NVIDIA vComputeServer with NGC Containers Brings GPU Virtualization to AI, Deep Learning and Data Science 

August 26, 2019

NVIDIA now supports server virtualization for AI, deep learning and data science. Anne Hecht, from NVIDIA, covers the details in the following blog:

August 26, 2019 — NVIDIA’s virtual GPU (vGPU) technology, which has already transformed virtual client computing, now supports server virtualization for AI, deep learning and data science.

Previously limited to CPU-only, AI workloads can now be easily deployed on virtualized environments like VMware vSphere with new vComputeServer software and NVIDIA NGC. Through our partnership with VMware, this architecture will help organizations to seamlessly migrate AI workloads on GPUs between customer data centers and VMware Cloud on AWS.

Image courtesy of NVIDIA

vComputeServer gives data center administrators the option to run AI workloads on GPU servers in virtualized environments for improved security, utilization and manageability. IT administrators can use hypervisor virtualization tools like VMware vSphere, including vCenter and vMotion, to manage all their data center applications, including AI applications running on NVIDIA GPUs.

Many companies deploy GPUs in the data center, but GPU-accelerated workloads such as AI training and inferencing run on bare metal. These GPU servers are often isolated, with the need to be managed separately. This limits utilization and flexibility.

With vComputeServer, IT admins can better streamline management of GPU-accelerated virtualized servers while retaining existing workflows and lowering overall operational costs. Compared to CPU-only servers, vComputeServer with four NVIDIA V100 GPUs accelerates deep learning 50x faster, delivering performance near bare metal.

Today’s announcement brings support to VMware vSphere along with existing support for KVM-based hypervisors including Red Hat and Nutanix. This allows admins to use the same management tools for their GPU clusters as they do for the rest of their data center.

Virtual GPUs Boost Performance for Any Workload 

By expanding the vGPU portfolio with NVIDIA vComputeServer, NVIDIA is adding support for data analytics, machine learning, AI, deep learning, HPC and other server workloads. The vGPU portfolio also includes virtual desktop offerings — NVIDIA GRID Virtual PC and GRID Virtual Apps for knowledge workers and Quadro Virtual Data Center Workstation for professional graphics.

NVIDIA vComputerServer provides features like GPU sharing, so multiple virtual machines can be powered by a single GPU, and GPU aggregation, so one or multiple GPUs can power a virtual machine. This results in maximized utilization and affordability.

Features of vComputeServer include:

  • GPU Performance: Up to 50x faster deep learning training than CPU-only, similar performance to running GPU on bare metal.
  • Advanced compute: Error correcting code and dynamic page retirement prevent against data corruption for high-accuracy workloads.
  • Live migration: GPU-enabled virtual machines can be migrated with minimal disruption or downtime.
  • Increased security: Enterprises can extend security benefits of server virtualization to GPU clusters.
  • Multi-tenant isolation: Workloads can be isolated to securely support multiple users on a single infrastructure.
  • Management and monitoringAdmins can use the same hypervisor virtualization tools to manage GPU servers, with visibility at the host, virtual machine and app level.
  • Broad Range of Supported GPUs: vComputeServer is supported on NVIDIA T4 or V100 GPUs, as well as Quadro RTX 8000 and 6000 GPUs, and prior generations of Pascal-architecture P40, P100 and P60 GPUs.

NVIDIA NGC Adds Support for VMware vSphere

NVIDIA NGC, our hub for GPU-optimized software for deep learning, machine learning and HPC, offers over 150 containers, pre-trained models, training scripts and workflows to accelerate AI from concept to production, including RAPIDS, our CUDA-accelerated data science software.

RAPIDS offers a range of open-source libraries to accelerate the entire data science pipeline, including data loading, ETL, model training and inference. This enables data scientists to get their work done more quickly and significantly expands the type of models they’re able to create.

All NGC software can be deployed on virtualized environments like VMware vSphere with vComputeServer.

IT administrators can use hypervisor virtualization tools like VMware vSphere to manage all their NGC containers in VMs running on NVIDIA GPUs.

In addition, NVIDIA helps IT roll out GPU servers faster in production with validated NGC-Ready servers. And enterprise-grade support provides users and administrators with direct access to NVIDIA’s experts for NGC software, minimizing risk and improving productivity.


Source: Anne Hecht, NVIDIA

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