by Uday Kurkure, Lan Vu, and Hari Sivaraman
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. In a series of blogs, we will present the performance results of running machine learning benchmarks on VMware vSphere using NVIDIA GPUs.
This is episode 1. Also see:
- Episode 2: Machine Learning on vSphere 6 with NVIDIA GPUs
- Episode 3: Performance Comparison of Native GPU to Virtualized GPU and Scalability of Virtualized GPUs for Machine Learning
Episode 1: Performance Results of Machine Learning with DirectPath I/O and NVIDIA GPUs
In this episode, we present the performance results of running machine learning benchmarks on VMware vSphere with NVIDIA GPUs in DirectPath I/O mode and on GRID virtual GPU (vGPU) mode.