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<h1>SwanLab</h1>
一个开源、现代化设计的深度学习训练跟踪与可视化工具
同时支持云端/离线使用，适配30+主流框架，与你的实验代码轻松集成
<a href="https://swanlab.cn">🔥SwanLab 在线版</a> · <a href="https://docs.swanlab.cn">📃 文档</a> · <a href="https://github.com/swanhubx/swanlab/issues">报告问题</a> · <a href="https://geektechstudio.feishu.cn/share/base/form/shrcnyBlK8OMD0eweoFcc2SvWKc">建议反馈</a> · <a href="https://docs.swanlab.cn/zh/guide_cloud/general/changelog.html">更新日志</a>
[![][release-shield]][release-link]
[![][github-stars-shield]][github-stars-link]
[![][github-issues-shield]][github-issues-shield-link]
[![][github-contributors-shield]][github-contributors-link]
[![][license-shield]][license-shield-link]
[![][swanlab-cloud-shield]][swanlab-cloud-shield-link]
[![][last-commit-shield]][last-commit-shield-link]
[![][pypi-version-shield]][pypi-version-shield-link]
[![][wechat-shield]][wechat-shield-link]
[![][pypi-downloads-shield]][pypi-downloads-shield-link]
[![][colab-shield]][colab-shield-link]
中文 / [English](README_EN.md) / [日本語](README_JP.md) / [Русский](README_RU.md)
👋 加入我们的[微信群](https://docs.swanlab.cn/zh/guide_cloud/community/online-support.html)
</div>
## 目录
- [🌟 最近更新](#-最近更新)
- [👋🏻 什么是SwanLab](#-什么是swanlab)
- [📃 在线演示](#-在线演示)
- [🏁 快速开始](#-快速开始)
- [💻 自托管](#-自托管)
- [🚗 框架集成](#-框架集成)
- [🆚 与熟悉的工具的比较](#-与熟悉的工具的比较)
- [👥 社区](#-社区)
- [📃 协议](#-协议)
<br/>
## 🌟 最近更新
- 2025.02.09：我们与 [veRL](https://github.com/volcengine/verl) 完成了联合集成，现在你可以在veRL中使用SwanLab来**跟踪和可视化大模型强化学习实验**，[使用指引](https://docs.swanlab.cn/guide_cloud/integration/integration-verl.html)。
- 2025.02.05：`swanlab.log`支持嵌套字典 [#812](https://github.com/SwanHubX/SwanLab/pull/812)，适配Jax框架特性；支持`name`与`notes`参数；
- 2025.01.22：新增`sync_tensorboardX`与`sync_tensorboard_torch`功能，支持与此两种TensorBoard框架同步实验跟踪；
- 2025.01.17：新增`sync_wandb`功能，[文档](https://docs.swanlab.cn/guide_cloud/integration/integration-wandb.html)，支持与Weights & Biases实验跟踪同步；大幅改进了日志渲染性能
- 2025.01.11：云端版大幅优化了项目表格的性能，并支持拖拽、排序、筛选等交互
- 2025.01.01：新增折线图**持久化平滑**、折线图拖拽式改变大小，优化图表浏览体验
- 2024.12.22：我们与 [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) 完成了联合集成，现在你可以在LLaMA Factory中使用SwanLab来**跟踪和可视化大模型微调实验**，[使用指引](https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#use-swanlab-logger)。
- 2024.12.15：**硬件监控（0.4.0）** 功能上线，支持CPU、NPU（Ascend）、GPU（Nvidia）的系统级信息记录与监控。
<details><summary>完整更新日志</summary>
- 2024.12.06：新增对[LightGBM](https://docs.swanlab.cn/guide_cloud/integration/integration-lightgbm.html)、[XGBoost](https://docs.swanlab.cn/guide_cloud/integration/integration-xgboost.html)的集成；提高了对日志记录单行长度的限制。
- 2024.11.26：环境选项卡-硬件部分支持识别**华为昇腾NPU**与**鲲鹏CPU**；云厂商部分支持识别青云**基石智算**。
</details>
<br>
## 👋🏻 什么是SwanLab
SwanLab 是一款开源、轻量的 AI 模型训练跟踪与可视化工具，提供了一个跟踪、记录、比较、和协作实验的平台。
SwanLab 面向人工智能研究者，设计了友好的Python API 和漂亮的UI界面，并提供**训练可视化、自动日志记录、超参数记录、实验对比、多人协同**等功能。在SwanLab上，研究者能基于直观的可视化图表发现训练问题，对比多个实验找到研究灵感，并通过**在线网页**的分享与基于组织的**多人协同训练**，打破团队沟通的壁垒，提高组织训练效率。
以下是其核心特性列表：
**1. 📊 实验指标与超参数跟踪**: 极简的代码嵌入您的机器学习 pipeline，跟踪记录训练关键指标
- 支持**云端**使用（类似Weights & Biases），随时随地查看训练进展。[手机看实验的方法](https://docs.swanlab.cn/guide_cloud/general/app.html)
- 支持**超参数记录**与表格展示
- **支持的元数据类型**：标量指标、图像、音频、文本、...
- **支持的图表类型**：折线图、媒体图（图像、音频、文本）、...
- **后台自动记录**：日志logging、硬件环境、Git 仓库、Python 环境、Python 库列表、项目运行目录
**2. ⚡️ 全面的框架集成**: PyTorch、🤗HuggingFace Transformers、PyTorch Lightning、🦙LLaMA Factory、MMDetection、Ultralytics、PaddleDetetion、LightGBM、XGBoost、Keras、Tensorboard、Weights&Biases、OpenAI、Swift、XTuner、Stable Baseline3、Hydra 在内的 **30+** 框架
**3. 💻 硬件监控**: 支持实时记录与监控CPU、NPU（昇腾Ascend）、GPU（英伟达Nvidia）、内存的系统级硬件指标
**4. 📦 实验管理**: 通过专为训练场景设计的集中式仪表板，通过整体视图速览全局，快速管理多个项目与实验
**4. 🆚 比较结果**: 通过在线表格与对比图表比较不同实验的超参数和结果，挖掘迭代灵感
**5. 👥 在线协作**: 您可以与团队进行协作式训练，支持将实验实时同步在一个项目下，您可以在线查看团队的训练记录，基于结果发表看法与建议
**6. ✉️ 分享结果**: 复制和发送持久的 URL 来共享每个实验，方便地发送给伙伴，或嵌入到在线笔记中
**7. 💻 支持自托管**: 支持离线环境使用，自托管的社区版同样可以查看仪表盘与管理实验
> \[!IMPORTANT]
>
> **收藏项目**，你将从 GitHub 上无延迟地接收所有发布通知～ ⭐️
<br>
## 📃 在线演示
来看看 SwanLab 的在线演示：
| [ResNet50 猫狗分类][demo-cats-dogs] | [Yolov8-COCO128 目标检测][demo-yolo] |
| :--------: | :--------: |
| [![][demo-cats-dogs-image]][demo-cats-dogs] | [![][demo-yolo-image]][demo-yolo] |
| 跟踪一个简单的 ResNet50 模型在猫狗数据集上训练的图像分类任务。 | 使用 Yolov8 在 COCO128 数据集上进行目标检测任务，跟踪训练超参数和指标。 |
| [Qwen2 指令微调][demo-qwen2-sft] | [LSTM Google 股票预测][demo-google-stock] |
| :--------: | :--------: |
| [![][demo-qwen2-sft-image]][demo-qwen2-sft] | [![][demo-google-stock-image]][demo-google-stock] |
| 跟踪 Qwen2 大语言模型的指令微调训练，完成简单的指令遵循。 | 使用简单的 LSTM 模型在 Google 股价数据集上训练，实现对未来股价的预测。 |
| [ResNeXt101 音频分类][demo-audio-classification] | [Qwen2-VL COCO数据集微调][demo-qwen2-vl] |
| :--------: | :--------: |
| [![][demo-audio-classification-image]][demo-audio-classification] | [![][demo-qwen2-vl-image]][demo-qwen2-vl] |
| 从ResNet到ResNeXt在音频分类任务上的渐进式实验过程 | 基于Qwen2-VL多模态大模型，在COCO2014数据集上进行Lora微调。 |
[更多案例](https://docs.swanlab.cn/zh/examples/mnist.html)
<br>
## 🏁 快速开始
### 1.安装
```bash
pip install swanlab
```
### 2.登录并获取 API Key
1. 免费[注册账号](https://swanlab.cn)
2. 登录账号，在用户设置 > [API Key](https://swanlab.cn/settings) 里复制您的 API Key
3. 打开终端，输入：
```bash
swanlab login
```
出现提示时，输入您的 API Key，按下回车，完成登陆。
### 3.将 SwanLab 与你的代码集成
```python
import swanlab
# 初始化一个新的swanlab实验
swanlab.init(
project="my-first-ml",
config={'learning-rate': 0.003},
)
# 记录指标
for i in range(10):
swanlab.log({"loss": i, "acc": i})
```
大功告成！前往[SwanLab](https://swanlab.cn)查看你的第一个 SwanLab 实验。
<br>
## 💻 自托管
自托管社区版支持离线查看 SwanLab 仪表盘。
### 离线实验跟踪
在 swanlab.init 中设置`logir`和`mode`这两个参数，即可离线跟踪实验：
```python
...
