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91 lines
4.0 KiB
91 lines
4.0 KiB
# PaddlePaddle
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[](https://travis-ci.org/baidu/Paddle)
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Welcome to the PaddlePaddle GitHub.
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The software will be released on Sept. 30 with full documentation and installation support.
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A pre-release version is available now for those who are eager to take a look.
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PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use,
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efficient, flexible and scalable deep learning platform, which is originally
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developed by Baidu scientists and engineers for the purpose of applying deep
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learning to many products at Baidu.
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## Features
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- **Flexibility**
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PaddlePaddle supports a wide range of neural network architectures and
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optimization algorithms. It is easy to configure complex models such as
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neural machine translation model with attention mechanism or complex memory
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connection.
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- **Efficiency**
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In order to unleash the power of heterogeneous computing resource,
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optimization occurs at different levels of PaddlePaddle, including
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computing, memory, architecture and communication. The following are some
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examples:
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1. Optimized math operations through SSE/AVX intrinsics, BLAS libraries
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(e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels.
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2. Highly optimized recurrent networks which can handle **variable-length**
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sequence without padding.
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3. Optimized local and distributed training for models with high dimensional
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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
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up your training. PaddlePaddle can achieve high throughput and performance
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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 or service with a vast number
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of users, including ad click-through rate (CTR) prediction, large-scale image
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classification, optical character recognition(OCR), search ranking, computer
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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 exploit
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the capability of PaddlePaddle to make a huge impact for your product.
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## Installation
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See [Installation Guide](http://paddlepaddle.org/doc/build/) 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.).
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## Documentation
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- [Chinese Documentation](http://paddlepaddle.org/doc_cn/) <br>
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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
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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
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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
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system. We also use SWIG to wrap C++ source code to create a user friendly
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interface for Python. You can also use SWIG to create interface for your
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favorite programming language.
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- [How to Contribute](http://paddlepaddle.org/doc/build/contribute_to_paddle.html) <br>
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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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If you want to ask questions and discuss about methods and models, welcome
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to send email to paddle-dev@baidu.com. 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
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PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
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