This optimizes the use of the GPU hardware and it can serve more than one user, reducing costs. A basic level of familiarity with the core concepts in Kubernetes and in GPU Acceleration will be useful to the reader of this article. We first look more closely at pods in Kubernetes and how they relate to a GPU. A pod is the unit of deployment, at the lowest level, in Kubernetes. A pod can have one or more containers within it. The lifetime of the containers within a pod tend to be about the same, although one container may start before the others, as the "init" container. You can deploy higher-level objects like Kubernetes services and deployments that have many pods in them. We focus on pods and their use of GPUs in this article. Given access rights to a Tanzu Kubernetes cluster (TKC) running on the VMware vSphere with Tanzu environment (i.e. a set of host servers running the ESXi hypervisor, managed by VMware vCenter), a user can issue the command:
Enterprises can begin to run trials of their AI projects using VMware vSphere with Tanzu together with Nvidia AI Enterprise software suite, as part of moves by both companies to further simplify AI development and application management. By extending testing to vSphere with Tanzu, Nvidia boasts it will enable developers to run AI workloads on Kubernetes containers within their existing VMware environments. The software suite will run on mainstream Nvidia-certified systems, the company said, noting it would provide a complete software and hardware stack suitable for AI development. "Nvidia has gone and invested in building all of the next-generation cloud application-level components, where you can now take the NGC libraries, which are container-based, and run those in a Kubernetes orchestrated VMware environment, so you're getting the ability now to go and bridge the world of developers and infrastructure," VMware cloud infrastructure business group marketing VP Lee Caswell told media. The move comes off the back of VMware announcing Nvidia AI Enterprise in March.
Enterprises can begin to run trials of their AI projects using VMware vSphere with Tanzu together with Nvidia AI Enterprises software suite, as part of moves by both companies to further simplify AI development and application management. By extending testing to vSphere with Tanzu, Nvidia boasts it will enable developers to run AI workloads on Kubernetes containers within their existing VMware environments. The software suite will run on mainstream Nvidia-certified systems, the company said, noting it would provide a complete software and hardware stack suitable for AI development. "Nvidia has gone and invested in building all of the next-generation cloud application-level components, where you can now take the NGC libraries, which are container-based, and run those in a Kubernetes orchestrated VMware environment, so you're getting the ability now to go and bridge the world of developers and infrastructure," VMware cloud infrastructure business group marketing VP Lee Caswell told media. The move comes off the back of VMware announcing Nvidia AI Enterprise in March.
The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Dell Technologies is rolling out a series of designs and systems that aim to speed up artificial intelligence deployments by using VMware's acquired Bitfusion technology. Two Dell EMC Ready Solutions are based on VMware Validated Designs to combine Dell EMC hardware with VMware Cloud Foundation and AI management Bitfusion tools in VMware vSphere 7. Dell Technologies said that its Dell Dell Technologies is claiming to be among the first IT companies to equip systems to run AI workloads within VMware environments. Ravi Pendekanti, senior vice president of product management and marketing for Dell Technologies server unit, said the new systems are designed to run AI anywhere and take advantage of underutilized GPUs. "GPU instances are being underutilized and that is holding back AI," said Pendekanti.
VMware is developing a cloud service to monitor software in customer deployments and tune it automatically to improve performance. This is Project Magna and its first target is vSAN in hyperconverged infrastructure. It will work like this: customers select their key performance indicator – read or write optimisation or both. Magna examines their vSAN environment and compares it to the KPI average for stored and monitored deployments. If the site is below average, Magna changes it to bring it closer to the average.
SAN FRANCISCO, California--VMware announced today that it has closed its Uhana acquisition, and unveiled the next release of its OpenStack solution. Uhana, which provides artificial intelligence and machine learning optimization for mobile networks, will be particularly useful in 5G radio access networks (RANs) and the buildout of the 5G core. "We see a major change coming with all the radio aspects," said VMware's Gabriele di Piazza, vice president of managed products and solutions for telco NFV and edge cloud. "We invested in Uhana to provide fine-grained streaming analytics, which actually leverage direct feeds from radio networks, and then we can apply AI techniques, such as machine learning, to what we call observe, predict and control your network. "With Uhana, we are getting deep into the network.
If you've ever found yourself wishing you could do all the things you've been able to do with a hypervisor and regular virtual machines but on a GPU cluster – in your own data center or in the cloud – Nvidia and VMware are now saying your wish is about to come true. Monday morning, in conjunction with the start of VMworld in San Francisco, the two companies announced that VMware Cloud on AWS, the VMware-operated cloud service running on bare-metal infrastructure in AWS data centers, will soon feature virtualized GPUs you'll be able to provision and manage using the same vSphere tools you use with regular VM infrastructure. You'll be able to share a single physical GPU among multiple VMs, but you'll also be able to aggregate the power of many GPUs to train a machine-learning model at massive scale, the companies said. Related: VMworld: Look at Acquisitions for Virtualization's Cloud Play The play here is to get VMware into the infrastructure mix for the emerging set of enterprise computing workloads that benefit from GPU acceleration, such as AI and machine learning, as well as more traditional Big Data analytics. Also on Monday, the company announced a broad strategy for tackling the hybrid cloud opportunity, which is essentially to provide a single set of tools for managing all enterprise infrastructure, on premises and/or in any public cloud, in a uniform way.
Nvidia and VMware today announced an enterprise-grade hybrid cloud on AWS that is optimized for machine learning, AI, and data science workflows. The VMware Cloud on AWS with Nvidia is capable of operating from cloud and on-premise servers and will make it easier to migrate VMware vSphere-based applications to the cloud to accelerate high-performance computing or machine learning for research, experimentation, and deployment in production. Today's news comes at the beginning of the VMworld conference in San Francisco and just days after VMware acquired cloud security and app development startups Pivotal and Carbon Black for $5 billion. Nvidia today also introduced vCompute Server for the GPU-accelerated deployment of workloads in virtual environments, including VMware's vSphere, vCenter, and VMware Cloud. The latest virtual GPU offering from Nvidia can make the completion of deep learning training up to 50 times faster than with a CPU alone.