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100 lines
4.3 KiB
100 lines
4.3 KiB
# PaddlePaddle
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[](https://travis-ci.org/PaddlePaddle/Paddle)
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[](http://doc.paddlepaddle.org/develop/doc/)
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[](http://doc.paddlepaddle.org/develop/doc_cn/)
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[](https://coveralls.io/github/PaddlePaddle/Paddle?branch=develop)
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[](https://github.com/PaddlePaddle/Paddle/releases)
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[](LICENSE)
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Welcome to the PaddlePaddle GitHub.
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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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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
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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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- 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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- Highly optimized recurrent networks which can handle **variable-length**
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sequence without padding.
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- 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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It is recommended to check out the
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[Docker installation guide](http://doc.paddlepaddle.org/develop/doc/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://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html)
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## Documentation
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We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
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[Chinese](http://doc.paddlepaddle.org/doc_cn/) documentation.
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- [Deep Learning 101](http://book.paddlepaddle.org/index.html)
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You might want to start from the this online interactive book that can run in Jupyter Notebook.
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- [Distributed Training](http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
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You can run distributed training jobs on MPI clusters.
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- [Distributed Training on Kubernetes](http://doc.paddlepaddle.org/develop/doc/howto/usage/k8s/k8s_en.html)
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You can also run distributed training jobs on Kubernetes clusters.
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- [Python API](http://doc.paddlepaddle.org/develop/doc/api/index_en.html)
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Our new API enables much shorter programs.
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- [How to Contribute](http://doc.paddlepaddle.org/develop/doc/howto/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
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PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
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