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- --------------------------------------------------------------------------------- +# IMPROVED Paddle for Algmon Brain +## Intro English | [简体中文](./README_cn.md) [](https://travis-ci.org/PaddlePaddle/Paddle) @@ -12,11 +9,11 @@ English | [简体中文](./README_cn.md) [](https://github.com/PaddlePaddle/Paddle/releases) [](LICENSE) -Welcome to the PaddlePaddle GitHub. +Welcome to the Algmon brain_general_x3 -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 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 4.7 million developers, 180,000 companies and generating 560,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI. +As the first independent Research & Development deep learning framework in China, has been officially open-sourced to professional communities years ago. It is an industrial platform with some technologies and enough features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms. +The framework is originated from industrial practices with dedication and commitments to industrialization. It has been adopted by a range of vertical segments such as manufacturing, agriculture, enterprise service etc. Around 4.7 million developers, 180,000 companies have been touched, generating around 560,000 models in total. With such advantages, this framework has helped an increasing number of partners. ## Installation @@ -34,63 +31,4 @@ pip install paddlepaddle-gpu ``` For more information about installation, please view [Quick Install](https://www.paddlepaddle.org.cn/install/quick) -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). - -## FOUR LEADING TECHNOLOGIES - -- **Agile Framework for Industrial Development of Deep Neural Networks** - - 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. - - -- **Support Ultra-Large-Scale Training of Deep Neural Networks** - - 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. - [Click here to learn more](https://github.com/PaddlePaddle/Fleet) - - -- **High-Performance Inference Engines for Comprehensive Deployment Environments** - - 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://paddle-inference.readthedocs.io/en/master/guides/introduction/index_intro.html): Native inference library for high-performance server and cloud inference; [Paddle Serving](https://github.com/PaddlePaddle/Serving): A service-oriented framework suitable for distributed and pipeline productions; [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. - - -- **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 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. - [Click here to learn more](https://github.com/PaddlePaddle/models) - - -## Documentation - -We provide [English](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) and -[Chinese](https://www.paddlepaddle.org.cn/documentation/docs/zh/guide/index_cn.html) documentation. - -- [Guides](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) - - You might want to start from how to implement deep learning basics with PaddlePaddle. - -- [Practice](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html) - - So far you have already been familiar with Fluid. And the next step should be building