Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
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 2 months ago
JSON representation
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 (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-04-30T08:30:54.000Z (8 months ago)
- Last Synced: 2024-05-19T14:32:34.219Z (7 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
- awesome-llm-eval - MegEngine - MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation. (Frameworks-for-Training / Popular-LLM)
- awesome-list - MegEngine - An open source deep learning framework by MEGVII, with GPU support. (Deep Learning Framework / High-Level DL APIs)
- StarryDivineSky - MegEngine/MegEngine
- awesome-llmops - MegEngine - to-use deep learning framework, with auto-differentiation. |  | (Training / Frameworks for Training)
README
# MegEngine
Documentation | 中文文档
[](README_CN.md) [](https://megengine.org.cn/) [](LICENSE) [](https://jq.qq.com/?_wv=1027&k=jJcBU1xi) [](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.