sharing gears.pngIn Part 1 of “Share the GPU Love” we covered the need for improving the utilization of GPU accelerators and how a relatively simple technology like VMware DirectPath I/O together with some sharing processes could be a starting point.  As with most things in technology, some additional technology, and knowledge you can achieve high goals beyond just the basics.  In this article, we are going to introduce another technology for managing GPU-as-a-service – NVIDIA GRID 9.0.

 

Before we jump to this next technology, let’s review some of the limitations of using DirectPath I/O for virtual machine access to physical PCI functions. The online documentation for VMware DirectPath I/O has a complete list of features that are unavailable for virtual machines configured with DirectPath I/O.  Some of the most important ones are:

 

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  • Fault tolerance
  • High availability
  • Snapshots
  • Hot adding and removing of virtual devices


The technique of “passing through” host hardware to a virtual machine (VM) is simple but doesn’t leverage many of the virtues of true hardware virtualization.  NVIDIA delivers software to virtualize GPUs in the data center for years.  The primary use case has been Virtual Desktop Infrastructure (VDI)  using vGPUs.  The current release - NVIDIA vGPU Software 9 adds the vComputeServer vGPU capability for supporting artificial intelligence, deep learning, and high-performance computing workloads.  The rest of this article will cover using vGPU for machine learning in a VMware ESXi environment.


We want to compare the setup and features of this latest NVIIDA software version, so we worked on adding the vComputeServer to our PowerEdge ESXi that we used for the DirectPath I/O research in our first blog [add blog here].  Our NVIDIA Turing architecture T4 GPUs are on the list of supported devices, so we can check that box and our ESXi version is compatible.  The NVIDIA vGPU software documentation for VMware vSphere has an exhaustive list of requirements and compatibility notes.


You’ll have to put your host into maintenance mode during installation and then reboot after the install of the VIB completes.  When the ESXi host is back online you can use the now familiar nvidia-smi command with no parameters and see a list of all available GPUs that indicates you are ready to proceed.


We configured two of our T4 GPUs for vGPU use and setup the required licenses.  Then we followed the same approach that we used for DirectPath I/O to build out VM templates with everything that is common to all developments and use those to create the developer specific VMs – one with all Python tools and another with R tools.  NVIDIA vGPU software supports only 64-bit guest operating systems. No 32-bit guest operating systems are supported.  You should only use a guest OS release that is supported by both for NVIDIA vGPU software and by VMware.  NVIDIA will not be able to support guest OS releases that are not supported by your virtualization software.


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Now that we have both a DirectPath I/O enabled setup and the NVIDIA vGPU environment let’s compare the user experience.  First, starting with vSphere 6.7 U1 release, vMotion with vGPU and suspend and resume with vGPU are supported on suitable GPUs. Always check the NVIDIA Virutual GPU Software Documentation for all the latest details.  vSphere 6.7 only supports suspend and resume with vGPU. vMotion with vGPU is not supported on release 6.7. [double check this because vMotion is supported I just cant remember what version and update number it is] 


vMotion can be extremely valuable for data scientists doing long running training jobs that you don’t get with DirectPath I/O and suspend/resume of vGPU enabled VMs creates opportunities to increase the return from your GPU investments by enabling scenarios with data science model training running at night and interactive graphics intensive applications running during the day utilizing the same pool of GPUs.  Organizations with workers spread across time zones may also find that suspend/resume of vGPU enabled VMs to be useful.


There is still a lot of work that we want to do in our lab including capturing some informational videos that will highlight some of the concepts we have been talking about in this last two articles.  We are also starting to build out some VMs configured with Docker so we can look at using our vGPUs with NVIDIA GPU Cloud (GCP) deep learning training and inferencing containers.  Our goal is to get more folks setting up a sandbox environment using these articles along with the NVIDIA and VMware links we have provided.  We want to hear about your experience working with vGPUs and VMware.  If you have any questions or comments post them in the feedback section below.

 

Thanks for reading,

Phil Hummel - @GotDisk