Machine Learning on VMware vSphere with NVIDIA GPUs

Efficient deployment of GPU-based machine learning, especially deep learning, in cloud environments is an important focus of research and development. As the leader in cloud infrastructure software, VMware provides multiple solutions that optimize performance and enhance flexibility for machine learning workloads. We'll present the results of our research on machine learning with NVIDIA GPUs on VMware's vSphere platform. You will learn different ways to deploy GPU-based workloads developed with popular machine learning frameworks like TensorFlow in a virtualized environment using VMware DirectPath I/O and NVIDIA GRID vGPU solutions. We'll discuss how to mix workloads to maximize resource utilization and deployment flexibility by running machine learning together with other workloads on the same server. Finally, we'll present the performance characteristics of machine learning with GPUs for multiple use cases and at different scales in virtualized cloud data centers.

Uday Kurkure works on Virtual Desktop Infrastructure (VDI) Performance at VMware. Uday has broad experience in Computer Graphics, ASIC Design, and Compilers. He has worked at Adobe Systems, Transmeta, Synopsys and MIPS Computers. Uday holds a MS degree in Computer Science from Stanford University and a B. Tech. in Electronics and Telecommunications from Indian Institute of Technology. He had been granted 9 patents.