* these are the revised readme/readme_cn pull-request, test=develop
* this is a readme pull request, test = develop
* these are the revised readme/readme_cn pull request,test = develop
@ -10,15 +11,17 @@ English | [简体中文](./README_cn.md)
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.
PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tool & component as well as service platforms.
PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service and so on while serving more than 1.5 million developers. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
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.
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.
### Install Latest Stable Release:
```
# Linux CPU
@ -28,72 +31,61 @@ pip install paddlepaddle-gpu
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.6.3.post97
# For installation on other platform, refer to http://paddlepaddle.org/
```
It is recommended to read [this doc](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/install/index_en.html) on our website.
Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. [Click here to start](http://ai.baidu.com/support/news?action=detail&id=981).
## Features
## FOUR LEADING TECHNOLOGIES
- **Flexibility**
- **Agile Framework for Industrial Development of Deep Neural Networks**
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.
The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden,through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved. The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.
- **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:
- **Support Ultra-Large-Scale Training of Deep Neural Networks**
- 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.
PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open source training platform that supports the deep networks training with 100 billions of features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved the real-time model updating with more than 1 trillion parameters.
[Click here to learn more](https://github.com/PaddlePaddle/Fleet)
- **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.
- **Accelerated High-Performance Inference over Ubiquitous Deployments**
- **Connected to Products**
PaddlePaddle is not only compatible with other open-source frameworks for models training, but also works well on the ubiquitous developments, varying from platforms to devices. More specific, PaddlePaddle accelerates the inference procedure with fastest speed-up. Note that, a recent breakthrough of inference speed has been made by PaddlePaddle on Huawei's Kirin NPU, through the hardware/software co-optimization.
[Click here to learn more](https://github.com/PaddlePaddle/Paddle-Lite)
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
- **Industry-Oriented Models and Libraries with Open Source Repositories**
PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications.
[Click here to learn more](https://github.com/PaddlePaddle/models)
It is recommended to read [this doc](http://www.paddlepaddle.org.cn/documentation/docs/en/1.6/beginners_guide/index_en.html) on our website.
## Documentation
We provide [English](http://www.paddlepaddle.org.cn/documentation/docs/en/1.6/beginners_guide/index_en.html) and
- [Advanced User Guides](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_usage/index_en.html)
So far you have already been familiar with Fluid. And the next expectation should be building a more efficient model or inventing your original Operator.
Daniel Yang
5 years ago
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GitHub