Tuesday, October 26, 2021
Kubernetes has become the de-facto tool for orchestrating containerized workloads, and AI workloads are no different. Built to provide isolated environments and simplify reproducibility and portability, it’s an obvious choice for data science, and an ecosystem of data science tools has been built around containers and K8s. But can an orchestrator built for services meet the needs of research experimentation? Can IT easily incorporate K8s into their workflows? Join Guy Salton of Run:AI for a crash course in Kubernetes for AI. Learn what’s working, what’s not, and some fixes for supporting research environments with K8s.