swanlab.init(
logdir='./logs',
mode='local',
)
...
```
- 参数`mode`设置为`local`，关闭将实验同步到云端
- 参数`logdir`的设置是可选的，它的作用是指定了 SwanLab 日志文件的保存位置（默认保存在`swanlog`文件夹下）
- 日志文件会在跟踪实验的过程中被创建和更新，离线看板的启动也将基于这些日志文件
其他部分和云端使用完全一致。
### 开启离线看板
打开终端，使用下面的指令，开启一个 SwanLab 仪表板:
```bash
swanlab watch ./logs
```
运行完成后，SwanLab 会给你 1 个本地的 URL 链接（默认是[http://127.0.0.1:5092](http://127.0.0.1:5092)）
访问该链接，就可以在浏览器用离线看板查看实验了。
<br>
## 🚗 框架集成
将你最喜欢的框架与 SwanLab 结合使用！
下面是我们已集成的框架列表，欢迎提交 [Issue](https://github.com/swanhubx/swanlab/issues) 来反馈你想要集成的框架。
**基础框架**
- [PyTorch](https://docs.swanlab.cn/guide_cloud/integration/integration-pytorch.html)
- [MindSpore](https://docs.swanlab.cn/guide_cloud/integration/integration-ascend.html)
- [Keras](https://docs.swanlab.cn/guide_cloud/integration/integration-keras.html)
**专有/微调框架**
- [PyTorch Lightning](https://docs.swanlab.cn/guide_cloud/integration/integration-pytorch-lightning.html)
- [HuggingFace Transformers](https://docs.swanlab.cn/guide_cloud/integration/integration-huggingface-transformers.html)
- [OpenMind](https://modelers.cn/docs/zh/openmind-library/1.0.0/basic_tutorial/finetune/finetune_pt.html#%E8%AE%AD%E7%BB%83%E7%9B%91%E6%8E%A7)
- [LLaMA Factory](https://docs.swanlab.cn/guide_cloud/integration/integration-llama-factory.html)
- [Modelscope Swift](https://docs.swanlab.cn/guide_cloud/integration/integration-swift.html)
- [Sentence Transformers](https://docs.swanlab.cn/guide_cloud/integration/integration-sentence-transformers.html)
- [Torchtune](https://docs.swanlab.cn/guide_cloud/integration/integration-pytorch-torchtune.html)
- [XTuner](https://docs.swanlab.cn/guide_cloud/integration/integration-xtuner.html)
- [MMEngine](https://docs.swanlab.cn/guide_cloud/integration/integration-mmengine.html)
- [FastAI](https://docs.swanlab.cn/guide_cloud/integration/integration-fastai.html)
- [LightGBM](https://docs.swanlab.cn/guide_cloud/integration/integration-lightgbm.html)
- [XGBoost](https://docs.swanlab.cn/guide_cloud/integration/integration-xgboost.html)
**计算机视觉**
- [Ultralytics](https://docs.swanlab.cn/guide_cloud/integration/integration-ultralytics.html)
- [MMDetection](https://docs.swanlab.cn/guide_cloud/integration/integration-mmdetection.html)
- [MMSegmentation](https://docs.swanlab.cn/guide_cloud/integration/integration-mmsegmentation.html)
- [PaddleDetection](https://docs.swanlab.cn/guide_cloud/integration/integration-paddledetection.html)
- [PaddleYOLO](https://docs.swanlab.cn/guide_cloud/integration/integration-paddleyolo.html)
**强化学习**
- [Stable Baseline3](https://docs.swanlab.cn/guide_cloud/integration/integration-sb3.html)
- [veRL](https://docs.swanlab.cn/guide_cloud/integration/integration-verl.html)
- [HuggingFace trl](https://docs.swanlab.cn/guide_cloud/integration/integration-huggingface-trl.html)
**其他框架：**
- [Tensorboard](https://docs.swanlab.cn/guide_cloud/integration/integration-tensorboard.html)
- [Weights&Biases](https://docs.swanlab.cn/guide_cloud/integration/integration-wandb.html)
- [HuggingFace Accelerate](https://docs.swanlab.cn/guide_cloud/integration/integration-huggingface-accelerate.html)
- [Hydra](https://docs.swanlab.cn/guide_cloud/integration/integration-hydra.html)
- [Omegaconf](https://docs.swanlab.cn/guide_cloud/integration/integration-omegaconf.html)
- [OpenAI](https://docs.swanlab.cn/guide_cloud/integration/integration-openai.html)
- [ZhipuAI](https://docs.swanlab.cn/guide_cloud/integration/integration-zhipuai.html)
[更多集成](https://docs.swanlab.cn/zh/guide_cloud/integration/integration-pytorch-lightning.html)
<br>
## 🆚 与熟悉的工具的比较
### Tensorboard vs SwanLab
- **☁️ 支持在线使用**：
通过 SwanLab 可以方便地将训练实验在云端在线同步与保存，便于远程查看训练进展、管理历史项目、分享实验链接、发送实时消息通知、多端看实验等。而 Tensorboard 是一个离线的实验跟踪工具。
- **👥 多人协作**：
在进行多人、跨团队的机器学习协作时，通过 SwanLab 可以轻松管理多人的训练项目、分享实验链接、跨空间交流讨论。而 Tensorboard 主要为个人设计，难以进行多人协作和分享实验。
- **💻 持久、集中的仪表板**：
无论你在何处训练模型，无论是在本地计算机上、在实验室集群还是在公有云的 GPU 实例中，你的结果都会记录到同一个集中式仪表板中。而使用 TensorBoard 需要花费时间从不同的机器复制和管理
TFEvent 文件。
- **💪 更强大的表格**：
通过 SwanLab 表格可以查看、搜索、过滤来自不同实验的结果，可以轻松查看数千个模型版本并找到适合不同任务的最佳性能模型。
TensorBoard 不适用于大型项目。
### Weights and Biases vs SwanLab
- Weights and Biases 是一个必须联网使用的闭源 MLOps 平台
- SwanLab 不仅支持联网使用，也支持开源、免费、自托管的版本
<br>
## 👥 社区
### 社区与支持
- [GitHub Issues](https://github.com/SwanHubX/SwanLab/issues)：使用 SwanLab 时遇到的错误和问题
- [电子邮件支持](zeyi.lin@swanhub.co)：反馈关于使用 SwanLab 的问题
- <a href="https://docs.swanlab.cn/guide_cloud/community/online-support.html">微信交流群</a>：交流使用 SwanLab 的问题、分享最新的 AI 技术
### SwanLab README 徽章
如果你喜欢在工作中使用 SwanLab，请将 SwanLab 徽章添加到你的 README 中：
[](https://github.com/swanhubx/swanlab)
```
[](https://github.com/swanhubx/swanlab)
```
### 在论文中引用 SwanLab
如果您发现 SwanLab 对您的研究之旅有帮助，请考虑以下列格式引用：
```bibtex
@software{Zeyilin_SwanLab_2023,
author = {Zeyi Lin, Shaohong Chen, Kang Li, Qiushan Jiang, Zirui Cai, Kaifang Ji and {The SwanLab team}},
doi = {10.5281/zenodo.11100550},
license = {Apache-2.0},
