未验证 提交 31218a18 编写于 作者: K kagome1007 提交者: GitHub

Merge pull request #476 from MegEngine/HuaHua404-patch-4

docs(readme): update key features description
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[![](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 easy-to-use deep learning framework with 3 key features.
* **Unified core for both training and inference**
* You can represent quantization/dynamic shape/image pre-processing and even derivation in one model.
* After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. Check the usage [here](https://www.megengine.org.cn/doc/stable/zh/user-guide/model-development/traced_module/index.html).
* **Lowest hardware requirements helped by algorithms**
* In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the [DTR algorithm](https://www.megengine.org.cn/doc/stable/zh/user-guide/model-development/dtr/index.html).
* Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner
* **Inference efficiently on all-platform**
* Inference fast and high-precision on x86/Arm/CUDA/RoCM
* Support Linux/Windows/iOS/Android/TEE...
* Save more memory and optimize speed by leveraging [advanced usage](https://www.megengine.org.cn/doc/stable/zh/user-guide/deployment/lite/advance/index.html)
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).
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