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English | [简体中文](./README_cn.md) # 飞桨框架 ROCm 版安装说明
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle) 飞桨框架 ROCm 版支持基于海光 CPU 和海光 DCU 的训练和预测,不仅支持 AMD ROCm,同样支持海光 DCUToolkit(DTK),当前支持的 ROCm 版本为 4.0.1,支持的 DTK 有多个版本。提供两种安装方式:
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
Welcome to the PaddlePaddle GitHub. - 通过预编译的 wheel 包安装
- 通过源代码编译安装
PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms. **说明**:基于对应 DTK 版本的飞桨 wheel 包可在[光合开发者社区 ](https://developer.hpccube.com/tool/#sdk) AI 生态包中进行下载
PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 5.35 million developers, 200,000 companies and generating 670,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
## 安装方式一:通过 wheel 包安装
## Installation **注意**:当前提供基于 CentOS 7.8 & ROCm 4.0.1 的 docker 镜像,与 Python 3.7 的 wheel 安装包。同时提供基于 CentOS 7.6 & DTK 22.10.1 的 docker 镜像,镜像中包含 Python 3.7 的飞浆 2.3.2 wheel 安装包( image.sourcefind.cn:5000/dcu/admin/base/paddlepaddle:2.3.2-centos7.6-dtk-22.10.1-py37-latest )
### Latest PaddlePaddle Release: [v2.4](https://github.com/PaddlePaddle/Paddle/tree/release/2.4) **第一步**:准备 CentOS 7.6 & DTK 22.10.1 运行环境 (推荐使用 Paddle 镜像)
Our vision is to enable deep learning for everyone via PaddlePaddle. 可以直接从 Paddle 的官方镜像库拉取预先装有 CentOS 7.6 & DTK 22.10.1 的 docker 镜像
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest features of PaddlePaddle.
### Install Latest Stable Release: ```bash
# 拉取镜像
docker pull image.sourcefind.cn:5000/dcu/admin/base/paddlepaddle:2.3.2-centos7.6-dtk-22.10.1-py37-latest
# 启动容器,注意这里的参数,例如 shm-size, device 等都需要配置
docker run -it --network=host --name=oneflow_compile --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 -v /public/home/xxx:/home image.sourcefind.cn:5000/dcu/admin/base/paddlepaddle:2.3.2-centos7.6-dtk-22.10.1-py37-latest /bin/bash
# 检查容器是否可以正确识别海光 DCU 设备
rocm-smi
# 预期得到以下结果:
======================= ROCm System Management Interface =======================
================================= Concise Info =================================
GPU Temp AvgPwr SCLK MCLK Fan Perf PwrCap VRAM% GPU%
0 50.0c 23.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
1 48.0c 25.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
2 48.0c 24.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
3 49.0c 27.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
================================================================================
============================= End of ROCm SMI Log ==============================
``` ```
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
**第二步**:此镜像中已经集成 Python 3.7 的飞浆 2.3.2 版本,如果重新安装需要
```bash
pip3 uninstall paddlepaddle-rocm
pip3 install paddlepaddle-2.3.2_dtk2210_git0195561-cp37-cp37m-manylinux2014_x86_64.whl
``` ```
For more information about installation, please view [Quick Install](https://www.paddlepaddle.org.cn/install/quick)
Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 8 hours to train models online per day. [Click here to start](https://aistudio.baidu.com/aistudio/index). **第三步**:验证安装包
## FOUR LEADING TECHNOLOGIES 安装完成之后,运行如下命令。如果出现 PaddlePaddle is installed successfully!,说明已经安装成功
- **Agile Framework for Industrial Development of Deep Neural Networks** ```bash
python -c "import paddle; paddle.utils.run_check()"
```
The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden, through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved. The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts. ## 安装方式二:通过源码编译安装
**注意**:可使用 Paddle 支持的 CentOS 7.8 & ROCm 4.0.1 编译镜像,且根据 ROCm 4.0.1 的需求,支持的编译器为 devtoolset-7
- **Support Ultra-Large-Scale Training of Deep Neural Networks** **第一步**:准备 ROCm 4.0.1 编译环境 (推荐使用 Paddle 镜像)
PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open-source training platform that supports the training of deep networks with 100 billion features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved real-time model updating with more than 1 trillion parameters. 可以直接从 Paddle 的官方镜像库拉取预先装有 ROCm 4.0.1 的 docker 镜像,在[开发者社区](https://developer.hpccube.com/tool/#sdk) DCU Toolkit 中下载 DTK-22.10.1 解压至 /opt/ 路径下,更换/opt下的原有的 ROCm 4.0.1 文件夹。
[Click here to learn more](https://github.com/PaddlePaddle/Fleet)
```bash
# 拉取镜像
docker pull paddlepaddle/paddle:latest-dev-rocm4.0-miopen2.11
- **High-Performance Inference Engines for Comprehensive Deployment Environments** # 启动容器,注意这里的参数,例如 shm-size, device 等都需要配置
docker run -it --name paddle-rocm-dev --shm-size=128G \
--device=/dev/kfd --device=/dev/dri --group-add video \
--cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
