Data scientists use Graphics Processing Unit, or GPU, to achieve the highest performance for deep learning training and inference. However, there is complexity to managing those hardware resources efficiently which may be outside the scope of the data scientists’ expertise. OpenShift is the ideal platform for simplifying that complexity by providing powerful abstractions for scalable cloud computing. This session will review the value of GPU in data science, how modern deep learning software frameworks consume GPU resources, and the operator-based architecture that enables GPU in OpenShift today.
