PaddlePaddle

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.

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:

1. Optimized math operations through SSE/AVX intrinsics, BLAS libraries (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels.
2. Highly optimized recurrent networks which can handle variable-length sequence without padding.
3. 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

See Installation Guide to install from pre-built package or build from the source code. (Note: The installation packages are still in pre-release state and your experience of installation may not be smooth.).

Documentation

• Chinese Documentation
  

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

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

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

• Python API
  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
  We sincerely appreciate your interest and contributions. If you would like to contribute, please read the contribution guide.

• Source Code Documents
  

Ask Questions

If you want to ask questions and discuss about methods and models, welcome to send email to [email protected]. Framework development discussions and bug reports are collected on Issues.

Copyright and License

PaddlePaddle is provided under the Apache-2.0 license.