forked from beimingwu/beimingwu
perf(docs): polish contents in system docs
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@ -5,7 +5,7 @@ title: Home
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name: "Beimingwu:"
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text: "A Learnware Dock System"
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tagline: As the first systematic implementation of the learnware paradigm, the system offers a solid research platform for learnware-related studies, and aims to help users efficiently solve machine learning tasks without starting from scratch.
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tagline: Beimingwu, as the first systematic open-source implementation of learnware, offers a preliminary research platform for learnware-related studies, and aims to help users efficiently solve machine learning tasks without starting from scratch.
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- theme: brand
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# Beimingwu: A Learnware Dock System
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The _learnware_ paradigm was proposed by Professor Zhi-Hua Zhou in 2016 [1, 2]. In this paradigm, developers worldwide can share models with the _learnware dock system_, which effectively searches for and reuse learnware(s) to help users solve machine learning tasks efficiently without starting from scratch.
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_Learnware_ was proposed by Professor Zhi-Hua Zhou in 2016 [1, 2]. In the _learnware paradigm_, developers worldwide can share models with the _learnware dock system_, which effectively searches for and reuse learnware(s) to help users solve machine learning tasks efficiently without starting from scratch.
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Beimingwu is the first systematic implementation of the _learnware_ paradigm, offering a solid research platform for learnware-related studies.
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Beimingwu is the first systematic open-source implementation of learnware dock system, providing a preliminary research platform for learnware studies. Developers worldwide can submit their models freely to the learnware dock. They can generate specifications for the model with the help of Beimingwu without disclosing their raw data, and then the model and specification can be assembled into a learnware, which will be accommodated in the learnware dock. Future users can solve their tasks by submitting their requirements and reusing helpful learnwares returned by Beimingwu, while also not disclosing their own data. It is anticipated that after Beimingwu accumulates millions of learnwares, an "emergent" behavior may occur: machine learning tasks that have never been specifically tackled may be solved by assembling and reusing some existing learnwares.
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[1] Zhi-Hua Zhou. Learnware: on the future of machine learning. _Frontiers of Computer Science_, 2016, 10(4): 589–590 <br/>
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[2] Zhi-Hua Zhou. Machine Learning: Development and Future. _Communications of CCF_, 2017, vol.13, no.1 (2016 CNCC keynote)
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@ -71,7 +71,7 @@ The functionalities of the four layers are described as follows:
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## What Features Does the Beimingwu System Have?
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The Beimingwu learnware dock system, serving as a research platform for the learnware paradigm, systematically implements the core processes of the paradigm for the first time:
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The Beimingwu learnware dock system, serving as a preliminary research platform for learnware, systematically implements the core processes of the learnware paradigm for the first time:
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- **Submitting Stage**: The system includes multiple detection mechanisms to ensure the quality of uploaded learnwares. Additionally, the system trains a heterogeneous engine based on existing learnware specifications in the system to merge different specification islands and assign new specifications to learnwares. With more learnwares are submitted, the heterogeneous engine will continue to update, achieving continuous iteration of learnware specifications and building a more precise specification world.
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- **Deploying Stage**: After users upload task requirements, the system automatically selects whether to recommend a single learnware or multiple learnware combinations and provides efficient deployment methods. Whether it's a single learnware or a combination of multiple learnwares, the system offers convenient learnware reuse tools.
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## Beimingwu v1.0 Version (2024.01)
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The Beimingwu learnware dock system v1.0 version is the first systematic implementation of the _learnware_ paradigm, offering a solid research platform for learnware-related studies.
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The Beimingwu learnware dock system v1.0 version is the first systematic open-source implementation of _learnware_, offering a preliminary research platform for learnware-related studies.
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The system is designed to help users efficiently solve machine learning tasks without starting from scratch by effectively searching for and reusing learnware(s).
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### v1.0 System Features
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### v1.0 R&D Team
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- System Principal: <a href="http://cs.nju.edu.cn/zhouzh" style="text-decoration: none; color: inherit;">Zhi-Hua Zhou</a>、<a href="http://www.lamda.nju.edu.cn/yuy" style="text-decoration: none; color: inherit;">Yang Yu</a>
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The Beimingwu R&D Team consists of <a href="http://cs.nju.edu.cn/zhouzh" style="text-decoration: none; color: inherit;">Zhi-Hua Zhou</a>, <a href="http://www.lamda.nju.edu.cn/yuy" style="text-decoration: none; color: inherit;">Yang Yu</a>, <a href="http://www.lamda.nju.edu.cn/tanzh/" style="text-decoration: none; color: inherit;">Zhi-Hao Tan</a>, <a href="http://www.lamda.nju.edu.cn/liujd/" style="text-decoration: none; color: inherit;">Jian-Dong Liu</a>, <a href="http://www.lamda.nju.edu.cn/tanp/" style="text-decoration: none; color: inherit;">Peng Tan</a>, <a href="http://www.lamda.nju.edu.cn/bixd/" style="text-decoration: none; color: inherit;">Xiao-Dong Bi</a>, <a href="http://www.lamda.nju.edu.cn/zhengqc/" style="text-decoration: none; color: inherit;">Qin-Cheng Zheng</a>, Xiao-Chuan Zou, <a href="http://www.lamda.nju.edu.cn/xiey/" style="text-decoration: none; color: inherit;">Yi Xie</a>, Hai-Tian Liu, Hao-Yu Shi, Xin-Yu Zhang and others.
