| Name | test-sw-12241 JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | |
| Summary | Python library for streamlined tracking and management of AI training processes. |
| upload_time | 2023-12-22 14:11:49 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | |
| keywords |
machine learning
reproducibility
visualization
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<img alt="SwanLab Library" src="https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo.svg" width="352" height="59">
<br/>
<br/>
</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?color=blue"></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>
</p>
</h4>
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.
For specific guidance on SwanLab's features, please refer to the [User Guide](https://geektechstudio.feishu.cn/wiki/UInBw9eaziv17IkwfrOcHCZ1nbc).
Currently, SwanLab is undergoing rapid iterations and will continue to add new features.
## Installation
This repository is tested on Python 3.8+.
SwanLab can be installed using pip as follows:
```bash
pip install swanlab
```
## Quick tour
Let's simulate a simple machine learning training process, using `swanlab.init` to initialize the experiment and record configuration information, and using `swanlab.log` to track key metrics (in this example, it's `loss` and `acc`):
```python
import swanlab
import time
import random
lr = 0.01
epochs = 20
# Initialize the experiment and record configuration information
swanlab.init(
description="This is a sample experiment for machine learning training.",
config={
"learning_rate": lr,
"epochs": epochs,
},
)
# Simulate a machine learning training process
for epoch in range(2, epochs):
acc = 1 - 2**-epoch - random.random() / epoch - offset
loss = 2**-epoch + random.random() / epoch + offset
# Track key metrics
swanlab.log({"loss": loss, "accuracy": acc})
time.sleep(0.1)
```
During the program running, a `swanlog` folder will be generated in the directory to record your training data.
If you want to visualize your experiment, open the terminal and enter the root directory (no need to enter the `swanlog` folder), and run the following command:
```bash
swanlab watch
```
If you see the following output, it means that the experimental board is running successfully:
```console
[SwanLab-INFO]: SwanLab Experiment Dashboard ready in 375ms
➜ Local: http://127.0.0.1:5092
```
Access http://127.0.0.1:5092 at this time to enter the experiment dashboard and browse your experimental results：
<img alt="swanlab-dashboard-1" src="https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-dashborad-1.png" width="800">
# License
[Apache 2.0 License](https://github.com/SwanHubX/SwanLab/blob/main/LICENSE)
Raw data
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"description": "<p align=\"center\">\n <img alt=\"SwanLab Library\" src=\"https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-logo.svg\" width=\"352\" height=\"59\">\n <br/>\n <br/>\n</p>\n<p align=\"center\">\n <a href=\"https://pypi.python.org/pypi/swanlab\"><img src=\"https://img.shields.io/pypi/v/swanlab\" /></a>\n <a href=\"https://github.com/SwanHubX/SwanLab/blob/main/LICENSE\"><img src=\"https://img.shields.io/github/license/SwanHubX/SwanLab.svg?color=blue\"></a>\n <a href=\"https://github.com/SwanHubX/SwanLab/releases\"><img alt=\"GitHub release\" src=\"https://img.shields.io/github/release/SwanHubX/SwanLab.svg\"></a>\n</p>\n<h4 align=\"center\">\n <p>\n <b>English</b> |<a href=\"https://github.com/SwanHubX/SwanLab/blob/main/README_zh-hans.md\">\u7b80\u4f53\u4e2d\u6587</a>\n </p>\n</h4>\n\n\n\nSwanLab 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.\n\nSwanLab 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.\n\nFor specific guidance on SwanLab's features, please refer to the [User Guide](https://geektechstudio.feishu.cn/wiki/UInBw9eaziv17IkwfrOcHCZ1nbc).\n\nCurrently, SwanLab is undergoing rapid iterations and will continue to add new features.\n\n\n\n## Installation\n\nThis repository is tested on Python 3.8+.\n\nSwanLab can be installed using pip as follows:\n\n```bash\npip install swanlab\n```\n\n\n\n## Quick tour\n\nLet's simulate a simple machine learning training process, using `swanlab.init` to initialize the experiment and record configuration information, and using `swanlab.log` to track key metrics (in this example, it's `loss` and `acc`):\n\n```python\nimport swanlab\nimport time\nimport random\n\nlr = 0.01\nepochs = 20\n\n# Initialize the experiment and record configuration information\nswanlab.init(\n\t description=\"This is a sample experiment for machine learning training.\",\n config={\n \"learning_rate\": lr,\n \"epochs\": epochs,\n },\n)\n\n# Simulate a machine learning training process\nfor epoch in range(2, epochs):\n acc = 1 - 2**-epoch - random.random() / epoch - offset\n loss = 2**-epoch + random.random() / epoch + offset\n # Track key metrics\n swanlab.log({\"loss\": loss, \"accuracy\": acc})\n time.sleep(0.1)\n```\n\nDuring the program running, a `swanlog` folder will be generated in the directory to record your training data.\n\nIf you want to visualize your experiment, open the terminal and enter the root directory (no need to enter the `swanlog` folder), and run the following command:\n\n```bash\nswanlab watch\n```\n\nIf you see the following output, it means that the experimental board is running successfully:\n\n```console\n[SwanLab-INFO]: SwanLab Experiment Dashboard ready in 375ms\n\n \u279c Local: http://127.0.0.1:5092\n```\n\nAccess http://127.0.0.1:5092 at this time to enter the experiment dashboard and browse your experimental results\uff1a\n\n<img alt=\"swanlab-dashboard-1\" src=\"https://raw.githubusercontent.com/SwanHubX/swanlab/main/readme_files/swanlab-dashborad-1.png\" width=\"800\">\n\n\n\n# License\n\n[Apache 2.0 License](https://github.com/SwanHubX/SwanLab/blob/main/LICENSE)\n\n\n\n",
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