From e092c6195640bf173adbff97cf044a040ef5301c Mon Sep 17 00:00:00 2001 From: Yan Chunwei Date: Sun, 18 Aug 2019 18:01:20 +0800 Subject: [PATCH] enhance readme (#1804) * enhance readme * rename paddle-mobile to Paddle-Lite in wiki * disable build status * add benchmark desc --- README.md | 13 ++++++++----- README_cn.md | 12 +++++++----- 2 files changed, 15 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 23ec98a8ab..a6c4ea45c8 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,15 @@ # Paddle Lite -[![Build Status](https://travis-ci.org/PaddlePaddle/paddle-mobile.svg?branch=develop&longCache=true&style=flat-square)](https://travis-ci.org/PaddlePaddle/paddle-mobile) -[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/PaddlePaddle/paddle-mobile/tree/develop/doc) + +[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/PaddlePaddle/Paddle-Lite/wiki) [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) -Paddle Lite is an updated version of Paddle-Mobile, an open-open source deep learning framework designed to make it easy to perform inference on mobile and IoT devices. It is compatible with PaddlePaddle and pre-trained models from other sources. -For tutorials, please see [PaddleLite Wiki](https://github.com/PaddlePaddle/paddle-mobile/wiki). +Paddle Lite is an updated version of Paddle-Mobile, an open-open source deep learning framework designed to make it easy to perform inference on mobile, embeded, and IoT devices. It is compatible with PaddlePaddle and pre-trained models from other sources. + +For tutorials, please see [PaddleLite Wiki](https://github.com/PaddlePaddle/Paddle-Lite/wiki). ## Key Features @@ -29,6 +30,8 @@ It also supports INT8 quantizations with [PaddleSlim model compression tools](ht On Huawei NPU and FPGA, the performance is also boosted. +The latest benchmark is located at [benchmark](https://github.com/PaddlePaddle/Paddle-Lite/wiki/benchmark) + ### High Compatibility Hardware compatibility: Paddle Lite supports a diversity of hardwares — ARM CPU, Mali GPU, Adreno GPU, Huawei NPU and FPGA. In the near future, we will also support AI microchips from Cambricon and Bitmain. @@ -49,7 +52,7 @@ As is shown in the figure above, analysis phase includes Machine IR module, and The earlier Paddle-Mobile was designed to be compatible with PaddlePaddle and multiple hardwares, including ARM CPU, Mali GPU, Adreno GPU, FPGA, ARM-Linux and Apple's GPU Metal. Within Baidu, inc, many product lines have been using Paddle-Mobile. For more details, please see: [mobile/README](mobile/README). -As an update of Paddle-Mobile, Paddle Lite has incorporated many older capabilities into the [new architecture](https://github.com/PaddlePaddle/paddle-mobile/tree/develop/lite). For the time being, the code of Paddle-mobile will be kept under the directory `mobile/`, before complete transfer to Paddle Lite. +As an update of Paddle-Mobile, Paddle Lite has incorporated many older capabilities into the [new architecture](https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite). For the time being, the code of Paddle-mobile will be kept under the directory `mobile/`, before complete transfer to Paddle Lite. For demands of Apple's GPU Metal and web front end inference, please see `./metal` and `./web` . These two modules will be further developed and maintained. diff --git a/README_cn.md b/README_cn.md index 95072ed87a..42b91840ce 100644 --- a/README_cn.md +++ b/README_cn.md @@ -1,13 +1,13 @@ # Paddle Lite -[![Build Status](https://travis-ci.org/PaddlePaddle/paddle-mobile.svg?branch=develop&longCache=true&style=flat-square)](https://travis-ci.org/PaddlePaddle/paddle-mobile) -[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/PaddlePaddle/paddle-mobile/tree/develop/doc) + +[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/PaddlePaddle/Paddle-Lite/wiki) [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) Paddle Lite为Paddle-Mobile的升级版，定位支持包括手机移动端在内更多场景的轻量化高效预测，支持更广泛的硬件和平台，是一个高性能、轻量级的深度学习预测引擎。在保持和PaddlePaddle无缝对接外，也兼容支持其他训练框架产出的模型。 -完整使用文档位于 [PaddleLite Wiki](https://github.com/PaddlePaddle/paddle-mobile/wiki) 。 +完整使用文档位于 [PaddleLite Wiki](https://github.com/PaddlePaddle/Paddle-Lite/wiki) 。 ## 特性 @@ -21,6 +21,8 @@ Paddle Lite为Paddle-Mobile的升级版，定位支持包括手机移动端在 支持INT8量化计算，结合 [PaddleSlim 模型压缩工具](https://github.com/PaddlePaddle/models/tree/v1.5/PaddleSlim) 中 INT8量化训练功能，可以提供高精度高性能的预测能力。 在Huawei NPU， FPGA上也具有有很好的性能表现。 +最新 Benchmark 位于 [benchmark](https://github.com/PaddlePaddle/Paddle-Lite/wiki/benchmark)。 + ### 通用性 硬件方面，Paddle Lite 的架构设计为多硬件兼容支持做了良好设计。除了支持ARM CPU、Mali GPU、Adreno GPU，还特别支持了华为 NPU，以及 FPGA 等边缘设备广泛使用的硬件。即将支持支持包括寒武纪、比特大陆等AI芯片，未来会增加对更多硬件的支持。 @@ -38,9 +40,9 @@ PaddleLite 的架构设计着重考虑了对多硬件和平台的支持，并且 ## Paddle-Mobile升级为Paddle Lite的说明 -原Paddle-Mobile作为一个致力于嵌入式平台的PaddlePaddle预测引擎，已支持多种硬件平台，包括ARM CPU、 Mali GPU、Adreno GPU，以及支持苹果设备的GPU Metal实现、ZU5、ZU9等FPGA开发板、树莓派等arm-linux开发板。在百度内已经过广泛业务场景应用验证。对应设计文档可参考: [mobile/README](https://github.com/PaddlePaddle/paddle-mobile/blob/develop/mobile/README.md) +原Paddle-Mobile作为一个致力于嵌入式平台的PaddlePaddle预测引擎，已支持多种硬件平台，包括ARM CPU、 Mali GPU、Adreno GPU，以及支持苹果设备的GPU Metal实现、ZU5、ZU9等FPGA开发板、树莓派等arm-linux开发板。在百度内已经过广泛业务场景应用验证。对应设计文档可参考: [mobile/README](https://github.com/PaddlePaddle/Paddle-Lite/blob/develop/mobile/README.md) -Paddle-Mobile 整体升级重构并更名为Paddle Lite后，原paddle-mobile 的底层能力大部分已集成到[新架构 ](https://github.com/PaddlePaddle/paddle-mobile/tree/develop/lite)下。作为过渡，暂时保留原Paddle-mobile代码。 主体代码位于 `mobile/` 目录中，后续一段时间会继续维护，并完成全部迁移。新功能会统一到[新架构 ](https://github.com/PaddlePaddle/paddle-mobile/tree/develop/lite)下开发。 +Paddle-Mobile 整体升级重构并更名为Paddle Lite后，原paddle-mobile 的底层能力大部分已集成到[新架构 ](https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite)下。作为过渡，暂时保留原Paddle-mobile代码。 主体代码位于 `mobile/` 目录中，后续一段时间会继续维护，并完成全部迁移。新功能会统一到[新架构 ](https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite)下开发。 metal, web的模块相对独立，会继续在 `./metal` 和 `./web` 目录下开发和维护。对苹果设备的GPU Metal实现的需求及web前端预测需求，可以直接进入这两个目录。 -- GitLab