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Virtual Machine Configuration GPU (Nvidia)

This article will introduce the prerequisites for configuring GPU when creating a virtual machine.

The key point of configuring GPU for virtual machines is to configure the GPU Operator so that different software components can be deployed on working nodes depending on the GPU workloads configured on these nodes. Taking the following three nodes as examples:

  • The controller-node-1 node is configured to run containers.
  • The work-node-1 node is configured to run virtual machines with direct GPUs.
  • The work-node-2 node is configured to run virtual machines with virtual vGPUs.

Assumptions, Limitations, and Dependencies

Working nodes can run GPU-accelerated containers, virtual machines with direct GPUs, or virtual machines with vGPUs, but not a combination of any.

  1. Working nodes can run GPU-accelerated containers, virtual machines with direct GPUs, or virtual machines with vGPUs separately, without supporting any combination forms.
  2. Cluster administrators or developers need to understand the cluster situation in advance and correctly label the nodes to indicate the type of GPU workload they will run.
  3. The working nodes running virtual machines with direct GPUs or vGPUs are assumed to be bare metal. If the working nodes are virtual machines, the GPU direct pass-through feature needs to be enabled on the virtual machine platform. Please consult the virtual machine platform provider.
  4. Nvidia MIG vGPU is not supported.
  5. The GPU Operator will not automatically install GPU drivers in virtual machines.

Enable IOMMU

To enable the GPU direct pass-through feature, the cluster nodes need to enable IOMMU. Please refer to How to Enable IOMMU. If your cluster is running on a virtual machine, consult your virtual machine platform provider.

Build vGPU Manager Image

Note: Building a vGPU Manager image is only required when using NVIDIA vGPUs. If you plan to use only GPU direct pass-through, skip this section.

The following are the steps to build the vGPU Manager image and push it to the image repository:

  1. Download the vGPU software from the NVIDIA Licensing Portal.

    • Log in to the NVIDIA Licensing Portal and go to the Software Downloads page.
    • The NVIDIA vGPU software is located in the Driver downloads tab on the Software Downloads page.
    • Select VGPU + Linux in the filter criteria and click Download to get the software package for Linux KVM. Unzip the downloaded file (NVIDIA-Linux-x86_64-<version>-vgpu-kvm.run).

    Download vGPU Software

  2. Clone the container-images/driver repository in the terminal

    git clone https://gitlab.com/nvidia/container-images/driver cd driver

  3. Switch to the vgpu-manager directory for your operating system

    cd vgpu-manager/<your-os>

  4. Copy the .run file extracted in step 1 to the current directory

    cp <local-driver-download-directory>/*-vgpu-kvm.run ./

  5. Set environment variables

    • PRIVATE_REGISTRY: Name of the private registry to store the driver image.
    • VERSION: Version of NVIDIA vGPU Manager, downloaded from the NVIDIA Software Portal.
    • OS_TAG: Must match the operating system version of the cluster node.
    • CUDA_VERSION: CUDA base image version used to build the driver image.
    export PRIVATE_REGISTRY=my/private/registry VERSION=510.73.06 OS_TAG=ubuntu22.04 CUDA_VERSION=12.2.0

  6. Build the NVIDIA vGPU Manager Image

    docker build \
  --build-arg DRIVER_VERSION=${VERSION} \
  --build-arg CUDA_VERSION=${CUDA_VERSION} \
  -t ${PRIVATE_REGISTRY}``/vgpu-manager``:${VERSION}-${OS_TAG} .

  7. Push the NVIDIA vGPU Manager image to your image repository

    docker push ${PRIVATE_REGISTRY}/vgpu-manager:${VERSION}-${OS_TAG}

Label Cluster Nodes

Go to Container Management , select your working cluster, click Nodes in the action bar, and click Edit Labels to add labels to the nodes. Each node can only have one label.

You can assign the following values to the labels: container, vm-passthrough, and vm-vgpu.

Label

Install Nvidia Operator

  1. Go to Container Management , select your working cluster, click Helm Apps -> Helm Charts , choose and install gpu-operator. You need to modify some fields in the yaml.

