GTC Silicon Valley-2019 ID:S9815:Maximizing Utilization of NVIDIA Virtual GPUs in VMware vSphere for End-to-End Machine Learning
Uday Kurkure(VMware),Manvender Rawat(NVIDIA)
End-to-end machine learning workloads perform well using NVIDIA virtual GPUs in VMware vSphere. We'll discuss how to combine the performance of NVIDIA GPUs with manageability and scalability features and maximize GPU utilization for machine learning workloads using VMware and NVIDIA technology. We will outline end-to-end machine learning, including training, deploying for inferencing, and managing a production environment using VMware vSphere and VMware's Pivotal Kubernetes Service. NVIDIA Turing architecture is positioned for mixed-precision training and inferencing workloads. We'll describe ways to deploy GPU-Based workloads developed with machine learning frameworks like TensorFlow and Caffe2 by using VMware DirectPathIO and NVIDIA virtual GPU (vGPU). We'll also provide case studies that leverage vGPU scheduling options such as Equal Share, Fixed Share, and Best Effort, and illustrate their benefits with our performance study.