By Uday Kurkure, Lan Vu, and Hari Sivaraman VMware, with Dell, submitted its first machine learning benchmark results to MLCommons. The results—which show that high performance can be achieved on a VMware virtualized platform featuring NVIDIA GPU and AI technology—were accepted and published in the MLPerf 1.1 Inference category. The testbed consisted of a VMware … Continued The post VMware and NVIDIA solutions deliver high performance in machine learning workloads appeared first on VROOM!…Read More
VMware and NVIDIA continue to grow their partnership with NVIDIA’s accelerated computing and networking platform, and you can experience it all at VMworld 2021. Register for the virtual event, where NVIDIA will be featured in 25 sessions that cover AI and machine learning, 5G and edge services, Project Monterey over NVIDIA’s BlueField-2 DPU, and multi-cloud … Continued The post NVIDIA at VMworld 2021 appeared first on VMworld Blog.
Mission-ready, Trusted Platform for Modern Warfighters VMware supports the modern warfighter by addressing key tactical kit challenges. First, VMware’s hyperconverged technology drastically reduces the SWaP-C (size, weight, power, and cost) requirements. What used to require 750 pounds of […]
Introduction While virtualization technologies have proven themselves in the enterprise with cost effective, scalable and reliable IT computing, High Performance Computing (HPC) however has not evolved and is still bound to dedicating physical resources to obtain explicit runtimes and maximum […]
Nvidia today announced the general availability of AI Enterprise, a software suite of tools and frameworks that enable companies running VMware vSphere to virtualize AI workloads on Nvidia-certified servers.
Today NVIDIA has launched its brand new enterprise solution: NVIDIA Enterprise AI
NVIDIA AI Enterprise software suite is an end-to-end cloud-native suite of AI tools and frameworks, optimized and exclusively certified by NVIDIA to run on VMware vSphere. This includes frameworks that are broadly applicable and used across vertical industries such as manufacturing, logistics, financial services, retail, and healthcare. For example, it includes:
TensorFlow and PyTorch for machine learning
NVIDIA Tensor RT, for GPU optimized deep learning inference and NVIDIA Triton Inference Server to deploy trained AI models at scale
RAPIDS, for end-to-end data science and analytics pipelines
This unique combination of the NVIDIA AI Enterprise software suite with VMware vSphere is the next step in delivering a powerful AI-Ready Enterprise Platform.
Scale without compromise: With NVIDIA Virtual GPUs (vGPUs) enabling both time-sliced sharing as well as new multi-instance GPU (MIG) hardware-level spatial partitioning, customers have multiple options in how they can share valuable GPU resources across more data scientists and increase GPU utilization as they scale AI workloads in the enterprise.
vSphere DRS automatically places workloads across AI infrastructure at scale for optimal consumption, and vSphere vMotion provides live migration to simplify infrastructure maintenance such as consolidation, expansion, or upgrades.
NVIDIA datacenter and edge portfolio, a GPU for every possible workload.
Nvidia Networking portfolio for Enterprise AI
Multi Instance GPU (MIG) is especially useful for AI Inference. Fully isolated and secure instances at the hardware level with dedicated high-bandwidth memory, cache, and compute cores.
AI workloads come in all sizes with a wide variety of data requirements. Some process images, like live traffic reporting systems or online shopping recommender systems. Others are text-based, like a customer service support system powered by conversational AI. Everything is possible with this very cool new solution from NVIDIA and VMware. I really love it
For the last 18 months, I’ve been focusing on machine learning, especially how customers can successfully deploy machine learning infrastructure on a vSphere infrastructure. This space is exciting as it has so many great angles to explore. Besides the model training, a lot of stuff happens […]
Last year at VMworld 2020 we announced Project Monterey, the next step in the evolution of VMware Cloud Foundation to meet the needs of next generation of applications. Project Monterey leverages the power of dynamic composability of hardware accelerators to improve the overall data center […]
This operations guide demonstrates the prerequisites and procedures for conducting multiple Day-2 operational jobs including upgrading a VMware vSphere® Cluster with running VMs that are configured with one or more vGPUs, scaling up and down the GPU resources for the Machine Learning workload […]
Co-authored with Phil Hummel, Distinguished Member Technical Staff, Dell Technologies Data is in the driver’s seat The realization that “Data is King” is driving businesses to evolve their capability to tap into their unique sets of data. The potential for high-value business outcomes made […]
Sizing guidance for vSphere systems running machine learning workloads. Recommendations for CPUs, GPUs, memory, storage, networking, and VM configuration. Based on a bottoms-up approach looking at some representative maching learning applications.
Tweet Risk has always been top of mind for financial services leaders. What’s different today is their urgency to combat an increasing number of cybercrimes at once. Especially the uptick in fraud. No sector has been more regularly targeted during the pandemic than the financial sector, which is why I recommend reading the “Modern Bank Heist” report detailing how cybercriminals are evolving. Financial … Continued The post Modern Data Strategies: Using AI/ML to Fight Banking Fraud appeared…Read More
Analogous to the role of the software-development lifecycle (SDLC), the machine learning model-development lifecycle (MDLC) guides the activities of ML model development from inception through retirement. In this article, we outline the key phases of the MDLC — including data ingestion, […]