提交 bebaab3a 编写于 作者: H HuaHua404 提交者: wenjuan

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) [![](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. MegEngine is a fast, scalable, and user friendly 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. * **Unified framework for both training and inference**
* 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). * Quantization, dynamic shape/image pre-processing, and even derivation with a single model.
* **Lowest hardware requirements helped by algorithms** * 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.
* 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). * **The lowest hardware requirements**
* Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner * 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 efficiently on all-platform** * Inference models with the lowest memory usage by leveraging our Pushdown memory planner.
* Inference fast and high-precision on x86/Arm/CUDA/RoCM * **Inference efficiently on all platforms**
* Support Linux/Windows/iOS/Android/TEE... * Inference with speed and high-precision on x86, Arm, CUDA, and RoCM.
* 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) * 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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