发表机构
TUD Dresden University of Technology; Center for Interdisciplinary Digital Sciences (CIDS); Center for Information Services and High Performance Computing (ZIH); Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（德累斯顿工业大学; 跨学科数字科学中心; 信息服务与高性能计算中心; 可扩展数据分析与人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
JuPyLive可在Jupyter笔记本环境内一键实现本地与HPC资源间的双向无缝迁移，无需用户修改代码或配置，助力跨学科研究人员便捷使用HPC集群扩展资源密集型工作流。
AI 中文摘要
本研究提出JuPyLive，一种可在用户工作站本地资源与高性能计算（HPC）环境远程资源之间无缝迁移Jupyter笔记本的机制，同时保留用户体验。JuPyLive消除了迁移过程的底层复杂性，使用户可在熟悉的Jupyter笔记本环境内通过一次点击，在可用本地与远程资源间自由选择。JuPyLive利用ElasticNotebook管理内存状态迁移，自动分配HPC集群资源，并编排源与目标间所需的远程通信通道以实现双向迁移。此外，JuPyLive的HPC状态监视器提供可用远程资源的实时概览，使用户在启动迁移前能做出明智的资源选择决策。该全自动机制无需最终用户修改代码或配置，也无需学习新语法，迁移过程可通过Jupyter内的可视化元素直观启动与监控。通过弥合本地工作区与远程资源间的差距，JuPyLive以最少的用户干预为资源密集型工作流的扩展提供了无缝体验，从而进一步促进跨学科研究人员使用HPC集群。
英文摘要
This work introduces JuPyLive, a migration mechanism that enables seamless transition of Jupyter notebooks between local resources of user's workstation and remote resources of high-performance computing~(HPC) environments, while preserving the user experience. JuPyLive eliminates the underlying complexities of migration process, enabling users to freely choose among available local and remote resources, directly within the familiar Jupyter notebook environment via a single click. JuPyLive leverages ElasticNotebook to manage in-memory state migration, it automates resource allocation on HPC cluster and orchestrates required remote communication channels between the source and destination to enable a bidirectional migration. Furthermore, HPC status monitor of JuPyLive provides a live overview of available remote resources, allowing users to make informed decisions on choosing the relevant resources before initiating a migration process. The proposed fully automatic mechanism requires no code changes or configurations by the end user, nor does it demand users to learn a new syntax, instead the migration process can be intuitively initiated and monitored using visual elements from within the Jupyter notebook. By bridging the gap between local workspace and remote resources, JuPyLive offers a seamless experience for scaling local resource-intensive workflows with minimal user intervention, thus further democratizing the usage of HPC clusters among the interdisciplinary researchers.
Comments9 pages, 2 figures
