Paddle

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Welcome to the PaddlePaddle GitHub.

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.
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 23.33 million developers, 760,000 companies and generating 1,100,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.

## Installation

### Latest PaddlePaddle Release: 3.3
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 features of PaddlePaddle.

### Install Latest Stable Release or Nightly Release

For detailed information about installation, please view [Quick Install](https://www.paddlepaddle.org.cn/install/quick)

## **PaddlePaddle New Generation Framework 3.2**

* **Unified Dynamic/Static Graphs and Automatic Parallelism**

    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.

* **Integrated Training and Inference for Large Models**

    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.

* **High-Order Differentiation for Scientific Computing**

    Pr