Cyber Training: Pilot -- Breaking the Compute Barrier, Upskilling Agri-Food Researchers to Utilize HPC Resources

在农业食品和环境领域，缺乏具有高性能计算（HPC）环境支持的计算机到规模能力的专业科学家。农业食品研究人员对HPC能力的低采用率主要归因于使用HPC的实际（或感知）复杂性。此外，植根于CSE科学的传统培训课程往往缺乏情境化的问题重点，也缺乏对定制的学习数据和问题集的实际操作，而这些数据和问题集对于提高这一特定劳动力部门的技能是很有用的。该项目建议开发和部署针对农业食品科学中的ci应用量身定制的多模块学习课程，该课程作为同步的虚拟课程提供大量基于实践应用的学习机会。提议工作的挑战将被普遍化，这样其他落后的社区寻求利用数据科学和高性能计算的核心进步，可以利用在这个提议下开发的方法和基础设施。拟议的多模块课程侧重于通过量身定制的、适合学科的课程材料，为农业食品科学家弥合领域特定科学与计算机科学之间的差距，建立必要的基础、数据驱动技能，以创建一个可持续发展的熟练CI用户社区。该提案旨在开发和部署针对农业食品科学中的网络基础设施（CI）应用（特别是高性能计算（HPC））量身定制的多模块学习课程，该课程作为同步的虚拟课程提供大量基于实践应用的学习机会。这门30人的课程将通过一个集装箱化的学习环境进行，以确保所有学习者都能随时使用一套相同的工具。前三个课程模块为基于HPC的分析提供了基本的构建模块，随后是一系列实践应用模块，使农业食品研究人员具备促进关键农业食品问题HPC分析所需的能力水平。该课程将面向美国国内外的学术（本科生、研究生和教职员工）受众（特别是针对代表性不足的学生群体），以及在美国政府机构和农业企业工作的个人。为了实现学术和非学术的可访问性，该试点项目将在微软Azure云计算基础设施上托管以ci为重点的农业食品分析课程，但该课程将向学习者介绍可用的私人、学术和基于云的HPC资源组合。项目团队将与内部和外部农业食品网络合作，并利用ACCESS知识库查询的功能。CI和/或社区关联组。该团队将在整个资助期间进行一系列外部和内部内容和交付审计，以确保为农业食品研究人员确定最佳的HPC学习途径。在提供了他们的农业食品研究人员HPC课程的alpha、beta和完整课程实例之后，该课程将通过他们的GEMS学习组合每年提供2-3次，超过赠款的生命周期。该奖项反映了美国国家科学基金会的法定使命，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。

There is a dearth of scientists with expertise in the agri-food and environment domains that have compute-to-scale capabilities enabled by High Performance Computing (HPC) environments. Low adoption of HPC capabilities among agri-food researchers can be largely attributed to the real (or perceived) complexity of using HPC. Moreover, traditional training courses rooted in the CSE sciences often lack the contextualized problem focus and hands-on access to tailor-made learning data and problem sets that are familiar to and thus useful for upskilling this particular sector of the workforce. This project proposes to develop and deploy a multi-module learning curriculum tailored to CI-applications in the agri-food sciences that is provided as a synchronous, virtual offering with substantial hands-on application-based learning opportunities. The challenges that the proposed work will be made generalizable, such that other left-behind communities seeking to capitalize on core advances in data science and HPC can leverage the approaches and infrastructure developed under this proposal. The proposed multi-module course is focused on building the foundational, data-driven skills necessary to create a sustainable community of skilled CI Users through tailored, discipline-appropriate course materials targeted at bridging the gap between domain specific science and computer science for agri-food scientists.This proposal aims to develop and deploy a multi-module learning curriculum tailored to Cyberinfrastructure (CI)-applications, notably High-Performance Computing (HPC), in the agri-food sciences that is provided as a synchronous, virtual offering with substantial hands-on application-based learning opportunities. The 30-person course will be delivered via a containerized learning environment to ensure all learners have ready access to an identical set of tools. The first three course modules provide the basic building blocks for HPC-based analytics, followed by a series of hands-on application modules that enable agri-food researchers with the levels of competency needed to facilitate HPC analyses of critical agri-food problems. The course will be accessible to academic (undergraduate, graduate, and faculty/staff) audiences around the US and abroad (especially targeting underrepresented populations of students), as well as individuals working in US government agencies and agri-business firms. To enable both academic and non-academic accessibility, this pilot project will host the CI-focused agri-food analytics curriculum on Microsoft Azure cloud computing infrastructure, but the course will introduce learners to the portfolio of available private, academic, and cloud-based HPC resources. The project team will work with internal and external agri-food networks and leverage the capabilities of the ACCESS Knowledge Base Ask.CI and/or Community Affinity Groups. The team will engage in a series of external and internal content and delivery audits throughout the grant period to ensure the identification of optimal HPC learning pathways for agri-food researchers. After delivering alpha-, beta- and full-course instances of their HPC for Agri-Food Researchers course, the course will continue to be offered 2-3 times annually through their GEMS Learning portfolio beyond the life of the grant.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.