title = {{SwanLab}},
url = {https://github.com/swanhubx/swanlab},
year = {2023}
}
```
### 为 SwanLab 做出贡献
考虑为 SwanLab 做出贡献吗？首先，请花点时间阅读 [贡献指南](CONTRIBUTING.md)。
同时，我们非常欢迎通过社交媒体、活动和会议的分享来支持 SwanLab，衷心感谢！
### 下载 Icon
[SwanLab-Icon-SVG](https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo.svg)
<br>
**Contributors**
<a href="https://github.com/swanhubx/swanlab/graphs/contributors">
<img src="https://contrib.rocks/image?repo=swanhubx/swanlab" />
</a>
<br>
## 📃 协议
本仓库遵循 [Apache 2.0 License](https://github.com/SwanHubX/SwanLab/blob/main/LICENSE) 开源协议
## Star History
[](https://star-history.com/#swanhubx/swanlab&Date)
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[demo-cats-dogs]: https://swanlab.cn/@ZeyiLin/Cats_Dogs_Classification/runs/jzo93k112f15pmx14vtxf/chart
[demo-cats-dogs-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-catsdogs.png
[demo-yolo]: https://swanlab.cn/@ZeyiLin/ultratest/runs/yux7vclmsmmsar9ear7u5/chart
[demo-yolo-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-yolo.png
[demo-qwen2-sft]: https://swanlab.cn/@ZeyiLin/Qwen2-fintune/runs/cfg5f8dzkp6vouxzaxlx6/chart
[demo-qwen2-sft-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-qwen2.png
[demo-google-stock]:https://swanlab.cn/@ZeyiLin/Google-Stock-Prediction/charts
[demo-google-stock-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-lstm.png
[demo-audio-classification]:https://swanlab.cn/@ZeyiLin/PyTorch_Audio_Classification/charts
[demo-audio-classification-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-audio-classification.png
[demo-qwen2-vl]:https://swanlab.cn/@ZeyiLin/Qwen2-VL-finetune/runs/pkgest5xhdn3ukpdy6kv5/chart
[demo-qwen2-vl-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-qwen2-vl.jpg
Raw data
{
"_id": null,
"home_page": null,
"name": "swanlab",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "machine learning, reproducibility, visualization",
"author": null,
"author_email": "Cunyue <team@swanhub.co>, Feudalman <team@swanhub.co>, ZeYi Lin <team@swanhub.co>, KashiwaByte <team@swanhub.co>",
"download_url": "https://files.pythonhosted.org/packages/62/64/29db0121b21b3fecdeb62eed3f9df6833fe919a54cf60f034fab956bfe1a/swanlab-0.4.8.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo-single-dark.svg\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo-single.svg\">\n <img alt=\"SwanLab\" src=\"https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo-single.svg\" width=\"70\" height=\"70\">\n</picture>\n\n<h1>SwanLab</h1>\n\n\u4e00\u4e2a\u5f00\u6e90\u3001\u73b0\u4ee3\u5316\u8bbe\u8ba1\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u8ddf\u8e2a\u4e0e\u53ef\u89c6\u5316\u5de5\u5177 \n\u540c\u65f6\u652f\u6301\u4e91\u7aef/\u79bb\u7ebf\u4f7f\u7528\uff0c\u9002\u914d30+\u4e3b\u6d41\u6846\u67b6\uff0c\u4e0e\u4f60\u7684\u5b9e\u9a8c\u4ee3\u7801\u8f7b\u677e\u96c6\u6210\n\n<a href=\"https://swanlab.cn\">\ud83d\udd25SwanLab \u5728\u7ebf\u7248</a> \u00b7 <a href=\"https://docs.swanlab.cn\">\ud83d\udcc3 \u6587\u6863</a> \u00b7 <a href=\"https://github.com/swanhubx/swanlab/issues\">\u62a5\u544a\u95ee\u9898</a> \u00b7 <a href=\"https://geektechstudio.feishu.cn/share/base/form/shrcnyBlK8OMD0eweoFcc2SvWKc\">\u5efa\u8bae\u53cd\u9988</a> \u00b7 <a href=\"https://docs.swanlab.cn/zh/guide_cloud/general/changelog.html\">\u66f4\u65b0\u65e5\u5fd7</a>\n\n[![][release-shield]][release-link]\n[![][github-stars-shield]][github-stars-link]\n[![][github-issues-shield]][github-issues-shield-link]\n[![][github-contributors-shield]][github-contributors-link]\n[![][license-shield]][license-shield-link] \n[![][swanlab-cloud-shield]][swanlab-cloud-shield-link]\n[![][last-commit-shield]][last-commit-shield-link]\n[![][pypi-version-shield]][pypi-version-shield-link]\n[![][wechat-shield]][wechat-shield-link]\n[![][pypi-downloads-shield]][pypi-downloads-shield-link]\n[![][colab-shield]][colab-shield-link]\n\n\n\n\n\u4e2d\u6587 / [English](README_EN.md) / [\u65e5\u672c\u8a9e](README_JP.md) / [\u0420\u0443\u0441\u0441\u043a\u0438\u0439](README_RU.md)\n\n\ud83d\udc4b \u52a0\u5165\u6211\u4eec\u7684[\u5fae\u4fe1\u7fa4](https://docs.swanlab.cn/zh/guide_cloud/community/online-support.html)\n\n</div>\n\n\n## \u76ee\u5f55\n\n- [\ud83c\udf1f \u6700\u8fd1\u66f4\u65b0](#-\u6700\u8fd1\u66f4\u65b0)\n- [\ud83d\udc4b\ud83c\udffb \u4ec0\u4e48\u662fSwanLab](#-\u4ec0\u4e48\u662fswanlab)\n- [\ud83d\udcc3 \u5728\u7ebf\u6f14\u793a](#-\u5728\u7ebf\u6f14\u793a)\n- [\ud83c\udfc1 \u5feb\u901f\u5f00\u59cb](#-\u5feb\u901f\u5f00\u59cb)\n- [\ud83d\udcbb \u81ea\u6258\u7ba1](#-\u81ea\u6258\u7ba1)\n- [\ud83d\ude97 \u6846\u67b6\u96c6\u6210](#-\u6846\u67b6\u96c6\u6210)\n- [\ud83c\udd9a \u4e0e\u719f\u6089\u7684\u5de5\u5177\u7684\u6bd4\u8f83](#-\u4e0e\u719f\u6089\u7684\u5de5\u5177\u7684\u6bd4\u8f83)\n- [\ud83d\udc65 \u793e\u533a](#-\u793e\u533a)\n- [\ud83d\udcc3 \u534f\u8bae](#-\u534f\u8bae)\n\n<br/>\n\n\n## \ud83c\udf1f \u6700\u8fd1\u66f4\u65b0\n\n- 2025.02.09\uff1a\u6211\u4eec\u4e0e [veRL](https://github.com/volcengine/verl) \u5b8c\u6210\u4e86\u8054\u5408\u96c6\u6210\uff0c\u73b0\u5728\u4f60\u53ef\u4ee5\u5728veRL\u4e2d\u4f7f\u7528SwanLab\u6765**\u8ddf\u8e2a\u548c\u53ef\u89c6\u5316\u5927\u6a21\u578b\u5f3a\u5316\u5b66\u4e60\u5b9e\u9a8c**\uff0c[\u4f7f\u7528\u6307\u5f15](https://docs.swanlab.cn/guide_cloud/integration/integration-verl.html)\u3002\n\n- 2025.02.05\uff1a`swanlab.log`\u652f\u6301\u5d4c\u5957\u5b57\u5178 [#812](https://github.com/SwanHubX/SwanLab/pull/812)\uff0c\u9002\u914dJax\u6846\u67b6\u7279\u6027\uff1b\u652f\u6301`name`\u4e0e`notes`\u53c2\u6570\uff1b\n\n- 2025.01.22\uff1a\u65b0\u589e`sync_tensorboardX`\u4e0e`sync_tensorboard_torch`\u529f\u80fd\uff0c\u652f\u6301\u4e0e\u6b64\u4e24\u79cdTensorBoard\u6846\u67b6\u540c\u6b65\u5b9e\u9a8c\u8ddf\u8e2a\uff1b\n\n- 2025.01.17\uff1a\u65b0\u589e`sync_wandb`\u529f\u80fd\uff0c[\u6587\u6863](https://docs.swanlab.cn/guide_cloud/integration/integration-wandb.html)\uff0c\u652f\u6301\u4e0eWeights & Biases\u5b9e\u9a8c\u8ddf\u8e2a\u540c\u6b65\uff1b\u5927\u5e45\u6539\u8fdb\u4e86\u65e5\u5fd7\u6e32\u67d3\u6027\u80fd\n\n- 2025.01.11\uff1a\u4e91\u7aef\u7248\u5927\u5e45\u4f18\u5316\u4e86\u9879\u76ee\u8868\u683c\u7684\u6027\u80fd\uff0c\u5e76\u652f\u6301\u62d6\u62fd\u3001\u6392\u5e8f\u3001\u7b5b\u9009\u7b49\u4ea4\u4e92\n\n- 2025.01.01\uff1a\u65b0\u589e\u6298\u7ebf\u56fe**\u6301\u4e45\u5316\u5e73\u6ed1**\u3001\u6298\u7ebf\u56fe\u62d6\u62fd\u5f0f\u6539\u53d8\u5927\u5c0f\uff0c\u4f18\u5316\u56fe\u8868\u6d4f\u89c8\u4f53\u9a8c\n\n- 2024.12.22\uff1a\u6211\u4eec\u4e0e [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) \u5b8c\u6210\u4e86\u8054\u5408\u96c6\u6210\uff0c\u73b0\u5728\u4f60\u53ef\u4ee5\u5728LLaMA Factory\u4e2d\u4f7f\u7528SwanLab\u6765**\u8ddf\u8e2a\u548c\u53ef\u89c6\u5316\u5927\u6a21\u578b\u5fae\u8c03\u5b9e\u9a8c**\uff0c[\u4f7f\u7528\u6307\u5f15](https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#use-swanlab-logger)\u3002\n\n- 2024.12.15\uff1a**\u786c\u4ef6\u76d1\u63a7\uff080.4.0\uff09** \u529f\u80fd\u4e0a\u7ebf\uff0c\u652f\u6301CPU\u3001NPU\uff08Ascend\uff09\u3001GPU\uff08Nvidia\uff09\u7684\u7cfb\u7edf\u7ea7\u4fe1\u606f\u8bb0\u5f55\u4e0e\u76d1\u63a7\u3002\n\n\n<details><summary>\u5b8c\u6574\u66f4\u65b0\u65e5\u5fd7</summary>\n\n- 2024.12.06\uff1a\u65b0\u589e\u5bf9[LightGBM](https://docs.swanlab.cn/guide_cloud/integration/integration-lightgbm.html)\u3001[XGBoost](https://docs.swanlab.cn/guide_cloud/integration/integration-xgboost.html)\u7684\u96c6\u6210\uff1b\u63d0\u9ad8\u4e86\u5bf9\u65e5\u5fd7\u8bb0\u5f55\u5355\u884c\u957f\u5ea6\u7684\u9650\u5236\u3002\n\n- 2024.11.26\uff1a\u73af\u5883\u9009\u9879\u5361-\u786c\u4ef6\u90e8\u5206\u652f\u6301\u8bc6\u522b**\u534e\u4e3a\u6607\u817eNPU**\u4e0e**\u9cb2\u9e4fCPU**\uff1b\u4e91\u5382\u5546\u90e8\u5206\u652f\u6301\u8bc6\u522b\u9752\u4e91**\u57fa\u77f3\u667a\u7b97**\u3002\n\n</details>\n\n<br>\n\n## \ud83d\udc4b\ud83c\udffb \u4ec0\u4e48\u662fSwanLab\n\nSwanLab \u662f\u4e00\u6b3e\u5f00\u6e90\u3001\u8f7b\u91cf\u7684 AI \u6a21\u578b\u8bad\u7ec3\u8ddf\u8e2a\u4e0e\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u8ddf\u8e2a\u3001\u8bb0\u5f55\u3001\u6bd4\u8f83\u3001\u548c\u534f\u4f5c\u5b9e\u9a8c\u7684\u5e73\u53f0\u3002\n\nSwanLab \u9762\u5411\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u8005\uff0c\u8bbe\u8ba1\u4e86\u53cb\u597d\u7684Python API \u548c\u6f02\u4eae\u7684UI\u754c\u9762\uff0c\u5e76\u63d0\u4f9b**\u8bad\u7ec3\u53ef\u89c6\u5316\u3001\u81ea\u52a8\u65e5\u5fd7\u8bb0\u5f55\u3001\u8d85\u53c2\u6570\u8bb0\u5f55\u3001\u5b9e\u9a8c\u5bf9\u6bd4\u3001\u591a\u4eba\u534f\u540c**\u7b49\u529f\u80fd\u3002\u5728SwanLab\u4e0a\uff0c\u7814\u7a76\u8005\u80fd\u57fa\u4e8e\u76f4\u89c2\u7684\u53ef\u89c6\u5316\u56fe\u8868\u53d1\u73b0\u8bad\u7ec3\u95ee\u9898\uff0c\u5bf9\u6bd4\u591a\u4e2a\u5b9e\u9a8c\u627e\u5230\u7814\u7a76\u7075\u611f\uff0c\u5e76\u901a\u8fc7**\u5728\u7ebf\u7f51\u9875**\u7684\u5206\u4eab\u4e0e\u57fa\u4e8e\u7ec4\u7ec7\u7684**\u591a\u4eba\u534f\u540c\u8bad\u7ec3**\uff0c\u6253\u7834\u56e2\u961f\u6c9f\u901a\u7684\u58c1\u5792\uff0c\u63d0\u9ad8\u7ec4\u7ec7\u8bad\u7ec3\u6548\u7387\u3002\n\n\u4ee5\u4e0b\u662f\u5176\u6838\u5fc3\u7279\u6027\u5217\u8868\uff1a\n\n**1. \ud83d\udcca \u5b9e\u9a8c\u6307\u6807\u4e0e\u8d85\u53c2\u6570\u8ddf\u8e2a**: \u6781\u7b80\u7684\u4ee3\u7801\u5d4c\u5165\u60a8\u7684\u673a\u5668\u5b66\u4e60 pipeline\uff0c\u8ddf\u8e2a\u8bb0\u5f55\u8bad\u7ec3\u5173\u952e\u6307\u6807\n\n- \u652f\u6301**\u4e91\u7aef**\u4f7f\u7528\uff08\u7c7b\u4f3cWeights & Biases\uff09\uff0c\u968f\u65f6\u968f\u5730\u67e5\u770b\u8bad\u7ec3\u8fdb\u5c55\u3002[\u624b\u673a\u770b\u5b9e\u9a8c\u7684\u65b9\u6cd5](https://docs.swanlab.cn/guide_cloud/general/app.html)\n- \u652f\u6301**\u8d85\u53c2\u6570\u8bb0\u5f55**\u4e0e\u8868\u683c\u5c55\u793a\n- **\u652f\u6301\u7684\u5143\u6570\u636e\u7c7b\u578b**\uff1a\u6807\u91cf\u6307\u6807\u3001\u56fe\u50cf\u3001\u97f3\u9891\u3001\u6587\u672c\u3001...