a more efficient model or inventing your original Operator. - -- [API Reference](https://www.paddlepaddle.org.cn/documentation/docs/en/api/index_en.html) - - Our new API enables much shorter programs. - -- [How to Contribute](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/08_contribution/index_en.html) - - We appreciate your contributions! - -## Communication - -- [Github Issues](https://github.com/PaddlePaddle/Paddle/issues): bug reports, feature requests, install issues, usage issues, etc. -- QQ discussion group: 441226485 (PaddlePaddle). -- [Forums](https://aistudio.baidu.com/paddle/forum): discuss implementations, research, etc. - -## Courses - -- [Server Deployments](https://aistudio.baidu.com/aistudio/course/introduce/19084): Courses intorducing high performance server deployments via local and remote services. -- [Edge Deployments](https://aistudio.baidu.com/aistudio/course/introduce/22690): Courses intorducing edge deployments from mobile, IoT to web and applets. - -## Copyright and License -PaddlePaddle is provided under the [Apache-2.0 license](LICENSE). +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). \ No newline at end of file diff --git a/README_cn.md b/README_cn.md index f4cb6f4fff78eb9a3ab3f3a2abd1f8fc80cd9e4e..2f217b87b90110ec2102b06cd8c48d96322037b3 100644 --- a/README_cn.md +++ b/README_cn.md @@ -1,9 +1,4 @@ - -
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- --------------------------------------------------------------------------------- +Welcome to Algmon Brain [English](./README.md) | 简体中文 @@ -11,84 +6,4 @@ [](https://paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) [](https://paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) [](https://github.com/PaddlePaddle/Paddle/releases) -[](LICENSE) - -欢迎来到 PaddlePaddle GitHub - -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477万，服务企业18万家，基于飞桨开源深度学习平台产生了56万个模型。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。 - -## 安装 - -### PaddlePaddle最新版本: [v2.3](https://github.com/PaddlePaddle/Paddle/tree/release/2.3) - -跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases) - -### 安装最新稳定版本: -``` -# CPU -pip install paddlepaddle -# GPU -pip install paddlepaddle-gpu -``` -更多安装信息详见官网 [安装说明](https://www.paddlepaddle.org.cn/install/quick) - -PaddlePaddle用户可领取**免费Tesla V100在线算力资源**，训练模型更高效。**每日登陆即送8小时**，[前往使用免费算力](https://aistudio.baidu.com/aistudio/index)。 - -## 四大领先技术 - -- **开发便捷的产业级深度学习框架** - - 飞桨深度学习框架采用基于编程逻辑的组网范式，对于普通开发者而言更容易上手，符合他们的开发习惯。同时支持声明式和命令式编程，兼具开发的灵活性和高性能。网络结构自动设计，模型效果超越人类专家。 - - -- **支持超大规模深度学习模型的训练** - - 飞桨突破了超大规模深度学习模型训练技术，实现了支持千亿特征、万亿参数、数百节点的开源大规模训练平台，攻克了超大规模深度学习模型的在线学习难题，实现了万亿规模参数模型的实时更新。 - [查看详情](https://github.com/PaddlePaddle/Fleet) - - -- **支持多端多平台的高性能推理部署工具** - - 飞桨不仅广泛兼容第三方开源框架训练的模型部署，并且为不同的场景的生产环境提供了完备的推理引擎，包括适用于高性能服务器及云端推理的原生推理库 [Paddle Inference](https://www.paddlepaddle.org.cn/inference/product_introduction/inference_intro.html)，面向分布式、流水线生产环境下自动上云、A/B测试等高阶功能的服务化推理框架 [Paddle Serving](https://github.com/PaddlePaddle/Serving)，针对于移动端、物联网场景的轻量化推理引擎 [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite)，以及在浏览器、小程序等环境下使用的前端推理引擎 [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs)。同时，透过与不同场景下的主流硬件高度适配优化及异构计算的支持, 飞桨的推理性能也领先绝大部分的主流实现。 - - -- **面向产业应用，开源开放覆盖多领域的工业级模型库。** - - 飞桨官方支持100多个经过产业实践长期打磨的主流模型，其中包括在国际竞赛中夺得冠军的模型；同时开源开放200多个预训练模型，助力快速的产业应用。 - [查看详情](https://github.com/PaddlePaddle/models) - - -## 文档 - -我们提供 [英文](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) 和 -[中文](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) 文档 - -- [使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) - - 或许您想从深度学习基础开始学习飞桨 - -- [应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html) - - -- [API Reference](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html) - - 新的API支持代码更少更简洁的程序 - - -- [贡献方式](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_contribution/index_cn.html) - - 欢迎您的贡献! - -## 交流与反馈 - -- 欢迎您通过[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)来提交问题、报告与建议 -- QQ群: 441226485 (PaddlePaddle) -- [论坛](https://aistudio.baidu.com/paddle/forum): 欢迎大家在PaddlePaddle论坛分享在使用PaddlePaddle中遇到的问题和经验, 营造良好的论坛氛围 - -## 课程 - -- [服务器部署](https://aistudio.baidu.com/aistudio/course/introduce/19084): 详细介绍高性能服务器端部署实操，包含本地端及服务化Serving部署等 -- [端侧部署](https://aistudio.baidu.com/aistudio/course/introduce/22690): 详细介绍端侧多场景部署实操，从移端端设备、IoT、网页到小程序部署 - -## 版权和许可证 -PaddlePaddle由[Apache-2.0 license](LICENSE)提供 +[](LICENSE) \ No newline at end of file diff --git a/SECURITY_cn.md b/SECURITY_cn.md index cd2b4b450b46e40db9bd298fd176a517dae12113..c7ef1e4ebfc154a42d98c6d48b15348eb3db9ca6 100644 --- a/SECURITY_cn.md +++ b/SECURITY_cn.md @@ -1,49 +1 @@ # 安全使用飞桨 - - - -本文将对飞桨模型及代码安全进行介绍，并介绍如何向飞桨提报漏洞。 - -## 飞桨模型安全 - -飞桨关注模型的安全性和隐私性。其中包括当模型被用于安全攸关场景时，如何避免模型在干扰下输出错误的决策结果，以及如何避免从模型本身、模型梯度或模型推理结果中泄露数据和隐私信息。 - -飞桨的安全和隐私套件[PaddleSleeve](https://github.com/PaddlePaddle/PaddleSleeve)提供了一系列工具，可帮助模型开发者及使用者在模型的开发或部署阶段，系统性地评估并提升模型的安全性和隐私性。这些工具包括对抗样本评估测试、拟自然环境鲁棒性评估测试、模型逆向评估测试、成员推断评估测试、样本去噪、对抗训练、隐私增强优化器等。 - -### 运行非信任模型 - -请永远在沙箱中加载和运行非信任模型并了解其可能造成的影响。 -有多种方式可能导致模型不受信任。飞桨的功能足以在加载不受信任的模型时对系统造成影响，如：`paddle.load` 使用了[pickle](https://docs.python.org/3/library/pickle.html)，这会导致恶意模型执行任意命令。所以在使用非信任模型时需要仔细地审计模型，并在沙箱中运行来确保安全。 - -## 飞桨代码安全 - -飞桨团队一向非常重视代码安全，但鉴于飞桨框架的实现非常复杂，并且依赖了多个第三方开源库，其中仍可能会存在未被发现的问题。因此，我们希望有更多安全研究人员、飞桨开发者能参与到飞桨代码安全保障项目中来，我们鼓励向飞桨负责任的披露(Responsible Disclosure)安全问题，也鼓励向飞桨贡献代码完善动静态漏洞挖掘工具，让飞桨变得更安全。 - -### 安全工具 - -飞桨安全团队对于飞桨框架自身的安全高度重视，为了尽快地发现和修复安全问题，我们内部在持续地进行代码安全审计和研发自动化漏洞挖掘工具。我们将一些工具和方法开源给社区，希望能抛砖引玉，大家一起来贡献提高飞桨的安全性和鲁棒性。工具开源见[CodeSecurity](https://github.com/PaddlePaddle/PaddleSleeve/tree/main/CodeSecurity)。该开源工具包含两部分内容，分别从动态（模糊测试）和静态（CodeQL）两个角度对飞桨代码进行安全审计和漏洞挖掘。通过参照和添加新的测试模块，可以帮助覆盖更多飞桨代码模块，发现更多的代码安全问题。 - -### 报告安全问题 - -我们鼓励向飞桨负责任地披露安全问题，请将所发现的安全问题发送电子邮件到 paddle-security@baidu.com。 - -在安全团队收到邮件后将会及时与您沟通并反馈问题修复进度。 - -为了更好地复现和认定问题情况，请在邮件中： - -- 详细描述漏洞细节，如何复现，并尽量附上PoC。 -- 描述攻击场景，介绍攻击者可能由此问题所能达到的效果。 -- 该问题是否已公开并描述情况。 -- 署名您的姓名和从属关系。 - -我们会将漏洞修复情况注明在飞桨的发布当中，并在致谢公告中发布漏洞情况和提报人（如果您选择不公开署名将不会发布提报人信息）。 - -### 安全问题认定说明 - -飞桨在计算图的过程中，由于模型可以执行任何计算，操作文件，进行网络通信等功能，可能造成内存耗尽，死锁等情况发生，这将导致飞桨产生一些非预期的行为。我们认为只有当这些行为超出了所涉及的操作意图时才算作是安全问题。 - -飞桨框架代码中对于一些非预期的参数和行为会进行检查，Python代码中以抛出异常为形式，C++代码中以返回错误状态为形式。这些情况下，飞桨代码的退出是干净的，但仍可能会因此造成拒绝服务，然而由于飞桨的处理是预期且正确的，所以造成这些情况并不算作是安全问题。 - -如果输入非预期的参数后，对飞桨代码造成了内存破坏，或者非干净退出，这类行为被认定为存在安全问题。 - -### [安全公告](https://github.com/PaddlePaddle/Paddle/blob/develop/security/README_cn.md)