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

缺乏在农业食品和环境领域拥有由高性能计算(HPC)环境实现计算到规模能力的专业知识的科学家。农业食品研究人员对HPC能力的采用程度较低，这在很大程度上可以归因于使用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针对具体问题的关注，缺乏对这一特定劳动力部门熟悉并因此有助于提高技能的定制学习数据和问题集的实际使用机会。该项目建议开发和部署一个针对农业食品科学中传播与信息应用的多模块学习课程，作为一个同步的虚拟课程，提供大量基于实际应用的学习机会。这些挑战将使拟议的工作具有普遍性，从而使其他寻求利用数据科学和高性能计算方面的核心进展的留守社区能够利用根据这一提议制定的办法和基础设施。拟议的多模块课程侧重于培养基础的、数据驱动的技能，通过量身定做的、适合学科的课程材料，为农业食品科学家弥合特定领域科学和计算机科学之间的差距，建立一个可持续的熟练CI用户群体。该建议旨在开发和部署一个针对农业食品科学中的网络基础设施(CI)应用程序，特别是高性能计算(HPC)的多模块学习课程，该课程作为一个同步的、虚拟的课程提供，具有大量的动手应用程序学习机会。这项30人的课程将通过集装箱化的学习环境提供，以确保所有学员都可以随时使用一套相同的工具。前三个课程模块提供了基于HPC的分析的基本构件，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对关键农业食品问题进行HPC分析所需的能力水平。该课程将面向美国和海外的学术界(本科生、研究生和教职员工)受众(特别是面向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访问，该试点项目将在Microsoft 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有、学术和基于云的HPC资源组合。项目小组将与内部和外部农业食品网络合作，并利用Access Knowledge Base Ask.CI和/或社区亲和力小组的能力。该小组将在整个赠款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。在为农业食品研究人员提供了HPC课程的阿尔法、贝塔和全程课程实例后，该课程将继续通过其GEM学习组合每年提供2-3次。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.