Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure

随着人工智能的最新进展，深度学习系统和应用程序已成为多个跨学科领域的驱动力。虽然这种发展在很大程度上得到了先进GPU网络基础设施的快速改进的支持，但通常缺乏将联合收割机应用驱动的深度学习技术与使用GPU网络基础设施实施此类技术相结合的综合培训材料。为了填补这一空白，该项目开发了一个在线研讨会，由来自五个学科的六名教师提供的一系列跨学科前沿培训课程组成。本次研讨会重点关注基于GPU的深度学习系统和应用的最新创新，培养下一代网络基础设施用户和贡献者社区，他们可以使用，开发和改进先进的GPU网络基础设施，以进行深度学习研究。这些培训工作增强了深度学习和GPU网络基础设施员工的知识，随后有助于解决重要的科学和社会问题，包括地理学中的水文测绘，航空航天中的空间环境临近预报，以及交通运输中的自动驾驶和交通监控。该项目开发的跨学科研讨会旨在使包括本科毕业生、研究生和研究人员在内的参与者能够提高其多学科技能组合，扩大其学术研究组合，发展其远程协作能力，并显著增强其职业竞争力。为了实现这一目标，密集研讨会包括1）一系列实践讲座模块，为学员提供先进GPU网络基础设施中的全栈深度学习系统的全面知识和技能，2）一系列关于先进GPU网络基础设施和深度学习系统的前沿研究的讲座，由学术和工业研究机构的知名科学家邀请，3）远程开放的跨学科合作项目，将讲座中介绍的技术应用于实践。此外，还开发了一个互动在线培训系统原型，为学员提供计算机资源，并跟踪他们的学习进度，以提高培训活动的成效和效率。该项目有望培养未来深度学习系统和应用的研究队伍，并扩大先进GPU网络基础设施在研究和教育中的采用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the recent advancements in artificial intelligence, deep learning systems and applications have become a driving force in multiple transdisciplinary domains. While this evolution has been largely supported by the rapid improvements in advanced GPU cyberinfrastructure, comprehensive training materials are generally absent that combine application-driven deep learning techniques with the implementation of such techniques using the GPU cyberinfrastructure. To fill in this gap, this project develops an online workshop that comprises of a set of interdisciplinary cutting-edge training sessions offered by six faculty members from five disciplines. With a focus on the latest innovations in GPU-based deep learning systems and applications, this workshop fosters a community of the next-generation cyberinfrastructure users and contributors, who can use, develop, and improve advanced GPU cyberinfrastructure for their deep learning research. Such training efforts enhance the knowledge of the deep learning and GPU cyberinfrastructure workforce, and subsequently contribute to the solutions of important scientific and societal problems, including hydrographic mapping in geography, space environment nowcasting in aerospace, and autonomous driving and traffic monitoring in transportation. The workshop will also attract trainees from underrepresented groups, including minority students and researchers from rural areas.The interdisciplinary workshop developed in this project aims at enabling participants, including undergraduate seniors, graduate students, and researchers, to improve their multidisciplinary skill-sets, extend their academic research portfolios, develop their remote collaboration capacities, and significantly strengthen their career competitiveness. To achieve this goal, the intensive workshop includes 1) a set of hands-on lecture modules that provide trainees with comprehensive knowledge and skills on the full stack of deep learning systems in advanced GPU cyberinfrastructure, 2) a series of talks on the cutting-edge research in advanced GPU cyberinfrastructure and deep learning systems and application given by renowned scientists invited from academic and industrial research institutes, and 3) a remote open-ended interdisciplinary collaborative project of applying techniques introduced in lectures into practice. In addition, a prototype of an interactive online training system is developed to provide computing resources for the trainees and to track their learning progress, for more effective and efficient training activities. The project is expected to develop a future research workforce in deep learning systems and applications and to broaden the adoption of advanced GPU cyberinfrastructure in research and education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.