In this video from from 2018 GPU Technology Conference, Ziv Kalmanovich from VMware and Fred Devoir from NVIDIA describe how they are working together to bring the benefits of virtualization to GPU workloads.
Machine learning is an exciting area of technology that allows computers to behave without being explicitly programmed, that is, in the way a person might learn. This tech is increasingly applied in many areas like health science, finance, and intelligent systems, among others. In recent years, the emergence of deep learning and the enhancement of accelerators like GPUs has brought the tremendous adoption of machine learning applications in a broader and deeper aspect of our lives. Some application areas include facial recognition in images, medical diagnosis in MRIs, robotics, automobile safety, and text and speech recognition. Machine learning workloads have also become a critical part in cloud computing. For cloud environments based on vSphere, you can even deploy a machine learning workload yourself using GPUs via the VMware DirectPath I/O or vGPU technology.
GPUs reduce the time it takes for a machine learning or deep learning algorithm to learn (known as the training time) from hours to minutes. With VMware vSphere, you can run machine learning and other GPU workloads in a productive enterprise-ready environment.
