GTC Silicon Valley-2019 ID:S9888:Virtual GPU Powers AI and Deep Learning in Universities
Emily Apsey(NVIDIA),Konstantin Cvetanov(NVIDIA),Neranjan Edirisinghe(Georgia State University),John Meza(Esri)
Universities have increasing demand for Deep Learning/AI classrooms or labs but are constrained by cost and availability of physical classroom labs. Students require access to a lab 24x7 to work on projects and assignments and find that they have to wait for HPC clusters to be free when submitting their jobs for training. In the past, students and researchers are tethered and require expensive data scientist workstations. Virtual GPUs provide a highly secure, flexible, accessible solution to power AI and deep learning coursework and research. Learn how Nanjing University is using virtual vGPUs with NGC for teaching AI and Deep learning courses, empowering researchers with the GPU power they need, and providing students with mobility to do coursework anywhere. Similarly, discover how other universities are maximizing their data center resources by running VDI, HPC and AI workloads on common infrastructure and even how companies like Esri are using virtualized deep learning classes to educate their user base. Discover the benefits of vGPUs for AI and how you can setup your environment to achieve optimum performance, as well as the tools you can use to manage and monitor your environment as you scale.