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# PaddlePaddle

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[![Build Status](https://travis-ci.org/baidu/Paddle.svg?branch=master)](https://travis-ci.org/baidu/Paddle)
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[![Downloads](https://img.shields.io/github/downloads/baidu/Paddle/total.svg)](https://github.com/baidu/Paddle/releases)
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[![Coverage Status](https://coveralls.io/repos/github/baidu/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/baidu/Paddle?branch=develop)
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[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)]()
[![Join the chat at](https://img.shields.io/gitter/room/PaddlePaddle/Deep_Learning.svg)](https://gitter.im/PaddlePaddle/Deep_Learning)

[![Release](https://img.shields.io/github/release/baidu/Paddle.svg)](https://github.com/baidu/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)

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Welcome to the PaddlePaddle GitHub.
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PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use,
efficient, flexible and scalable deep learning platform, which is originally
developed by Baidu scientists and engineers for the purpose of applying deep
learning to many products at Baidu.

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Our vision is to enable deep learning for everyone via PaddlePaddle.
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Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle. 
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## Features

- **Flexibility**

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    PaddlePaddle supports a wide range of neural network architectures and
    optimization algorithms. It is easy to configure complex models such as
    neural machine translation model with attention mechanism or complex memory
    connection.
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-  **Efficiency**
  
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    In order to unleash the power of heterogeneous computing resource,
    optimization occurs at different levels of PaddlePaddle, including
    computing, memory, architecture and communication. The following are some
    examples:

      - Optimized math operations through SSE/AVX intrinsics, BLAS libraries
      (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels. 
      - Highly optimized recurrent networks which can handle **variable-length** 
      sequence without padding.
      - Optimized local and distributed training for models with high dimensional
      sparse data.
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- **Scalability**

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    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
    up your training. PaddlePaddle can achieve high throughput and performance
    via optimized communication.
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- **Connected to Products**

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    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
    PaddlePaddle has been deployed into products or service with a vast number
    of users, including ad click-through rate (CTR) prediction, large-scale image
    classification, optical character recognition(OCR), search ranking, computer
    virus detection, recommendation, etc. It is widely utilized in products at
    Baidu and it has achieved a significant impact. We hope you can also exploit
    the capability of PaddlePaddle to make a huge impact for your product.
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## Installation
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Check out the [Install Guide](http://paddlepaddle.org/doc/build/) to install from
pre-built packages (**docker image**, **deb package**) or 
directly build on **Linux** and **Mac OS X** from the source code.
 
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## Documentation
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Both [English Docs](http://paddlepaddle.org/doc/) and [Chinese Docs](http://paddlepaddle.org/doc_cn/) are provided for our users and developers.
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- [Quick Start](http://paddlepaddle.org/doc/demo/quick_start/index_en) <br>
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   You can follow the quick start tutorial to learn how use PaddlePaddle
   step-by-step.
    
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- [Example and Demo](http://paddlepaddle.org/doc/demo/) <br>
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   We provide five demos, including: image classification, sentiment analysis,
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   sequence to sequence model, recommendation, semantic role labeling. 
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- [Distributed Training](http://paddlepaddle.org/doc/cluster) <br>
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  This system supports training deep learning models on multiple machines
  with data parallelism.
   
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- [Python API](http://paddlepaddle.org/doc/ui/) <br>
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   PaddlePaddle supports using either Python interface or C++ to build your
   system. We also use SWIG to wrap C++ source code to create a user friendly
   interface for Python. You can also use SWIG to create interface for your
   favorite programming language.
 
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- [How to Contribute](http://paddlepaddle.org/doc/build/contribute_to_paddle.html) <br>
   We sincerely appreciate your interest and contributions. If you would like to
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   contribute, please read the contribution guide.   

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- [Source Code Documents](http://paddlepaddle.org/doc/source/) <br>

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## Ask Questions
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Please join the [**gitter chat**](https://gitter.im/PaddlePaddle/Deep_Learning) or send email to
**paddle-dev@baidu.com** to ask questions and talk about methods and models.
Framework development discussions and
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bug reports are collected on [Issues](https://github.com/baidu/paddle/issues).
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## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).