<h1><center><img src="./Resources/DeepMuon.png" width='900px'></center></h1>
## Introduction
DeepMuon is a easy-using deep learning platform initially built for dark matter searching experiments. Up to now it has been a interdisciplinary deep learning platform. We are eager to provide advanced model training framework and excellent project management assistance.
Here we list out some available features of DeepMuon:
- **Single GPU** trainingļ¼ **Distributed Data Parallel** training and **Fully Sharded Distributed Parallel** training.
- Neural Network Hyperparameter Searching (NNHS)
- Gradient accumulation
- Gradient clipping
- Mixed precision training
- Double precision training
- Customize models
- Customize datasets
- Customize loss functions
- Tidy logging system
- Model interpretation
- Simple and direct tutorials
More details please refer to the home page of [DeepMuon](https://airscker.github.io/DeepMuon/).
## Installation (From source recommended)
```bash
git clone https://github.com/Airscker/DeepMuon.git
cd DeepMuon
pip install -v -e ./ --user
```
## CopyRight
> GNU AFFERO GENERAL PUBLIC LICENSE
>
> Project: DeepMuon
>
> Interdisciplinary Deep Learning Platform
>
> Author: Airscker/Yufeng Wang
>
> Contributors: Yufeng Wang, Shendong Su
>
> University of Science of Technology of China
>
> If you want to publish thesis using DeepMuon, please add bibliography:
>
> ```tex
> @misc{deepmuon,
> author = {Yufeng Wang},
> title = {DeepMuon: Interdisciplinary deep-learning platform},
> year = {2022},
> publisher = {GitHub},
> journal = {GitHub repository},
> howpublished = {\url{https://airscker.github.io/DeepMuon}},
> }
> ```
> Copyright (C) 2023 by Airscker(Yufeng), All Rights Reserved.
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"description": "<h1><center><img src=\"./Resources/DeepMuon.png\" width='900px'></center></h1>\n\n## Introduction\n\nDeepMuon is a easy-using deep learning platform initially built for dark matter searching experiments. Up to now it has been a interdisciplinary deep learning platform. We are eager to provide advanced model training framework and excellent project management assistance.\n\nHere we list out some available features of DeepMuon:\n\n- **Single GPU** training\uff0c **Distributed Data Parallel** training and **Fully Sharded Distributed Parallel** training.\n- Neural Network Hyperparameter Searching (NNHS)\n- Gradient accumulation\n- Gradient clipping\n- Mixed precision training\n- Double precision training\n- Customize models\n- Customize datasets\n- Customize loss functions\n- Tidy logging system\n- Model interpretation\n- Simple and direct tutorials\n\nMore details please refer to the home page of [DeepMuon](https://airscker.github.io/DeepMuon/).\n\n## Installation (From source recommended)\n\n```bash\ngit clone https://github.com/Airscker/DeepMuon.git\ncd DeepMuon\npip install -v -e ./ --user\n```\n\n## CopyRight\n\n> GNU AFFERO GENERAL PUBLIC LICENSE\n>\n> Project: DeepMuon\n>\n> Interdisciplinary Deep Learning Platform\n>\n> Author: Airscker/Yufeng Wang\n>\n> Contributors: Yufeng Wang, Shendong Su\n>\n> University of Science of Technology of China\n>\n> If you want to publish thesis using DeepMuon, please add bibliography:\n>\n> ```tex\n> @misc{deepmuon,\n> author = {Yufeng Wang},\n> title = {DeepMuon: Interdisciplinary deep-learning platform},\n> year = {2022},\n> publisher = {GitHub},\n> journal = {GitHub repository},\n> howpublished = {\\url{https://airscker.github.io/DeepMuon}},\n> }\n> ```\n> Copyright (C) 2023 by Airscker(Yufeng), All Rights Reserved.\n\n\n",
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