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_hub는 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 make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value 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)

< > Update on GitHub

Hub Python Library

TensorBoard 로거

HFSummaryWriter

class huggingface_hub.HFSummaryWriter