\n- **\u652f\u6301\u7684\u56fe\u8868\u7c7b\u578b**\uff1a\u6298\u7ebf\u56fe\u3001\u5a92\u4f53\u56fe\uff08\u56fe\u50cf\u3001\u97f3\u9891\u3001\u6587\u672c\uff09\u3001...\n- **\u540e\u53f0\u81ea\u52a8\u8bb0\u5f55**\uff1a\u65e5\u5fd7logging\u3001\u786c\u4ef6\u73af\u5883\u3001Git \u4ed3\u5e93\u3001Python \u73af\u5883\u3001Python \u5e93\u5217\u8868\u3001\u9879\u76ee\u8fd0\u884c\u76ee\u5f55\n\n**2. \u26a1\ufe0f \u5168\u9762\u7684\u6846\u67b6\u96c6\u6210**: PyTorch\u3001\ud83e\udd17HuggingFace Transformers\u3001PyTorch Lightning\u3001\ud83e\udd99LLaMA Factory\u3001MMDetection\u3001Ultralytics\u3001PaddleDetetion\u3001LightGBM\u3001XGBoost\u3001Keras\u3001Tensorboard\u3001Weights&Biases\u3001OpenAI\u3001Swift\u3001XTuner\u3001Stable Baseline3\u3001Hydra \u5728\u5185\u7684 **30+** \u6846\u67b6\n\n\n\n**3. \ud83d\udcbb \u786c\u4ef6\u76d1\u63a7**: \u652f\u6301\u5b9e\u65f6\u8bb0\u5f55\u4e0e\u76d1\u63a7CPU\u3001NPU\uff08\u6607\u817eAscend\uff09\u3001GPU\uff08\u82f1\u4f1f\u8fbeNvidia\uff09\u3001\u5185\u5b58\u7684\u7cfb\u7edf\u7ea7\u786c\u4ef6\u6307\u6807\n\n**4. \ud83d\udce6 \u5b9e\u9a8c\u7ba1\u7406**: \u901a\u8fc7\u4e13\u4e3a\u8bad\u7ec3\u573a\u666f\u8bbe\u8ba1\u7684\u96c6\u4e2d\u5f0f\u4eea\u8868\u677f\uff0c\u901a\u8fc7\u6574\u4f53\u89c6\u56fe\u901f\u89c8\u5168\u5c40\uff0c\u5feb\u901f\u7ba1\u7406\u591a\u4e2a\u9879\u76ee\u4e0e\u5b9e\u9a8c\n\n**4. \ud83c\udd9a \u6bd4\u8f83\u7ed3\u679c**: \u901a\u8fc7\u5728\u7ebf\u8868\u683c\u4e0e\u5bf9\u6bd4\u56fe\u8868\u6bd4\u8f83\u4e0d\u540c\u5b9e\u9a8c\u7684\u8d85\u53c2\u6570\u548c\u7ed3\u679c\uff0c\u6316\u6398\u8fed\u4ee3\u7075\u611f\n\n**5. \ud83d\udc65 \u5728\u7ebf\u534f\u4f5c**: \u60a8\u53ef\u4ee5\u4e0e\u56e2\u961f\u8fdb\u884c\u534f\u4f5c\u5f0f\u8bad\u7ec3\uff0c\u652f\u6301\u5c06\u5b9e\u9a8c\u5b9e\u65f6\u540c\u6b65\u5728\u4e00\u4e2a\u9879\u76ee\u4e0b\uff0c\u60a8\u53ef\u4ee5\u5728\u7ebf\u67e5\u770b\u56e2\u961f\u7684\u8bad\u7ec3\u8bb0\u5f55\uff0c\u57fa\u4e8e\u7ed3\u679c\u53d1\u8868\u770b\u6cd5\u4e0e\u5efa\u8bae\n\n**6. \u2709\ufe0f \u5206\u4eab\u7ed3\u679c**: \u590d\u5236\u548c\u53d1\u9001\u6301\u4e45\u7684 URL \u6765\u5171\u4eab\u6bcf\u4e2a\u5b9e\u9a8c\uff0c\u65b9\u4fbf\u5730\u53d1\u9001\u7ed9\u4f19\u4f34\uff0c\u6216\u5d4c\u5165\u5230\u5728\u7ebf\u7b14\u8bb0\u4e2d\n\n**7. \ud83d\udcbb \u652f\u6301\u81ea\u6258\u7ba1**: \u652f\u6301\u79bb\u7ebf\u73af\u5883\u4f7f\u7528\uff0c\u81ea\u6258\u7ba1\u7684\u793e\u533a\u7248\u540c\u6837\u53ef\u4ee5\u67e5\u770b\u4eea\u8868\u76d8\u4e0e\u7ba1\u7406\u5b9e\u9a8c\n\n> \\[!IMPORTANT]\n>\n> **\u6536\u85cf\u9879\u76ee**\uff0c\u4f60\u5c06\u4ece GitHub \u4e0a\u65e0\u5ef6\u8fdf\u5730\u63a5\u6536\u6240\u6709\u53d1\u5e03\u901a\u77e5\uff5e \u2b50\ufe0f\n\n\n\n<br>\n\n## \ud83d\udcc3 \u5728\u7ebf\u6f14\u793a\n\n\u6765\u770b\u770b SwanLab \u7684\u5728\u7ebf\u6f14\u793a\uff1a\n\n| [ResNet50 \u732b\u72d7\u5206\u7c7b][demo-cats-dogs] | [Yolov8-COCO128 \u76ee\u6807\u68c0\u6d4b][demo-yolo] |\n| :--------: | :--------: |\n| [![][demo-cats-dogs-image]][demo-cats-dogs] | [![][demo-yolo-image]][demo-yolo] |\n| \u8ddf\u8e2a\u4e00\u4e2a\u7b80\u5355\u7684 ResNet50 \u6a21\u578b\u5728\u732b\u72d7\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u7684\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u3002 | \u4f7f\u7528 Yolov8 \u5728 COCO128 \u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\uff0c\u8ddf\u8e2a\u8bad\u7ec3\u8d85\u53c2\u6570\u548c\u6307\u6807\u3002 |\n\n| [Qwen2 \u6307\u4ee4\u5fae\u8c03][demo-qwen2-sft] | [LSTM Google \u80a1\u7968\u9884\u6d4b][demo-google-stock] |\n| :--------: | :--------: |\n| [![][demo-qwen2-sft-image]][demo-qwen2-sft] | [![][demo-google-stock-image]][demo-google-stock] |\n| \u8ddf\u8e2a Qwen2 \u5927\u8bed\u8a00\u6a21\u578b\u7684\u6307\u4ee4\u5fae\u8c03\u8bad\u7ec3\uff0c\u5b8c\u6210\u7b80\u5355\u7684\u6307\u4ee4\u9075\u5faa\u3002 | \u4f7f\u7528\u7b80\u5355\u7684 LSTM \u6a21\u578b\u5728 Google \u80a1\u4ef7\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\uff0c\u5b9e\u73b0\u5bf9\u672a\u6765\u80a1\u4ef7\u7684\u9884\u6d4b\u3002 |\n\n| [ResNeXt101 \u97f3\u9891\u5206\u7c7b][demo-audio-classification] | [Qwen2-VL COCO\u6570\u636e\u96c6\u5fae\u8c03][demo-qwen2-vl] |\n| :--------: | :--------: |\n| [![][demo-audio-classification-image]][demo-audio-classification] | [![][demo-qwen2-vl-image]][demo-qwen2-vl] |\n| \u4eceResNet\u5230ResNeXt\u5728\u97f3\u9891\u5206\u7c7b\u4efb\u52a1\u4e0a\u7684\u6e10\u8fdb\u5f0f\u5b9e\u9a8c\u8fc7\u7a0b | \u57fa\u4e8eQwen2-VL\u591a\u6a21\u6001\u5927\u6a21\u578b\uff0c\u5728COCO2014\u6570\u636e\u96c6\u4e0a\u8fdb\u884cLora\u5fae\u8c03\u3002 |\n\n\n[\u66f4\u591a\u6848\u4f8b](https://docs.swanlab.cn/zh/examples/mnist.html)\n\n<br>\n\n## \ud83c\udfc1 \u5feb\u901f\u5f00\u59cb\n\n### 1.\u5b89\u88c5\n\n```bash\npip install swanlab\n```\n\n### 2.