Do you wanna try and play PaddlePaddle? Just following the [Install Guide](http://www.paddlepaddle.org/doc/build/index.html) and [Quick Start](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html). The chinese version is [Install Guide](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html) and [Quick Start](http://www.paddlepaddle.org/doc_cn/demo/quick_start/index.html).
Please refer to our [release log](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle.
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 log](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle.
## 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.
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:
1. Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels.
2. Highly optimized recurrent networks which can handle **variable-length**
sequence without padding.
3. Optimized local and distributed training for models with high dimensional
sparse data.
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.
-**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.
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 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.
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.
## Installation
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.).
Check out the [Install Guide](http://paddlepaddle.org/doc/build/) to install from
pre-built packages (**docker image**, **deb package**) or
directly build on **Linux** and **Mac OS X** from the source code.
