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Feat/better readme #70

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17 changes: 15 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,16 @@
</p>
<p align="center">
<a href="https://pypi.python.org/pypi/swanlab"><img src="https://img.shields.io/pypi/v/swanlab" /></a>
<a href="https://pypi.org/project/swanlab/"><img alt="pypi Download" src="https://img.shields.io/pypi/dm/SwanLab"></a>
<a href="https://geektechstudio.feishu.cn/wiki/UInBw9eaziv17IkwfrOcHCZ1nbc"><img alt="Website" src="https://img.shields.io/badge/website-online-blue"></a>
<a href="https://github.com/SwanHubX/SwanLab/blob/main/LICENSE"><img src="https://img.shields.io/github/license/SwanHubX/SwanLab.svg"></a>
<a href="https://github.com/SwanHubX/SwanLab/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/SwanHubX/SwanLab.svg"></a>
</p>





<h4 align="center">
<p>
<b>English</b> |<a href="https://github.com/SwanHubX/SwanLab/blob/main/README_zh-hans.md">简体中文</a>
Expand All @@ -16,6 +23,7 @@




SwanLab is the next-generation machine learning experiment management and visualization tool released by the [SwanHub](https://swanhub.co), designed to facilitate effective collaboration in machine learning training.

SwanLab provides streamlined APIs that make it easy to track machine learning metrics and record configurations. Additionally, SwanLab provides a visual dashboard for the most intuitive way to **monitor**, **analyze**, and **compare** your training.
Expand Down Expand Up @@ -53,7 +61,7 @@ offset = random.random() / 5

# Initialize the experiment and record configuration information
swanlab.init(
description="This is a sample experiment for machine learning training.",
description="This is a sample experiment for machine learning training.",
config={
"learning_rate": lr,
"epochs": epochs,
Expand All @@ -64,9 +72,10 @@ swanlab.init(
for epoch in range(2, epochs):
acc = 1 - 2**-epoch - random.random() / epoch - offset
loss = 2**-epoch + random.random() / epoch + offset
print(f"epoch={epoch}, accuracy={acc}, loss={loss}")
# Track key metrics
swanlab.log({"loss": loss, "accuracy": acc})
time.sleep(0.1)
time.sleep(1)
```

During the program running, a `swanlog` folder will be generated in the directory to record your training data.
Expand Down Expand Up @@ -97,3 +106,7 @@ Access`http://127.0.0.1:5092` at this time to enter the experiment dashboard and



# Contributors

[![swanlab contributors](https://contrib.rocks/image?repo=SwanHubX/SwanLab&max=2000)](https://github.com/SwanHubX/SwanLab/graphs/contributors)

42 changes: 27 additions & 15 deletions README_zh-hans.md
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</p>
<p align="center">
<a href="https://pypi.python.org/pypi/swanlab"><img src="https://img.shields.io/pypi/v/swanlab" /></a>
<a href="https://github.com/SwanHubX/SwanLab/blob/main/LICENSE">
<img src="https://img.shields.io/github/license/SwanHubX/SwanLab.svg">
</a>
<a href="https://github.com/SwanHubX/SwanLab/releases">
<img alt="GitHub release" src="https://img.shields.io/github/release/SwanHubX/SwanLab.svg">
</a>
<a href="https://pypi.org/project/swanlab/"><img alt="pypi Download" src="https://img.shields.io/pypi/dm/SwanLab"></a>
<a href="https://geektechstudio.feishu.cn/wiki/UInBw9eaziv17IkwfrOcHCZ1nbc"><img alt="Website" src="https://img.shields.io/badge/website-online-blue"></a>
<a href="https://github.com/SwanHubX/SwanLab/blob/main/LICENSE"><img src="https://img.shields.io/github/license/SwanHubX/SwanLab.svg"></a>
<a href="https://github.com/SwanHubX/SwanLab/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/SwanHubX/SwanLab.svg"></a>
</p>




<h4 align="center">
<p>
<a href="https://github.com/SwanHubX/SwanLab/blob/main/README.md">English</a> |<b>简体中文</b>
</p>
</h4>

SwanLab 是[SwanHub](https://swanhub.co)开源社区发布的新一代机器学习实验管理与可视化工具，旨在让机器学习训练有效地协作起
来。
SwanLab 是[SwanHub](https://swanhub.co)开源社区发布的新一代机器学习实验管理与可视化工具，旨在让机器学习训练有效地协作起来。

SwanLab 提供简洁的 API，轻松实现机器学习指标跟踪与配置记录。同时，SwanLab 还提供了一个可视化看板，以最直观的方式**监看、
分析和对比**你的训练。
Expand All @@ -29,7 +28,9 @@ SwanLab 提供简洁的 API，轻松实现机器学习指标跟踪与配置记

目前，SwanLab 正在快速迭代，并将持续添加新功能。

## Installation


## 安装

此存储库在 Python 3.8+上进行了测试。

Expand All @@ -39,10 +40,11 @@ SwanLab 可以使用 pip 安装，如下所示:
pip install swanlab
```

## Quick tour

让我们模拟一个简单的机器学习训练过程，使用`swanlab.init`来初始化实验并记录配置信息，并使用`swanlab.log`跟踪关键指标（在
这个例子中是 `loss` 和 `acc`）：

## 快速开始

让我们模拟一个简单的机器学习训练过程，使用`swanlab.init`来初始化实验并记录配置信息，并使用`swanlab.log`跟踪关键指标（在这个例子中是 `loss` 和 `acc`）：

```python
import swanlab
Expand All @@ -55,7 +57,7 @@ offset = random.random() / 5

# Initialize the experiment and record configuration information
swanlab.init(
description="This is a sample experiment for machine learning training.",
description="This is a sample experiment for machine learning training.",
config={
"learning_rate": lr,
"epochs": epochs,
Expand All @@ -66,9 +68,10 @@ swanlab.init(
for epoch in range(2, epochs):
acc = 1 - 2**-epoch - random.random() / epoch - offset
loss = 2**-epoch + random.random() / epoch + offset
print(f"epoch={epoch}, accuracy={acc}, loss={loss}")
# Track key metrics
swanlab.log({"loss": loss, "accuracy": acc})
time.sleep(0.1)
time.sleep(1)
```

在程序运行过程中，目录下会生成一个`swanlog`文件夹，记录了你的训练数据。
Expand All @@ -91,6 +94,15 @@ swanlab watch

<img alt="swanlab-dashboard-1" src="readme_files/swanlab-dashborad-1.png" width="800">



# License

[Apache 2.0 License](https://github.com/SwanHubX/SwanLab/blob/main/LICENSE)



# 贡献者

[![swanlab contributors](https://contrib.rocks/image?repo=SwanHubX/SwanLab&max=2000)](https://github.com/SwanHubX/SwanLab/graphs/contributors)