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109 lines
4.5 KiB
109 lines
4.5 KiB
# PaddlePaddle
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[](https://travis-ci.org/PaddlePaddle/Paddle)
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[](http://paddlepaddle.org/documentation/docs/en/1.1/getstarted/index_en.html)
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[](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html)
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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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### Latest PaddlePaddle Release: [Fluid 1.1.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.1)
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### Install Latest Stable Release:
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```
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# Linux CPU
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pip install paddlepaddle
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# Linux GPU cuda9cudnn7
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pip install paddlepaddle-gpu
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# Linux GPU cuda8cudnn7
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pip install paddlepaddle-gpu==1.1.0.post87
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# Linux GPU cuda8cudnn5
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pip install paddlepaddle-gpu==1.1.0.post85
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# For installation on other platform, refer to http://paddlepaddle.org/
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```
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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, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
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- Optimized CNN networks through MKL-DNN library.
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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 and services 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 explore
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the capability of PaddlePaddle to make an impact on your product.
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## Installation
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It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html) on our website.
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## Documentation
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We provide [English](http://paddlepaddle.org/documentation/docs/en/1.1/getstarted/index_en.html) and
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[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html) documentation.
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- [Deep Learning 101](https://github.com/PaddlePaddle/book)
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You might want to start from this online interactive book that can run in a Jupyter Notebook.
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- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.1/user_guides/howto/training/cluster_howto.html)
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You can run distributed training jobs on MPI clusters.
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- [Python API](http://paddlepaddle.org/documentation/api/zh/1.1/fluid.html)
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Our new API enables much shorter programs.
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- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.1/advanced_usage/development/contribute_to_paddle.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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