paddlepaddle/paddle:latest-dev-rocm4.0-miopen2.11 /bin/bash
PaddlePaddle is not only compatible with models trained in 3rd party open-source frameworks , but also offers complete inference products for various production scenarios. Our inference product line includes [Paddle Inference](https://paddle-inference.readthedocs.io/en/master/guides/introduction/index_intro.html): Native inference library for high-performance server and cloud inference; [Paddle Serving](https://github.com/PaddlePaddle/Serving): A service-oriented framework suitable for distributed and pipeline productions; [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite): Ultra-Lightweight inference engine for mobile and IoT environments; [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs): A frontend inference engine for browser and mini-apps. Furthermore, by great amounts of optimization with leading hardware in each scenario, Paddle inference engines outperform most of the other mainstream frameworks. # 替换DTK
# 检查容器是否可以正确识别海光 DCU 设备
rocm-smi
- **Industry-Oriented Models and Libraries with Open Source Repositories** # 预期得到以下结果:
======================= ROCm System Management Interface =======================
================================= Concise Info =================================
GPU Temp AvgPwr SCLK MCLK Fan Perf PwrCap VRAM% GPU%
0 50.0c 23.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
1 48.0c 25.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
2 48.0c 24.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
3 49.0c 27.0W 1319Mhz 800Mhz 0.0% auto 300.0W 0% 0%
================================================================================
============================= End of ROCm SMI Log ==============================
```
PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications. 请在编译之前,检查如下的环境变量是否正确,如果没有则需要安装相应的依赖库,并导出相应的环境变量。以 Paddle 官方的镜像举例,环境变量如下:
[Click here to learn more](https://github.com/PaddlePaddle/models)
```bash
# PATH 与 LD_LIBRARY_PATH 中存在 devtoolset-7,如果没有运行以下命令
source /opt/rh/devtoolset-7/enable
## Documentation # PATH 中存在 cmake 3.16.0
export PATH=/opt/cmake-3.16/bin:${PATH}
We provide [English](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) and # PATH 与 LD_LIBRARY_PATH 中存在 rocm 4.0.1
[Chinese](https://www.paddlepaddle.org.cn/documentation/docs/zh/guide/index_cn.html) documentation. export PATH=/opt/rocm/opencl/bin:/opt/rocm/bin:${PATH}
export LD_LIBRARY_PATH=/opt/rocm/lib:${LD_LIBRARY_PATH}
- [Guides](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) # PATH 中存在 Python 3.7
# 注意:镜像中的 python 3.7 通过 miniconda 安装,请通过 conda activate base 命令加载 Python 3.7 环境
export PATH=/opt/conda/bin:${PATH}
```
You might want to start from how to implement deep learning basics with PaddlePaddle. **第二步**:下载 Paddle 源码并编译,CMAKE 编译选项含义请参见[编译选项表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#Compile),如果指定 Paddle 版本,需要在编译前指定环境变量 PADDLE_VERSION
- [Practice](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html) ```bash
# 下载源码,默认 develop 分支
git clone -b 2.3.2-dtk-22.10.1 http://developer.hpccube.com/codes/aicomponent/paddle.git
cd Paddle
So far you have already been familiar with Fluid. And the next step should be building a more efficient model or inventing your original Operator. # 创建编译目录
mkdir build && cd build
- [API Reference](https://www.paddlepaddle.org.cn/documentation/docs/en/api/index_en.html) # 指定 Paddle 版本
export PADDLE_VERSION=2.3.2
Our new API enables much shorter programs. # 执行 cmake
export ROCM_PATH=/opt/rocm
- [How to Contribute](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/08_contribution/index_en.html) cmake .. -DPY_VERSION=3.7 -DWITH_GPU=OFF -DWITH_ROCM=ON -DWITH_RCCL=ON -DWITH_NCCL=OFF -DWITH_TESTING=ON -DWITH_DISTRIBUTE=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DCMAKE_VERBOSE_MAKEFILE=OFF -DWITH_TP_CACHE=ON -DROCM_PATH=${ROCM_PATH} -DWITH_MKLDNN=OFF
We appreciate your contributions! # 使用以下命令来编译
make -j$(nproc)
```
**第三步**:安装与验证编译生成的 wheel 包
## Communication 编译完成之后进入`Paddle/build/python/dist`目录即可找到编译生成的.whl 安装包,安装与验证命令如下:
- [Github Issues](https://github.com/PaddlePaddle/Paddle/issues): bug reports, feature requests, install issues, usage issues, etc. ```bash
- QQ discussion group: 441226485 (PaddlePaddle). # 安装命令
- [Forums](https://aistudio.baidu.com/paddle/forum): discuss implementations, research, etc. python -m pip install -U paddlepaddle_rocm-2.3.2-cp37-cp37m-linux_x86_64.whl
# 验证命令
python -c "import paddle; paddle.utils.run_check()"
```
## Courses ## 如何卸载
- [Server Deployments](https://aistudio.baidu.com/aistudio/course/introduce/19084): Courses introducing high performance server deployments via local and remote services. 请使用以下命令卸载 Paddle:
