# PaddlePaddle
[](https://travis-ci.org/PaddlePaddle/Paddle)
[](http://paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html)
[](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html)
[](https://github.com/PaddlePaddle/Paddle/releases)
[](LICENSE)
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](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.0.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
### Install Latest Stable Release:
```
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.0.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.0.1.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
## 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:
- Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
- Optimized CNN networks through MKL-DNN library.
- Highly optimized recurrent networks which can handle **variable-length**
sequence without padding.
- 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 and services 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 explore
the capability of PaddlePaddle to make an impact on your product.
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) on our website.
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.0.0/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.0/user_guides/howto/training/cluster_howto.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/api/zh/1.0/fluid.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.0/advanced_usage/development/contribute_to_paddle.html)
We appreciate your contributions!
## Ask Questions
You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues).
## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).