.add_scalar('Loss/test', np.random.random(), n_iter)
        writer.add_scalar('Accuracy/train', np.random.random(), n_iter)
        writer.add_scalar('Accuracy/test', np.random.random(), n_iter)

    >>> from huggingface_hub import HFSummaryWriter

    # Logs are automatically pushed every 15 minutes (5 by default) + when exiting the context manager
    >>> with HFSummaryWriter(repo_id="test_hf_logger", commit_every=15) as logger:
    ...     logger.add_scalar("a", 1)
    ...     logger.add_scalar("b", 2)

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