\u767b\u5f55\u5e76\u83b7\u53d6 API Key\n\n1. \u514d\u8d39[\u6ce8\u518c\u8d26\u53f7](https://swanlab.cn)\n\n2. \u767b\u5f55\u8d26\u53f7\uff0c\u5728\u7528\u6237\u8bbe\u7f6e > [API Key](https://swanlab.cn/settings) \u91cc\u590d\u5236\u60a8\u7684 API Key\n\n3. \u6253\u5f00\u7ec8\u7aef\uff0c\u8f93\u5165\uff1a\n\n```bash\nswanlab login\n```\n\n\u51fa\u73b0\u63d0\u793a\u65f6\uff0c\u8f93\u5165\u60a8\u7684 API Key\uff0c\u6309\u4e0b\u56de\u8f66\uff0c\u5b8c\u6210\u767b\u9646\u3002\n\n### 3.\u5c06 SwanLab \u4e0e\u4f60\u7684\u4ee3\u7801\u96c6\u6210\n\n```python\nimport swanlab\n\n# \u521d\u59cb\u5316\u4e00\u4e2a\u65b0\u7684swanlab\u5b9e\u9a8c\nswanlab.init(\n project=\"my-first-ml\",\n config={'learning-rate': 0.003},\n)\n\n# \u8bb0\u5f55\u6307\u6807\nfor i in range(10):\n swanlab.log({\"loss\": i, \"acc\": i})\n```\n\n\u5927\u529f\u544a\u6210\uff01\u524d\u5f80[SwanLab](https://swanlab.cn)\u67e5\u770b\u4f60\u7684\u7b2c\u4e00\u4e2a SwanLab \u5b9e\u9a8c\u3002\n\n<br>\n\n## \ud83d\udcbb \u81ea\u6258\u7ba1\n\n\u81ea\u6258\u7ba1\u793e\u533a\u7248\u652f\u6301\u79bb\u7ebf\u67e5\u770b SwanLab \u4eea\u8868\u76d8\u3002\n\n### \u79bb\u7ebf\u5b9e\u9a8c\u8ddf\u8e2a\n\n\u5728 swanlab.init \u4e2d\u8bbe\u7f6e`logir`\u548c`mode`\u8fd9\u4e24\u4e2a\u53c2\u6570\uff0c\u5373\u53ef\u79bb\u7ebf\u8ddf\u8e2a\u5b9e\u9a8c\uff1a\n\n```python\n...\n\nswanlab.init(\n logdir='./logs',\n mode='local',\n)\n\n...\n```\n\n- \u53c2\u6570`mode`\u8bbe\u7f6e\u4e3a`local`\uff0c\u5173\u95ed\u5c06\u5b9e\u9a8c\u540c\u6b65\u5230\u4e91\u7aef\n\n- \u53c2\u6570`logdir`\u7684\u8bbe\u7f6e\u662f\u53ef\u9009\u7684\uff0c\u5b83\u7684\u4f5c\u7528\u662f\u6307\u5b9a\u4e86 SwanLab \u65e5\u5fd7\u6587\u4ef6\u7684\u4fdd\u5b58\u4f4d\u7f6e\uff08\u9ed8\u8ba4\u4fdd\u5b58\u5728`swanlog`\u6587\u4ef6\u5939\u4e0b\uff09\n\n - \u65e5\u5fd7\u6587\u4ef6\u4f1a\u5728\u8ddf\u8e2a\u5b9e\u9a8c\u7684\u8fc7\u7a0b\u4e2d\u88ab\u521b\u5efa\u548c\u66f4\u65b0\uff0c\u79bb\u7ebf\u770b\u677f\u7684\u542f\u52a8\u4e5f\u5c06\u57fa\u4e8e\u8fd9\u4e9b\u65e5\u5fd7\u6587\u4ef6\n\n\u5176\u4ed6\u90e8\u5206\u548c\u4e91\u7aef\u4f7f\u7528\u5b8c\u5168\u4e00\u81f4\u3002\n\n### \u5f00\u542f\u79bb\u7ebf\u770b\u677f\n\n\u6253\u5f00\u7ec8\u7aef\uff0c\u4f7f\u7528\u4e0b\u9762\u7684\u6307\u4ee4\uff0c\u5f00\u542f\u4e00\u4e2a SwanLab \u4eea\u8868\u677f:\n\n```bash\nswanlab watch ./logs\n```\n\n\u8fd0\u884c\u5b8c\u6210\u540e\uff0cSwanLab \u4f1a\u7ed9\u4f60 1 \u4e2a\u672c\u5730\u7684 URL \u94fe\u63a5\uff08\u9ed8\u8ba4\u662f[http://127.0.0.1:5092](http://127.0.0.1:5092)\uff09\n\n\u8bbf\u95ee\u8be5\u94fe\u63a5\uff0c\u5c31\u53ef\u4ee5\u5728\u6d4f\u89c8\u5668\u7528\u79bb\u7ebf\u770b\u677f\u67e5\u770b\u5b9e\u9a8c\u4e86\u3002\n\n<br>\n\n## \ud83d\ude97 \u6846\u67b6\u96c6\u6210\n\n\u5c06\u4f60\u6700\u559c\u6b22\u7684\u6846\u67b6\u4e0e SwanLab \u7ed3\u5408\u4f7f\u7528\uff01 \n\u4e0b\u9762\u662f\u6211\u4eec\u5df2\u96c6\u6210\u7684\u6846\u67b6\u5217\u8868\uff0c\u6b22\u8fce\u63d0\u4ea4 [Issue](https://github.com/swanhubx/swanlab/issues) \u6765\u53cd\u9988\u4f60\u60f3\u8981\u96c6\u6210\u7684\u6846\u67b6\u3002\n\n**\u57fa\u7840\u6846\u67b6**\n- [PyTorch](https://docs.swanlab.cn/guide_cloud/integration/integration-pytorch.html)\n- [MindSpore](https://docs.swanlab.cn/guide_cloud/integration/integration-ascend.html)\n- [Keras](https://docs.swanlab.cn/guide_cloud/integration/integration-keras.html)\n\n**\u4e13\u6709/\u5fae\u8c03\u6846\u67b6**\n- [PyTorch Lightning](https://docs.swanlab.cn/guide_cloud/integration/integration-pytorch-lightning.html)\n- [HuggingFace Transformers](https://docs.swanlab.cn/guide_cloud/integration/integration-huggingface-transformers.html)\n- [OpenMind](https://modelers.cn/docs/zh/openmind-library/1.0.0/basic_tutorial/finetune/finetune_pt.html#%E8%AE%AD%E7%BB%83%E7%9B%91%E6%8E%A7)\n- [LLaMA Factory](https://docs.swanlab.cn/guide_cloud/integration/integration-llama-factory.html)\n- [Modelscope Swift](https://docs.swanlab.cn/guide_cloud/integration/integration-swift.html)\n- [Sentence Transformers](https://docs.swanlab.cn/guide_cloud/integration/integration-sentence-transformers.html)\n- [Torchtune](https://docs.swanlab.cn/guide_cloud/integration/integration-pytorch-torchtune.html)\n- [XTuner](https://docs.swanlab.cn/guide_cloud/integration/integration-xtuner.html)\n- [MMEngine](https://docs.swanlab.cn/guide_cloud/integration/integration-mmengine.html)\n- [FastAI](https://docs.swanlab.cn/guide_cloud/integration/integration-fastai.html)\n- [LightGBM](https://docs.swanlab.cn/guide_cloud/integration/integration-lightgbm.html)\n- [XGBoost](https://docs.swanlab.cn/guide_cloud/integration/integration-xgboost.html)\n\n\n**\u8ba1\u7b97\u673a\u89c6\u89c9**\n- [Ultralytics](https://docs.swanlab.cn/guide_cloud/integration/integration-ultralytics.html)\n- [MMDetection](https://docs.swanlab.cn/guide_cloud/integration/integration-mmdetection.html)\n- [MMSegmentation](https://docs.swanlab.cn/guide_cloud/integration/integration-mmsegmentation.html)\n- [PaddleDetection](https://docs.swanlab.cn/guide_cloud/integration/integration-paddledetection.html)\n- [PaddleYOLO](https://docs.swanlab.cn/guide_cloud/integration/integration-paddleyolo.html)\n\n**\u5f3a\u5316\u5b66\u4e60**\n- [Stable