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.
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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.
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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.
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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.
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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.
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.
Both English Docs and Chinese Docs are provided for our users and developers.
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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 labeling. -
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.
You are welcome to submit questions and bug reports as Github Issues.
PaddlePaddle is provided under the Apache-2.0 license.