- [Edge Deployments](https://aistudio.baidu.com/aistudio/course/introduce/22690): Courses introducing edge deployments from mobile, IoT to web and applets.
```
pip3 uninstall paddlepaddle-rocm
```
## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
<p align="center">
<img align="center" src="doc/imgs/logo.png", width=1600>
<p>
--------------------------------------------------------------------------------
English | [简体中文](./README_cn.md)
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
Welcome to the PaddlePaddle GitHub.
PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms.
PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 4.7 million developers, 180,000 companies and generating 560,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
## Installation
### Latest PaddlePaddle Release: [v2.3](https://github.com/PaddlePaddle/Paddle/tree/release/2.3)
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest features of PaddlePaddle.
### Install Latest Stable Release:
```
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
```
For more information about installation, please view [Quick Install](https://www.paddlepaddle.org.cn/install/quick)
Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 8 hours to train models online per day. [Click here to start](https://aistudio.baidu.com/aistudio/index).
## FOUR LEADING TECHNOLOGIES
- **Agile Framework for Industrial Development of Deep Neural Networks**
The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden, through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved. The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.
- **Support Ultra-Large-Scale Training of Deep Neural Networks**
PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open-source training platform that supports the training of deep networks with 100 billion features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved real-time model updating with more than 1 trillion parameters.
[Click here to learn more](https://github.com/PaddlePaddle/Fleet)
- **High-Performance Inference Engines for Comprehensive Deployment Environments**
PaddlePaddle is not only compatible with models trained in 3rd party open-source frameworks , but also offers complete inference products for various production scenarios. Our inference product line includes [Paddle Inference](https://paddle-inference.readthedocs.io/en/master/guides/introduction/index_intro.html): Native inference library for high-performance server and cloud inference; [Paddle Serving](https://github.com/PaddlePaddle/Serving): A service-oriented framework suitable for distributed and pipeline productions; [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite): Ultra-Lightweight inference engine for mobile and IoT environments; [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs): A frontend inference engine for browser and mini-apps. Furthermore, by great amounts of optimization with leading hardware in each scenario, Paddle inference engines outperform most of the other mainstream frameworks.
- **Industry-Oriented Models and Libraries with Open Source Repositories**
PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications.
[Click here to learn more](https://github.com/PaddlePaddle/models)
## Documentation
We provide [English](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) and
[Chinese](https://www.paddlepaddle.org.cn/documentation/docs/zh/guide/index_cn.html) documentation.
- [Guides](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
You might want to start from how to implement deep learning basics with PaddlePaddle.
- [Practice](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html)
So far you have already been familiar with Fluid. And the next step should be building a more efficient model or inventing your original Operator.
- [API Reference](https://www.paddlepaddle.org.cn/documentation/docs/en/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/08_contribution/index_en.html)
We appreciate your contributions!
## Communication
- [Github Issues](https://github.com/PaddlePaddle/Paddle/issues): bug reports, feature requests, install issues, usage issues, etc.
- QQ discussion group: 441226485 (PaddlePaddle).
- [Forums](https://aistudio.baidu.com/paddle/forum): discuss implementations, research, etc.
## Courses
- [Server Deployments](https://aistudio.baidu.com/aistudio/course/introduce/19084): Courses introducing high performance server deployments via local and remote services.
