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PaddlePaddle




Welcome to the PaddlePaddle GitHub.

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.

Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our release announcement to track the latest feature of PaddlePaddle.

Features

  • Flexibility

    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.

  • Efficiency

    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.
  • Scalability

    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.

  • Connected to Products

    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.

Installation

Check out the Install Guide to install from
pre-built packages (docker image, deb package) or
directly build on Linux and Mac OS X from the source code.

Documentation

Both English Docs and Chinese Docs are provided for our users and developers.

  • Quick Start <br>
    You can follow the quick start tutorial to learn how use PaddlePaddle
    step-by-step.

  • Example and Demo <br>
    We provide five demos, including: image classification, sentiment analysis,
    sequence to sequence model, recommendation, semantic role labeling.

  • Distributed Training <br>
    This system supports training deep learning models on multiple machines
    with data parallelism.

  • Python API <br>
    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.

  • How to Contribute <br>
    We sincerely appreciate your interest and contributions. If you would like to
    contribute, please read the contribution guide.

  • Source Code Documents <br>

Ask Questions

Please join the gitter chat or send email to
paddle-dev@baidu.com to ask questions and talk about methods and models.
Framework development discussions and
bug reports are collected on Issues.

Copyright and License

PaddlePaddle is provided under the Apache-2.0 license.

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