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https://github.com/megengine/megengine

MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
https://github.com/megengine/megengine

autograd deep-learning gpu machine-learning megengine numpy python tensor

Last synced: about 4 hours ago
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MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架

Host: GitHub
URL: https://github.com/megengine/megengine
Owner: MegEngine
License: apache-2.0
Created: 2020-03-18T08:21:58.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-04-30T08:30:54.000Z (5 months ago)
Last Synced: 2024-05-19T14:32:34.219Z (4 months ago)
Topics: autograd, deep-learning, gpu, machine-learning, megengine, numpy, python, tensor
Language: C++
Homepage: https://megengine.org.cn/
Size: 30.4 MB
Stars: 4,721
Watchers: 136
Forks: 532
Open Issues: 169
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        # MegEngine



  



  Documentation  |  中文文档  


[![](https://img.shields.io/badge/English-%E4%B8%AD%E6%96%87-green.svg)](README_CN.md) [![](https://img.shields.io/badge/Website-MegEngine-green.svg)](https://megengine.org.cn/) [![](https://img.shields.io/badge/License-Apache%202.0-green.svg)](LICENSE) [![](https://img.shields.io/badge/Chat-on%20QQ-green.svg?logo=tencentqq)](https://jq.qq.com/?_wv=1027&k=jJcBU1xi) [![](https://img.shields.io/badge/Discuss-on%20Zhihu-8A2BE2.svg?labelColor=00BFFF&logo=zhihu)](https://www.zhihu.com/people/megengine-bot)

MegEngine is a fast, scalable, and user friendly deep learning framework with 3 key features.

* **Unified framework for both training and inference**

    * Quantization, dynamic shape/image pre-processing, and even derivation with a single model.

    * After training, put everything into your model to inference on any platform with speed and precision. Check [here](https://www.megengine.org.cn/doc/stable/zh/user-guide/model-development/traced_module/index.html) for a quick guide.

* **The lowest hardware requirements**

    * The memory usage of the GPU can be reduced to one-third of the original memory usage when [DTR algorithm](https://www.megengine.org.cn/doc/stable/zh/user-guide/model-development/dtr/index.html) is enabled.

    * Inference models with the lowest memory usage by leveraging our Pushdown memory planner.

* **Inference efficiently on all platforms**

    * Inference with speed and high-precision on x86, Arm, CUDA, and RoCM.

    * Supports Linux, Windows, iOS, Android, TEE, etc.

    * Optimize performance and memory usage by leveraging our [advanced features](https://www.megengine.org.cn/doc/stable/zh/user-guide/deployment/lite/advance/index.html).

------

## Installation

**NOTE:** MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.6 to 3.9. On Windows 10 you can either install the Linux distribution through [Windows Subsystem for Linux (WSL)](https://docs.microsoft.com/en-us/windows/wsl) or install the Windows distribution directly. Many other platforms are supported for inference.

### Binaries

To install the pre-built binaries via pip wheels:

```bash

python3 -m pip install --upgrade pip

python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html

```

## Building from Source

* CMake build details. please refer to [BUILD_README.md](scripts/cmake-build/BUILD_README.md)

* Python binding build details, Please refer to [BUILD_PYTHON_WHL_README.md](scripts/whl/BUILD_PYTHON_WHL_README.md)

## How to Contribute

* MegEngine adopts [Contributor Covenant](https://contributor-covenant.org) as a guideline to run our community. Please read the [Code of Conduct](CODE_OF_CONDUCT.md).

* Every contributor of MegEngine must sign a [Contributor License Agreement (CLA)](CONTRIBUTOR_LICENSE_AGREEMENT.md) to clarify the intellectual property license granted with the contributions.

* You can help to improve MegEngine in many ways:

    * Write code.

    * Improve [documentation](https://github.com/MegEngine/Docs).

    * Answer questions on [MegEngine Forum](https://discuss.megengine.org.cn), or Stack Overflow.

    * Contribute new models in [MegEngine Model Hub](https://github.com/megengine/hub).

    * Try a new idea on [MegStudio](https://studio.brainpp.com).

    * Report or investigate [bugs and issues](https://github.com/MegEngine/MegEngine/issues).

    * Review [Pull Requests](https://github.com/MegEngine/MegEngine/pulls).

    * Star MegEngine repo.

    * Cite MegEngine in your papers and articles.

    * Recommend MegEngine to your friends.

    * Any other form of contribution is welcomed.

We strive to build an open and friendly community. We aim to power humanity with AI.

## How to Contact Us

* Issue: [github.com/MegEngine/MegEngine/issues](https://github.com/MegEngine/MegEngine/issues)

* Email: [[email protected]](mailto:[email protected])

* Forum: [discuss.megengine.org.cn](https://discuss.megengine.org.cn)

* QQ Group: 1029741705

## Resources

- [MegEngine](https://megengine.org.cn)

- [MegStudio](https://studio.brainpp.com)

- mirror repo

   - OPENI: [openi.org.cn/MegEngine](https://www.openi.org.cn/html/2020/Framework_0325/18.html)

   - Gitee: [gitee.com/MegEngine/MegEngine](https://gitee.com/MegEngine/MegEngine)

## License

MegEngine is licensed under the Apache License, Version 2.0

## Citation

If you use MegEngine in your publication,please cite it by using the following BibTeX entry.

```

@Misc{MegEngine,

  institution = {megvii},

  title =  {MegEngine:A fast, scalable and easy-to-use deep learning framework},

  howpublished = {\url{https://github.com/MegEngine/MegEngine}},

  year = {2020}

}

```

Copyright (c) 2014-2021 Megvii Inc. All rights reserved.