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<!-- - System Principal: <a href="http://cs.nju.edu.cn/zhouzh" style="text-decoration: none; color: inherit;">Zhi-Hua Zhou</a>、<a href="http://www.lamda.nju.edu.cn/yuy" style="text-decoration: none; color: inherit;">Yang Yu</a>
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- System R&D Principal: <a href="http://www.lamda.nju.edu.cn/tanzh/" style="text-decoration: none; color: inherit;">Zhi-Hao Tan</a>
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- System Architect & Developer: <a href="http://www.lamda.nju.edu.cn/liujd/" style="text-decoration: none; color: inherit;">Jian-Dong Liu</a>
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- Engine Architect & Developer: <a href="http://www.lamda.nju.edu.cn/bixd/" style="text-decoration: none; color: inherit;">Xiao-Dong Bi</a>
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- Backend Architect & Developer: Xiao-Chuan Zou
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- Algorithm Developer & Learnware Preparation: <a href="http://www.lamda.nju.edu.cn/xiey/" style="text-decoration: none; color: inherit;">Yi Xie</a>、Hai-Tian Liu、Hao-Yu Shi、Xin-Yu Zhang
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In addition, during the early prototype development phase of the system, <a href="https://www.lamda.nju.edu.cn/guolz/" style="text-decoration: none; color: inherit;">Lan-Zhe Guo</a>, <a href="https://www.lamda.nju.edu.cn/chenzx/" style="text-decoration: none; color: inherit;">Zi-Xuan Chen</a>, <a href="https://www.lamda.nju.edu.cn/zhouz/" style="text-decoration: none; color: inherit;">Zhi Zhou</a>, and <a href="https://www.lamda.nju.edu.cn/jinyx/" style="text-decoration: none; color: inherit;">Yi-Xuan Jin</a> were also involved.
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In addition, during the early prototype development phase of the system, <a href="https://www.lamda.nju.edu.cn/guolz/" style="text-decoration: none; color: inherit;">Lan-Zhe Guo</a>, <a href="https://www.lamda.nju.edu.cn/chenzx/" style="text-decoration: none; color: inherit;">Zi-Xuan Chen</a>, <a href="https://www.lamda.nju.edu.cn/zhouz/" style="text-decoration: none; color: inherit;">Zhi Zhou</a>, and <a href="https://www.lamda.nju.edu.cn/jinyx/" style="text-decoration: none; color: inherit;">Yi-Xuan Jin</a> were also involved. -->
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@ -5,7 +5,7 @@ title: 主页
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name: "北冥坞：学件基座系统"
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# text: "系统文档"
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tagline: 学件范式的首次系统性实现，为学件相关研究提供了坚实的科研平台。该系统旨在帮助用户高效解决机器学习任务，而无需从零开始构建模型。
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tagline: 学件的第一个系统性开源实现，为学件相关研究提供了一个初步科研平台。该系统旨在帮助用户高效解决机器学习任务，而无需从零开始构建模型。
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image: /logo-no-text.svg
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# 北冥坞：学件基座系统
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学件范式由周志华教授在 2016 年提出 [1, 2]。在学件范式下，世界各地的开发者可分享模型至学件基座系统，系统通过有效查搜和复用学件帮助用户高效解决机器学习任务，而无需从零开始构建机器学习模型。
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学件由周志华教授在 2016 年提出 [1, 2]。在学件范式下，世界各地的开发者可分享模型至学件基座系统，系统通过有效查搜和复用学件帮助用户高效解决机器学习任务，而无需从零开始构建机器学习模型。
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北冥坞是学件范式的首次系统性实现，为学件相关研究提供了坚实的科研平台。
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北冥坞是学件的第一个系统性开源实现，为学件相关研究提供了一个初步科研平台。有分享意愿的开发者可自由提交模型，学件坞协助产生规约形成学件存放在学件坞中，开发者在这个过程中无需向学件坞泄露自己的训练数据。未来的用户可以通过向学件坞提交需求，在学件坞协助下查搜复用学件来完成自己的机器学习任务，且用户可以不向学件坞泄露自有数据。预计在学件坞拥有数以百万计的学件后，将可能出现“涌现”行为：以往没有专门开发过模型的机器学习任务，可能通过复用若干个现有学件而解决。
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[1] Zhi-Hua Zhou. Learnware: on the future of machine learning. _Frontiers of Computer Science_, 2016, 10(4): 589–590 <br/>
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[2] 周志华. 机器学习: 发展与未来. 中国计算机学会通讯, 2017, vol.13, no.1 (2016 中国计算机大会 keynote)
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## 北冥坞系统有哪些特性？