Baseline3](https://docs.swanlab.cn/guide_cloud/integration/integration-sb3.html)\n- [veRL](https://docs.swanlab.cn/guide_cloud/integration/integration-verl.html)\n- [HuggingFace trl](https://docs.swanlab.cn/guide_cloud/integration/integration-huggingface-trl.html)\n\n**\u5176\u4ed6\u6846\u67b6\uff1a**\n- [Tensorboard](https://docs.swanlab.cn/guide_cloud/integration/integration-tensorboard.html)\n- [Weights&Biases](https://docs.swanlab.cn/guide_cloud/integration/integration-wandb.html)\n- [HuggingFace Accelerate](https://docs.swanlab.cn/guide_cloud/integration/integration-huggingface-accelerate.html)\n- [Hydra](https://docs.swanlab.cn/guide_cloud/integration/integration-hydra.html)\n- [Omegaconf](https://docs.swanlab.cn/guide_cloud/integration/integration-omegaconf.html)\n- [OpenAI](https://docs.swanlab.cn/guide_cloud/integration/integration-openai.html)\n- [ZhipuAI](https://docs.swanlab.cn/guide_cloud/integration/integration-zhipuai.html)\n\n[\u66f4\u591a\u96c6\u6210](https://docs.swanlab.cn/zh/guide_cloud/integration/integration-pytorch-lightning.html)\n\n<br>\n\n## \ud83c\udd9a \u4e0e\u719f\u6089\u7684\u5de5\u5177\u7684\u6bd4\u8f83\n\n### Tensorboard vs SwanLab\n\n- **\u2601\ufe0f \u652f\u6301\u5728\u7ebf\u4f7f\u7528**\uff1a\n \u901a\u8fc7 SwanLab \u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06\u8bad\u7ec3\u5b9e\u9a8c\u5728\u4e91\u7aef\u5728\u7ebf\u540c\u6b65\u4e0e\u4fdd\u5b58\uff0c\u4fbf\u4e8e\u8fdc\u7a0b\u67e5\u770b\u8bad\u7ec3\u8fdb\u5c55\u3001\u7ba1\u7406\u5386\u53f2\u9879\u76ee\u3001\u5206\u4eab\u5b9e\u9a8c\u94fe\u63a5\u3001\u53d1\u9001\u5b9e\u65f6\u6d88\u606f\u901a\u77e5\u3001\u591a\u7aef\u770b\u5b9e\u9a8c\u7b49\u3002\u800c Tensorboard \u662f\u4e00\u4e2a\u79bb\u7ebf\u7684\u5b9e\u9a8c\u8ddf\u8e2a\u5de5\u5177\u3002\n\n- **\ud83d\udc65 \u591a\u4eba\u534f\u4f5c**\uff1a\n \u5728\u8fdb\u884c\u591a\u4eba\u3001\u8de8\u56e2\u961f\u7684\u673a\u5668\u5b66\u4e60\u534f\u4f5c\u65f6\uff0c\u901a\u8fc7 SwanLab \u53ef\u4ee5\u8f7b\u677e\u7ba1\u7406\u591a\u4eba\u7684\u8bad\u7ec3\u9879\u76ee\u3001\u5206\u4eab\u5b9e\u9a8c\u94fe\u63a5\u3001\u8de8\u7a7a\u95f4\u4ea4\u6d41\u8ba8\u8bba\u3002\u800c Tensorboard \u4e3b\u8981\u4e3a\u4e2a\u4eba\u8bbe\u8ba1\uff0c\u96be\u4ee5\u8fdb\u884c\u591a\u4eba\u534f\u4f5c\u548c\u5206\u4eab\u5b9e\u9a8c\u3002\n\n- **\ud83d\udcbb \u6301\u4e45\u3001\u96c6\u4e2d\u7684\u4eea\u8868\u677f**\uff1a\n \u65e0\u8bba\u4f60\u5728\u4f55\u5904\u8bad\u7ec3\u6a21\u578b\uff0c\u65e0\u8bba\u662f\u5728\u672c\u5730\u8ba1\u7b97\u673a\u4e0a\u3001\u5728\u5b9e\u9a8c\u5ba4\u96c6\u7fa4\u8fd8\u662f\u5728\u516c\u6709\u4e91\u7684 GPU \u5b9e\u4f8b\u4e2d\uff0c\u4f60\u7684\u7ed3\u679c\u90fd\u4f1a\u8bb0\u5f55\u5230\u540c\u4e00\u4e2a\u96c6\u4e2d\u5f0f\u4eea\u8868\u677f\u4e2d\u3002\u800c\u4f7f\u7528 TensorBoard \u9700\u8981\u82b1\u8d39\u65f6\u95f4\u4ece\u4e0d\u540c\u7684\u673a\u5668\u590d\u5236\u548c\u7ba1\u7406\n TFEvent \u6587\u4ef6\u3002\n\n- **\ud83d\udcaa \u66f4\u5f3a\u5927\u7684\u8868\u683c**\uff1a\n \u901a\u8fc7 SwanLab \u8868\u683c\u53ef\u4ee5\u67e5\u770b\u3001\u641c\u7d22\u3001\u8fc7\u6ee4\u6765\u81ea\u4e0d\u540c\u5b9e\u9a8c\u7684\u7ed3\u679c\uff0c\u53ef\u4ee5\u8f7b\u677e\u67e5\u770b\u6570\u5343\u4e2a\u6a21\u578b\u7248\u672c\u5e76\u627e\u5230\u9002\u5408\u4e0d\u540c\u4efb\u52a1\u7684\u6700\u4f73\u6027\u80fd\u6a21\u578b\u3002\n TensorBoard \u4e0d\u9002\u7528\u4e8e\u5927\u578b\u9879\u76ee\u3002\n\n### Weights and Biases vs SwanLab\n\n- Weights and Biases \u662f\u4e00\u4e2a\u5fc5\u987b\u8054\u7f51\u4f7f\u7528\u7684\u95ed\u6e90 MLOps \u5e73\u53f0\n\n- SwanLab \u4e0d\u4ec5\u652f\u6301\u8054\u7f51\u4f7f\u7528\uff0c\u4e5f\u652f\u6301\u5f00\u6e90\u3001\u514d\u8d39\u3001\u81ea\u6258\u7ba1\u7684\u7248\u672c\n\n<br>\n\n## \ud83d\udc65 \u793e\u533a\n\n### \u793e\u533a\u4e0e\u652f\u6301\n\n- [GitHub Issues](https://github.com/SwanHubX/SwanLab/issues)\uff1a\u4f7f\u7528 SwanLab \u65f6\u9047\u5230\u7684\u9519\u8bef\u548c\u95ee\u9898\n- [\u7535\u5b50\u90ae\u4ef6\u652f\u6301](zeyi.lin@swanhub.co)\uff1a\u53cd\u9988\u5173\u4e8e\u4f7f\u7528 SwanLab \u7684\u95ee\u9898\n- <a href=\"https://docs.swanlab.cn/guide_cloud/community/online-support.html\">\u5fae\u4fe1\u4ea4\u6d41\u7fa4</a>\uff1a\u4ea4\u6d41\u4f7f\u7528 SwanLab \u7684\u95ee\u9898\u3001\u5206\u4eab\u6700\u65b0\u7684 AI \u6280\u672f\n\n### SwanLab README \u5fbd\u7ae0\n\n\u5982\u679c\u4f60\u559c\u6b22\u5728\u5de5\u4f5c\u4e2d\u4f7f\u7528 SwanLab\uff0c\u8bf7\u5c06 SwanLab \u5fbd\u7ae0\u6dfb\u52a0\u5230\u4f60\u7684 README \u4e2d\uff1a\n\n[](https://github.com/swanhubx/swanlab)\n\n```\n[](https://github.com/swanhubx/swanlab)\n```\n\n### \u5728\u8bba\u6587\u4e2d\u5f15\u7528 SwanLab\n\n\u5982\u679c\u60a8\u53d1\u73b0 SwanLab \u5bf9\u60a8\u7684\u7814\u7a76\u4e4b\u65c5\u6709\u5e2e\u52a9\uff0c\u8bf7\u8003\u8651\u4ee5\u4e0b\u5217\u683c\u5f0f\u5f15\u7528\uff1a\n\n```bibtex\n@software{Zeyilin_SwanLab_2023,\n author = {Zeyi Lin, Shaohong Chen, Kang Li, Qiushan Jiang, Zirui Cai, Kaifang Ji and {The SwanLab team}},\n doi = {10.5281/zenodo.11100550},\n license = {Apache-2.0},\n title = {{SwanLab}},\n url = {https://github.com/swanhubx/swanlab},\n year = {2023}\n}\n```\n\n### \u4e3a SwanLab \u505a\u51fa\u8d21\u732e\n\n\u8003\u8651\u4e3a SwanLab \u505a\u51fa\u8d21\u732e\u5417\uff1f\u9996\u5148\uff0c\u8bf7\u82b1\u70b9\u65f6\u95f4\u9605\u8bfb [\u8d21\u732e\u6307\u5357](CONTRIBUTING.md)\u3002\n\n\u540c\u65f6\uff0c\u6211\u4eec\u975e\u5e38\u6b22\u8fce\u901a\u8fc7\u793e\u4ea4\u5a92\u4f53\u3001\u6d3b\u52a8\u548c\u4f1a\u8bae\u7684\u5206\u4eab\u6765\u652f\u6301 SwanLab\uff0c\u8877\u5fc3\u611f\u8c22\uff01\n\n### \u4e0b\u8f7d Icon\n\n[SwanLab-Icon-SVG](https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo.svg)\n\n<br>\n\n**Contributors**\n\n<a href=\"https://github.com/swanhubx/swanlab/graphs/contributors\">\n <img src=\"https://contrib.rocks/image?repo=swanhubx/swanlab\" />\n</a>\n\n<br>\n\n## \ud83d\udcc3 \u534f\u8bae\n\n\u672c\u4ed3\u5e93\u9075\u5faa [Apache 2.0 License](https://github.com/SwanHubX/SwanLab/blob/main/LICENSE) \u5f00\u6e90\u534f\u8bae\n\n## Star History\n\n[](https://star-history.com/#swanhubx/swanlab&Date)\n\n<!-- link -->\n\n[release-shield]: https://img.shields.io/github/v/release/swanhubx/swanlab?color=369eff&labelColor=black&logo=github&style=flat-square\n[release-link]: https://github.com/swanhubx/swanlab/releases\n\n[license-shield]: https://img.shields.io/badge/license-apache%202.0-white?labelColor=black&style=flat-square\n[license-shield-link]: https://github.com/SwanHubX/SwanLab/blob/main/LICENSE\n\n[last-commit-shield]: https://img.shields.io/github/last-commit/swanhubx/swanlab?color=c4f042&labelColor=black&style=flat-square\n[last-commit-shield-link]: https://github.com/swanhubx/swanlab/commits/main\n\n[pypi-version-shield]: https://img.shields.io/pypi/v/swanlab?color=orange&labelColor=black&style=flat-square\n[pypi-version-shield-link]: https://pypi.org/project/swanlab/\n\n[pypi-downloads-shield]: https://static.pepy.tech/badge/swanlab?labelColor=black&style=flat-square\n[pypi-downloads-shield-link]: https://pepy.tech/project/swanlab\n\n[swanlab-cloud-shield]: https://img.shields.io/badge/Product-SwanLab\u4e91\u7aef\u7248-636a3f?labelColor=black&style=flat-square\n[swanlab-cloud-shield-link]: https://swanlab.cn/\n\n[wechat-shield]: https://img.shields.io/badge/WeChat-\u5fae\u4fe1-4cb55e?labelColor=black&style=flat-square\n[wechat-shield-link]: https://docs.swanlab.cn/guide_cloud/community/online-support.html\n\n[colab-shield]: https://colab.research.google.com/assets/colab-badge.svg\n[colab-shield-link]: https://colab.research.google.com/drive/1RWsrY_1bS8ECzaHvYtLb_1eBkkdzekR3?usp=sharing\n\n[github-stars-shield]: https://img.shields.io/github/stars/swanhubx/swanlab?labelColor&style=flat-square&color=ffcb47\n[github-stars-link]: https://github.com/swanhubx/swanlab\n\n[github-issues-shield]: https://img.shields.io/github/issues/swanhubx/swanlab?labelColor=black&style=flat-square&color=ff80eb\n[github-issues-shield-link]: https://github.com/swanhubx/swanlab/issues\n\n[github-contributors-shield]: https://img.shields.io/github/contributors/swanhubx/swanlab?color=c4f042&labelColor=black&style=flat-square\n[github-contributors-link]: https://github.com/swanhubx/swanlab/graphs/contributors\n\n[demo-cats-dogs]: https://swanlab.cn/@ZeyiLin/Cats_Dogs_Classification/runs/jzo93k112f15pmx14vtxf/chart\n[demo-cats-dogs-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-catsdogs.png\n\n[demo-yolo]: https://swanlab.cn/@ZeyiLin/ultratest/runs/yux7vclmsmmsar9ear7u5/chart\n[demo-yolo-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-yolo.png\n\n[demo-qwen2-sft]: https://swanlab.cn/@ZeyiLin/Qwen2-fintune/runs/cfg5f8dzkp6vouxzaxlx6/chart\n[demo-qwen2-sft-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-qwen2.png\n\n[demo-google-stock]:https://swanlab.cn/@ZeyiLin/Google-Stock-Prediction/charts\n[demo-google-stock-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-lstm.png\n\n[demo-audio-classification]:https://swanlab.cn/@ZeyiLin/PyTorch_Audio_Classification/charts\n[demo-audio-classification-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-audio-classification.png\n\n[demo-qwen2-vl]:https://swanlab.cn/@ZeyiLin/Qwen2-VL-finetune/runs/pkgest5xhdn3ukpdy6kv5/chart\n[demo-qwen2-vl-image]: https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/example-qwen2-vl.jpg",
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