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# PaddlePaddle

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[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
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[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html)
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[![Coverage Status](https://coveralls.io/repos/github/PaddlePaddle/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/PaddlePaddle/Paddle?branch=develop)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
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[![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/PaddlePaddle/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
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      (e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
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      - Optimized CNN networks through MKL-DNN library.
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      - Highly optimized recurrent networks which can handle **variable-length**
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      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,
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    PaddlePaddle has been deployed into products and services with a vast number
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    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
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    Baidu and it has achieved a significant impact. We hope you can also explore
    the capability of PaddlePaddle to make an impact on your product.
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## Installation
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It is recommended to check out the
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[Docker installation guide](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html)
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before looking into the
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[build from source guide](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/build_from_source_en.html).
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## Documentation
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We provide [English](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html) and
[Chinese](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html) documentation.
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- [Deep Learning 101](http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html)
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  You might want to start from this online interactive book that can run in a Jupyter Notebook.
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- [Distributed Training](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/cluster/index_en.html)
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  You can run distributed training jobs on MPI clusters.

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- [Distributed Training on Kubernetes](http://www.paddlepaddle.org/docs/develop/documentation/en/howto/cluster/multi_cluster/k8s_en.html)
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   You can also run distributed training jobs on Kubernetes clusters.
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- [Python API](http://www.paddlepaddle.org/docs/develop/api/en/overview.html)
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   Our new API enables much shorter programs.
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- [How to Contribute](http://www.paddlepaddle.org/docs/develop/documentation/fluid/en/dev/contribute_to_paddle_en.html)
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   We appreciate your contributions!
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## Ask Questions
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You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues).
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## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).