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北冥坞学件基座系统作为学件范式的科研平台，首次系统性地实现了学件范式中的核心流程：
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北冥坞学件基座系统作为学件的初步科研平台，首次系统性地实现了学件范式中的核心流程：
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- **提交阶段**：系统内置了多重检测机制，以确保上传学件的质量。另外，系统会根据已有的学件规约，训练一个异构引擎，用于合并不同的规约岛屿，以及为学件赋予新规约。随着更多学件的上传，异构引擎将持续更新，实现学件规约的持续迭代，构建更精准的规约世界。
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- **部署阶段**：用户上传任务需求后，系统会自动选择是推荐单学件还是多学件组合，并提供高效的部署方式。无论是单个学件还是多学件组合，系统均提供了便捷的学件复用接口。
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## 北冥坞 v1.0 版本 (2024.01)
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北冥坞学件基座系统 v1.0 版本是学件范式的首次系统性实现，为学件相关研究提供了坚实的科研平台。该系统旨在通过有效查搜和复用学件，帮助用户高效解决机器学习任务，而无需从零开始构建机器学习模型。
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北冥坞学件基座系统 v1.0 版本是学件的第一个系统性开源实现，为学件相关研究提供了一个初步科研平台。该系统旨在通过有效查搜和复用学件，帮助用户高效解决机器学习任务，而无需从零开始构建机器学习模型。
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### v1.0 系统特点
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### v1.0 研发团队
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- 系统总负责：<a href="http://cs.nju.edu.cn/zhouzh" style="text-decoration: none; color: inherit;">周志华</a>、<a href="http://www.lamda.nju.edu.cn/yuy" style="text-decoration: none; color: inherit;">俞扬</a>
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北冥坞研发团队人员包括：<a href="http://cs.nju.edu.cn/zhouzh" style="text-decoration: none; color: inherit;">周志华</a>、<a href="http://www.lamda.nju.edu.cn/yuy" style="text-decoration: none; color: inherit;">俞扬</a>、<a href="http://www.lamda.nju.edu.cn/tanzh/" style="text-decoration: none; color: inherit;">谭志豪</a>、<a href="http://www.lamda.nju.edu.cn/liujd/" style="text-decoration: none; color: inherit;">刘建东</a>、<a href="http://www.lamda.nju.edu.cn/tanp/" style="text-decoration: none; color: inherit;">谭鹏</a>、<a href="http://www.lamda.nju.edu.cn/bixd/" style="text-decoration: none; color: inherit;">毕晓栋</a>、<a href="http://www.lamda.nju.edu.cn/zhengqc/" style="text-decoration: none; color: inherit;">郑钦城</a>、邹晓川、<a href="http://www.lamda.nju.edu.cn/xiey/" style="text-decoration: none; color: inherit;">谢逸</a>、刘海天、史浩宇、张鑫宇等人。
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<!-- - 系统总负责：<a href="http://cs.nju.edu.cn/zhouzh" style="text-decoration: none; color: inherit;">周志华</a>、<a href="http://www.lamda.nju.edu.cn/yuy" style="text-decoration: none; color: inherit;">俞扬</a>
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- 系统研发负责：<a href="http://www.lamda.nju.edu.cn/tanzh/" style="text-decoration: none; color: inherit;">谭志豪</a>
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- 系统架构与开发：<a href="http://www.lamda.nju.edu.cn/liujd/" style="text-decoration: none; color: inherit;">刘建东</a>
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- 引擎架构与开发：<a href="http://www.lamda.nju.edu.cn/bixd/" style="text-decoration: none; color: inherit;">毕晓栋</a>
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- 后端架构与开发：邹晓川
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- 算法开发与开源学件准备：<a href="http://www.lamda.nju.edu.cn/xiey/" style="text-decoration: none; color: inherit;">谢逸</a>、刘海天、史浩宇、张鑫宇
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此外，系统早期原型开发期间还有<a href="https://www.lamda.nju.edu.cn/guolz/" style="text-decoration: none; color: inherit;">郭兰哲</a>、<a href="http://www.lamda.nju.edu.cn/chenzx/" style="text-decoration: none; color: inherit;">陈梓轩</a>、<a href="http://www.lamda.nju.edu.cn/zhouz/" style="text-decoration: none; color: inherit;">周植</a>、<a href="http://www.lamda.nju.edu.cn/jinyx/" style="text-decoration: none; color: inherit;">金苡萱</a>参与。
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此外，系统早期原型开发期间还有<a href="https://www.lamda.nju.edu.cn/guolz/" style="text-decoration: none; color: inherit;">郭兰哲</a>、<a href="http://www.lamda.nju.edu.cn/chenzx/" style="text-decoration: none; color: inherit;">陈梓轩</a>、<a href="http://www.lamda.nju.edu.cn/zhouz/" style="text-decoration: none; color: inherit;">周植</a>、<a href="http://www.lamda.nju.edu.cn/jinyx/" style="text-decoration: none; color: inherit;">金苡萱</a>参与。 -->
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