PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice.
（『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署） http://www.paddlepaddle.org/
（『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署） http://www.paddlepaddle.org/
English | 简体中文
Welcome to the PaddlePaddle.
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.
Installation
Latest PaddlePaddle Release: v1.7
Our vision is to enable deep learning for everyone via PaddlePaddle. Please refer to our release announcement to track the latest feature of PaddlePaddle.
Install Latest Stable Release:
# Linux CPU
pip install paddlepaddle
# Linux GPU cuda10cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu==1.7.2.post97
It is recommended to read this doc 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.
FOUR LEADING TECHNOLOGIES
- Agile Framework for Industrial Development of Deep Neural NetworksThe 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.
- Support Ultra-Large-Scale Training of Deep Neural NetworksPaddlePaddle 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
- Accelerated High-Performance Inference over Ubiquitous DeploymentsPaddlePaddle 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
- Industry-Oriented Models and Libraries with Open Source RepositoriesPaddlePaddle 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
Documentation
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You might want to start from how to implement deep learning basics with PaddlePaddle.
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You might have got the hang of Beginner’s Guide, and wish to model practical problems and build your original networks.
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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.
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Our new API enables much shorter programs.
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We appreciate your contributions!
Communication
- Github Issues: bug reports, feature requests, install issues, usage issues, etc.
- QQ discussion group: 796771754 (PaddlePaddle).
- Forums: discuss implementations, research, etc.
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百度的深度学习平台PaddlePaddle
PaddlePaddle是百度开源的深度学习平台，具有易用，高效，灵活和可伸缩等特点，已为百度内部多项产品提供深度学习算法支持，在深度学习框架方面，覆盖了搜索、图像识别、语音语义识别理解、情感分析、机器翻译、用户画像推荐等多领域的业务和技术, 如外卖的预估出餐时间、预判网盘故障时间点、精准推荐用户所需信息、海量图像识别分类、字符识别（OCR）、病毒和垃圾信息检测、机器翻译和自动驾驶等领域。
特性：
*易用性是PaddlePaddle的设计核心之一，它为用户提供了直观且灵活的数据接口和模型定义接口。
*PaddlePaddle支持多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型，如带注意力机制或复杂记忆连接的神经机器翻译模型。
*为充分发挥多种计算资源的效力，PaddlePaddle在计算、存储、架构、通信等多方面都做了细致优化，性能优异。
*PaddlePaddle全面支持多核、多GPU、多机环境，优化的通信实现使高吞吐与高性能成为可能，轻松应对大规模数据训练需求。
官网：http://www.paddlepaddle.org/
PaddlePaddle是百度开源的深度学习平台，具有易用，高效，灵活和可伸缩等特点，已为百度内部多项产品提供深度学习算法支持，在深度学习框架方面，覆盖了搜索、图像识别、语音语义识别理解、情感分析、机器翻译、用户画像推荐等多领域的业务和技术, 如外卖的预估出餐时间、预判网盘故障时间点、精准推荐用户所需信息、海量图像识别分类、字符识别（OCR）、病毒和垃圾信息检测、机器翻译和自动驾驶等领域。
特性：
*易用性是PaddlePaddle的设计核心之一，它为用户提供了直观且灵活的数据接口和模型定义接口。
*PaddlePaddle支持多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型，如带注意力机制或复杂记忆连接的神经机器翻译模型。
*为充分发挥多种计算资源的效力，PaddlePaddle在计算、存储、架构、通信等多方面都做了细致优化，性能优异。
*PaddlePaddle全面支持多核、多GPU、多机环境，优化的通信实现使高吞吐与高性能成为可能，轻松应对大规模数据训练需求。
官网：http://www.paddlepaddle.org/