    Click to view complete YAML 
    gpu-operator.sandboxWorkloads.enabled=true

// Set the following fields only if you need vGPU
gpu-operator.vgpuManager.enabled=true
gpu-operator.vgpuManager.repository=<your-register-url>      // Image repository address in the "Build vGPU Manager Image" step
gpu-operator.vgpuManager.image=vgpu-manager
gpu-operator.vgpuManager.version=<your-vgpu-manager-version> // VERSION from the "Build vGPU Manager Image" step

// GPU direct pass-through related
gpu-operator.vgpuDeviceManager.enabled=true
gpu-operator.vfioManager.enabled=true
gpu-operator.sandboxDevicePlugin.enabled=true
gpu-operator.sandboxDevicePlugin.version=v1.2.4
gpu-operator.toolkit.version=v1.14.3-ubuntu20.04

  2. Wait for the installation to be successful, as shown in the image below:

    Installation Successful

Install virtnest-agent and Configure CR

  1. Install virtnest-agent, refer to Install virtnest-agent.

  2. Add vGPU and GPU direct pass-through to the Virtnest Kubevirt CR. The following example shows the key yaml after adding vGPU and GPU direct pass-through:

    spec:
  configuration:
    developerConfiguration:
      featureGates:
      - GPU
      - DisableMDEVConfiguration
    # Fill in the information below
    permittedHostDevices:
      mediatedDevices:            # vGPU
      - mdevNameSelector: GRID P4-1Q
        resourceName: nvidia.com /GRID_P4-1Q
    pciHostDevices:             # GPU direct pass-through
    - externalResourceProvider:  true
      pciVendorSelector: 10DE:1BB3
      resourceName: nvidia.com /GP104GL_TESLA_P4

  3. In the kubevirt CR yaml, permittedHostDevices is used to import VM devices, and vGPU should be added in mediatedDevices with the following structure:

    mediatedDevices:          
- mdevNameSelector: GRID P4-1Q          # Device Name
  resourceName: nvidia.com/GRID_P4-1Q   # vGPU information registered by GPU Operator to the node

  4. GPU direct pass-through should be added in pciHostDevices under permittedHostDevices with the following structure:

    pciHostDevices:           
- externalResourceProvider: true           # Do not change by default
  pciVendorSelector: 10DE:1BB3              # Vendor id of the current pci device
  resourceName: nvidia.com/GP104GL_TESLA_P4 # GPU information registered by GPU Operator to the node

  5. Example of obtaining vGPU information (only applicable to vGPU): View node information on a node marked as nvidia.com/gpu.workload.config=vm-vgpu (e.g., work-node-2), and the nvidia.com/GRID_P4-1Q: 8 in Capacity indicates available vGPUs:

    # kubectl describe node work-node-2
Capacity:
  cpu:                                 64
  devices.kubevirt.io/kvm:             1k
  devices.kubevirt.io/tun:             1k
  devices.kubevirt.io/vhost-net:       1k
  ephemeral-storage:                   102626232Ki
  hugepages-1Gi:                       0
  hugepages-2Mi:                       0
  memory:                              264010840Ki
  nvidia.com/GRID_P4-1Q :              8
  pods:                                110
Allocatable:
  cpu:                                  64
  devices.kubevirt.io/kvm:              1k
  devices.kubevirt.io/tun:              1k
  devices.kubevirt.io/vhost-net:        1k
  ephemeral-storage:                    94580335255
  hugepages-1Gi:                        0
  hugepages-2Mi:                        0
  memory:                               263908440Ki
  nvidia.com/GRID_P4-1Q:                8
  pods:                                 110

    So the mdevNameSelector should be "GRID P4-1Q" and the resourceName should be "GRID_P4-1Q".

