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* update README for version 3
Signed-off-by: Zhang Jun <jzhang533@gmail.com>
* Update README_cn.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* update paddlepaddle 3.0 new features
Signed-off-by: Zhang Jun <jzhang533@gmail.com>
* minor
Signed-off-by: Zhang Jun <jzhang533@gmail.com>
* minor
Signed-off-by: Zhang Jun <jzhang533@gmail.com>
* Update README.md
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* linting update; test=document_fix
Signed-off-by: Zhang Jun <jzhang533@gmail.com>
---------
Signed-off-by: Zhang Jun <jzhang533@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
PaddlePaddle, as the first 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, tools & components as well as service platforms.
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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 10.7 million developers, 235,000 companies and generating 860,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
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PaddlePaddle originates 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 18.08 million developers, 430,000 companies and generating 1,010,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
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 features of PaddlePaddle.
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For more information about installation, please view [Quick Install](https://www.paddlepaddle.org.cn/install/quick)
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Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 8 hours to train models online per day. [Click here to start](https://aistudio.baidu.com/aistudio/index).
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## **PaddlePaddle New Generation Framework 3.0**
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## FOUR LEADING TECHNOLOGIES
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***Unified Dynamic/Static Graphs and Automatic Parallelism**
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-**Agile Framework for Industrial Development of Deep Neural Networks**
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By requiring only minimal tensor partitioning annotations based on a single-card configuration, PaddlePaddle automatically discovers the most efficient distributed parallel strategy. This significantly reduces the costs of industrial development and training, enabling developers to focus more intently on model and algorithm innovation.
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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.
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***Integrated Training and Inference for Large Models**
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-**Support Ultra-Large-Scale Training of Deep Neural Networks**
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The same framework supports both training and inference, achieving code reuse and seamless integration between these stages. This provides a unified development experience and maximum training efficiency for the entire large model workflow, offering the industry a superior development experience.
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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 training of deep networks with 100 billion 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 real-time model updating with more than 1 trillion parameters.
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[Click here to learn more](https://github.com/PaddlePaddle/Fleet)
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***High-Order Differentiation for Scientific Computing**
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-**High-Performance Inference Engines for Comprehensive Deployment Environments**
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Provides capabilities such as high-order automatic differentiation, complex number operations, Fourier transforms, compilation optimization, and distributed training support. It facilitates scientific exploration in fields including mathematics, mechanics, materials science, meteorology, and biology, substantially improving the speed of solving differential equations.
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PaddlePaddle is not only compatible with models trained in 3rd party open-source frameworks , but also offers complete inference products for various production scenarios. Our inference product line includes [Paddle Inference](https://www.paddlepaddle.org.cn/inference/master/guides/introduction/index_intro.html): Native inference library for high-performance server and cloud inference; [FastDeploy](https://github.com/PaddlePaddle/FastDeploy): Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge without-of-the-box and unified experience; [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite): Ultra-Lightweight inference engine for mobile and IoT environments; [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs): A frontend inference engine for browser and mini-apps. Furthermore, by great amounts of optimization with leading hardware in each scenario, Paddle inference engines outperform most of the other mainstream frameworks.
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***Neural Network Compiler**
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-**Industry-Oriented Models and Libraries with Open Source Repositories**
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Adopting an integrated framework design, it supports efficient training and flexible inference for diverse models, including generative and scientific computing models. It achieves an effective balance between computational flexibility and high performance, significantly lowering performance optimization costs.
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PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the 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.
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[Click here to learn more](https://github.com/PaddlePaddle/models)
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***Heterogeneous Multi-Chip Adaptation**
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Features a mature and complete unified adaptation solution for multiple hardware types. Through standardized interfaces, it abstracts the variations in development interfaces across different chip software stacks, realizing a pluggable architecture.
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## Documentation
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We provide [English](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) and
- Community Governance Organization: [PaddlePaddle OpenSource Development Working Group, PPOSDWG](https://github.com/PaddlePaddle/community/tree/master/pposdwg)
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- Community Blog: <https://pfcc.blog/>
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## Courses
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-[Server Deployments](https://aistudio.baidu.com/aistudio/course/introduce/19084): Courses introducing high performance server deployments via local and remote services.
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-[Edge Deployments](https://aistudio.baidu.com/aistudio/course/introduce/22690): Courses introducing edge deployments from mobile, IoT to web and applets.
* Many of our contribution events offer varying levels of mentorship from experienced community members, please check the events in the pinned issues, and consider attending.
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* Community Blog: <https://pfcc.blog/>
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* See more details about PaddlePaddle community at [community](https://github.com/PaddlePaddle/community).
飞桨(PaddlePaddle)以百度多年的深度学习技术研究和业务应用为基础，是中国首个自主研发、功能完备、 开源开放的产业级深度学习平台，集深度学习核心训练和推理框架、基础模型库、端到端开发套件和丰富的工具组件于一体。目前，飞桨累计开发者 1808 万，服务企业 43 万家，基于飞桨开源深度学习平台产生了 101 万个模型。飞桨助力开发者快速实现 AI 想法，快速上线 AI 业务。帮助越来越多的行业完成 AI 赋能，实现产业智能化升级。
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