- [Edge Deployments](https://aistudio.baidu.com/aistudio/course/introduce/22690): Courses introducing edge deployments from mobile, IoT to web and applets.
## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
<p align="center">
<img align="center" src="doc/imgs/logo.png", width=1600>
<p>
--------------------------------------------------------------------------------
[English](./README.md) | 简体中文
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
欢迎来到 PaddlePaddle GitHub
飞桨(PaddlePaddle)以百度多年的深度学习技术研究和业务应用为基础,是中国首个自主研发、功能完备、 开源开放的产业级深度学习平台,集深度学习核心训练和推理框架、基础模型库、端到端开发套件和丰富的工具组件于一体。目前,飞桨累计开发者535万,服务企业20万家,基于飞桨开源深度学习平台产生了67万个模型。飞桨助力开发者快速实现AI想法,快速上线AI业务。帮助越来越多的行业完成AI赋能,实现产业智能化升级。
## 安装
### PaddlePaddle最新版本: [v2.4](https://github.com/PaddlePaddle/Paddle/tree/release/2.4)
跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
### 安装最新稳定版本:
```
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
```
更多安装信息详见官网 [安装说明](https://www.paddlepaddle.org.cn/install/quick)
PaddlePaddle用户可领取**免费Tesla V100在线算力资源**,训练模型更高效。**每日登陆即送8小时**[前往使用免费算力](https://aistudio.baidu.com/aistudio/index)
## 四大领先技术
- **开发便捷的产业级深度学习框架**
飞桨深度学习框架采用基于编程逻辑的组网范式,对于普通开发者而言更容易上手,符合他们的开发习惯。同时支持声明式和命令式编程,兼具开发的灵活性和高性能。网络结构自动设计,模型效果超越人类专家。
- **支持超大规模深度学习模型的训练**
飞桨突破了超大规模深度学习模型训练技术,实现了支持千亿特征、万亿参数、数百节点的开源大规模训练平台,攻克了超大规模深度学习模型的在线学习难题,实现了万亿规模参数模型的实时更新。
[查看详情](https://github.com/PaddlePaddle/Fleet)
- **支持多端多平台的高性能推理部署工具**
飞桨不仅广泛兼容第三方开源框架训练的模型部署,并且为不同的场景的生产环境提供了完备的推理引擎,包括适用于高性能服务器及云端推理的原生推理库 [Paddle Inference](https://www.paddlepaddle.org.cn/inference/product_introduction/inference_intro.html),面向分布式、流水线生产环境下自动上云、A/B测试等高阶功能的服务化推理框架 [Paddle Serving](https://github.com/PaddlePaddle/Serving),针对于移动端、物联网场景的轻量化推理引擎 [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite),以及在浏览器、小程序等环境下使用的前端推理引擎 [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs)。同时,透过与不同场景下的主流硬件高度适配优化及异构计算的支持, 飞桨的推理性能也领先绝大部分的主流实现。
- **面向产业应用,开源开放覆盖多领域的工业级模型库。**
飞桨官方支持100多个经过产业实践长期打磨的主流模型,其中包括在国际竞赛中夺得冠军的模型;同时开源开放200多个预训练模型,助力快速的产业应用。
[查看详情](https://github.com/PaddlePaddle/models)
## 文档
我们提供 [英文](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
[中文](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) 文档
- [使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html):或许您想从深度学习基础开始学习飞桨
- [应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html):使用飞桨搭建您的模型,更高效的完成深度学习任务
- [API 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html):新的 API 支持代码更少更简洁的程序
- [贡献方式](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_contribution/index_cn.html):欢迎您的贡献!
## 交流与反馈
- 欢迎您通过[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)来提交问题、报告与建议
- QQ群: 441226485 (PaddlePaddle)
- [论坛](https://aistudio.baidu.com/paddle/forum): 欢迎大家在PaddlePaddle论坛分享在使用PaddlePaddle中遇到的问题和经验,营造良好的论坛氛围
## 课程
- [服务器部署](https://aistudio.baidu.com/aistudio/course/introduce/19084): 详细介绍高性能服务器端部署实操,包含本地端及服务化Serving部署等
- [端侧部署](https://aistudio.baidu.com/aistudio/course/introduce/22690): 详细介绍端侧多场景部署实操,从移动端设备、IoT、网页到小程序部署
## 版权和许可证
PaddlePaddle由[Apache-2.0 license](LICENSE)提供
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