  6. Obtain GPU direct pass-through information: On a node marked as nvidia.com/gpu.workload.config=vm-passthrough (e.g., work-node-1), view the node information, and nvidia.com/GP104GL_TESLA_P4: 2 in Capacity indicates available vGPUs:

    # kubectl describe node work-node-1
Capacity:
  cpu:                            64
  devices.kubevirt.io/kvm:        1k
  devices.kubevirt.io/tun:        1k
  devices.kubevirt.io/vhost-net:  1k
  ephemeral-storage:              102626232Ki
  hugepages-1Gi:                  0
  hugepages-2Mi:                  0
  memory:                         264010840Ki
  nvidia.com/GP104GL_TESLA_P4:    2
  pods:                           110
Allocatable:
  cpu:                            64
  devices.kubevirt.io/kvm:        1k
  devices.kubevirt.io/tun:        1k
  devices.kubevirt.io/vhost-net:  1k
  ephemeral-storage:              94580335255
  hugepages-1Gi:                  0
  hugepages-2Mi:                  0
  memory:                         263908440Ki
  nvidia.com/GP104GL_TESLA_P4:    2
  pods:                           110

    So the resourceName should be "GRID_P4-1Q". How to obtain the pciVendorSelector? SSH into the target node work-node-1 and use the command "lspci -nnk -d 10de:" to get the Nvidia GPU PCI information, as shown in the image above.

  7. Editing kubevirt CR note: If there are multiple GPUs of the same model, only one needs to be written in the CR, listing each GPU is not necessary.

    # kubectl -n virtnest-system edit kubevirt kubevirt
spec:
  configuration:
    developerConfiguration:
      featureGates:
      - GPU
      - DisableMDEVConfiguration
    # Fill in the information below
    permittedHostDevices:
      mediatedDevices:                    # vGPU
      - mdevNameSelector: GRID P4-1Q
        resourceName: nvidia.com/GRID_P4-1Q
    pciHostDevices:                       # GPU direct pass-through, in the above example, TEESLA P4 has two GPUs, only register one here
      - externalResourceProvider: true
        pciVendorSelector: 10DE:1BB3
        resourceName: nvidia.com/GP104GL_TESLA_P4

Create VM YAML and Use GPU Acceleration

The only difference from a regular virtual machine is adding GPU-related information in the devices section.

Click to view complete YAML 
apiVersion: kubevirt.io/v1
kind: VirtualMachine
metadata:
  name: testvm-gpu1
  namespace: default
spec:
  dataVolumeTemplates:
  - metadata:
      creationTimestamp: null
      name: systemdisk-testvm-gpu1
      namespace: default
    spec:
      pvc:
        accessModes:
        - ReadWriteOnce
        resources:
          requests:
            storage: 10Gi
        storageClassName: www
      source:
        registry:
          url: docker://release-ci.daocloud.io/virtnest/system-images/debian-12-x86_64:v1
runStrategy: Manual
template:
    metadata:
      creationTimestamp: null
    spec:
      domain:
        cpu:
          cores: 1
          sockets: 1
          threads: 1
        devices:
          disks:
          - bootOrder: 1
            disk:
              bus: virtio
            name: systemdisk-testvm-gpu1
          - disk:
              bus: virtio
            name: cloudinitdisk
        gpus:
        - deviceName: nvidia.com/GP104GL_TESLA_P4
            name: gpu-0-0
        - deviceName: nvidia.com/GP104GL_TESLA_P4
          name: gpu-0-1
        interfaces:
        - masquerade: {}
          name: default
      machine:
        type: q35
      resources:
        requests:
          memory: 2Gi
    networks:
    - name: default
      pod: {}
    volumes:
    - dataVolume:
        name: systemdisk-testvm-gpu1
      name: systemdisk-testvm-gpu1
    - cloudInitNoCloud:
        userDataBase64: I2Nsb3VkLWNvbmZpZwpzc2hfcHdhdXRoOiB0cnVlCmRpc2FibGVfcm9vdDogZmFsc2UKY2hwYXNzd2Q6IHsibGlzdCI6ICJyb290OmRhbmdlcm91cyIsIGV4cGlyZTogRmFsc2V9CgoKcnVuY21kOgogIC0gc2VkIC1pICIvI1w/UGVybWl0Um9vdExvZ2luL3MvXi4qJC9QZXJtaXRSb290TG9naW4geWVzL2ciIC9ldGMvc3NoL3NzaGRfY29uZmlnCiAgLSBzeXN0ZW1jdGwgcmVzdGFydCBzc2guc2VydmljZQ==
        name: cloudinitdisk

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