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Running AI workloads is coming to a virtual machine near you, powered by GPUs and Kubernetes

ZDNet

Run:AI offers a virtualization layer for AI, aiming to facilitate AI infrastructure. It's seeing lots of traction and just raised a $75M Series C funding round. Here's how the evolution of the AI landscape has shaped its growth.


How Virtual GPUs Enhance Sharing in Kubernetes for Machine Learning on VMware vSphere

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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:


Determining GPU Memory for Machine Learning Applications on VMware vSphere with Tanzu

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VMware vSphere with Tanzu provides users with the ability to easily construct a Kubernetes cluster on demand for model development/test or deployment work in machine learning applications. These on-demand clusters are called Tanzu Kubernetes clusters (TKC) and their participating nodes, just like VMs, can be sized as required using a YAML specification. In a TKC running on vSphere with Tanzu, each Kubernetes node is implemented as a virtual machine. Kubernetes pods are scheduled onto these nodes or VMs by the Kubernetes scheduler running in the Control Plane VMs in that cluster. To accelerate machine learning training or inference code, one or more of these pods require a GPU or virtual GPU (vGPU) to be associated with them.


Nvidia adds container support into AI Enterprise suite

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Nvidia has rolled out the latest version of its AI Enterprise suite for GPU-accelerated workloads, adding integration for VMware's vSphere with Tanzu to enable organisations to run workloads in both containers and inside virtual machines. Available now, Nvidia AI Enterprise 1.1 is an updated release of the suite that GPUzilla delivered last year in collaboration with VMware. It is essentially a collection of enterprise-grade AI tools and frameworks certified and supported by Nvidia to help organisations develop and operate a range of AI applications. That's so long as those organisations are running VMware, of course, which a great many enterprises still use in order to manage virtual machines across their environment, but many also do not. However, as noted by Gary Chen, research director for Software Defined Compute at IDC, deploying AI workloads is a complex task requiring orchestration across many layers of infrastructure.


Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications

arXiv.org Artificial Intelligence

Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace. While many research works are being undertaken to enhance Deep Neural Networks (DNN) -- the centerpiece of DL applications -- practical deployment challenges of these applications in the Cloud and Edge systems, and their impact on the usability of the applications have not been sufficiently investigated. In particular, the impact of deploying different virtualization platforms, offered by the Cloud and Edge, on the usability of DL applications (in terms of the End-to-End (E2E) inference time) has remained an open question. Importantly, resource elasticity (by means of scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have shown to be influential on the virtualization overhead. Accordingly, the goal of this research is to study the impact of these potentially decisive deployment options on the E2E performance, thus, usability of the DL applications. To that end, we measure the impact of four popular execution platforms (namely, bare-metal, virtual machine (VM), container, and container in VM) on the E2E inference time of four types of DL applications, upon changing processor configuration (scale-up, CPU pinning) and processor types. This study reveals a set of interesting and sometimes counter-intuitive findings that can be used as best practices by Cloud solution architects to efficiently deploy DL applications in various systems. The notable finding is that the solution architects must be aware of the DL application characteristics, particularly, their pre- and post-processing requirements, to be able to optimally choose and configure an execution platform, determine the use of GPU, and decide the efficient scale-up range.


Nvidia and VMware team up to help enterprises scale up AI development

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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.


Nvidia and VMware team up to help enterprises scale up AI development

ZDNet

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.


Vizio M51ax-J6 review: This 5.1-channel soundbar boasts virtualized Dolby Atmos and DTS:X audio

PCWorld

Vizio's latest mid-range soundbar caters to bargain hunters who want immersive Dolby Atmos and DTS:X audio without breaking the bank, and without the need for upfiring drivers that require flat ceilings at just the right height. Given that it uses virtualization instead of physical drivers to deliver height cues, it comes as no surprise that the 5.1-channel M51ax-J6 can't match the precision of pricier soundbars that do have upfiring drivers. You'll also have to do without Wi-Fi connectivity and audio casting support, although that's no big surprise given the M51ax-J6's price tag and feature set. Despite those shortcomings, the 5.1-channel M51ax-J6 offers plenty of value, including rich, punchy, and exciting sound, eARC support, and a dedicated audio jack for connecting a smart speaker, such as an Amazon Echo or Echo Dot. This review is part of TechHive's coverage of the best soundbars.


The Top 100 Software Companies of 2021

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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 ...


Steven Fleischman on LinkedIn: Industry Standard AI infrastructure powered by HPE, VMware, and NVIDIA

#artificialintelligence

AI technology used to be limited to advanced research teams. It is now a key capability for many businesses to improve sales and product quality, provide deep personalisation and new interfaces, and to improve safety and reduce risk. AI is materially changing how we interact with and benefit from technology. Having ready access to and consistent operational control over AI infrastructure is a game changer that democratizes AI for enterprises and opens access to many new use cases.