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Nvidia GPU Driver Installation

GPU Driver Installation

Two installation methods are provided: manual installation and installation using gpu-operator. Using gpu-operator simplifies the process, so it is recommended.

To use GPUs on Kubernetes, you need to install the relevant drivers and programs. This involves the following steps:

  • Install the GPU physical device driver
  • Install the CUDA toolkit
  • Modify the container runtime
  • Install the device plugin

Manual Installation

  1. Install the physical device driver

    • Add the PPA repository

      add-apt-repository ppa:graphics-drivers/ppa
apt-get update

    • View the available driver versions

      ubuntu-drivers devices

      Alt text

    • Select the appropriate version for installation

    • Recommended version for installation:

      ubuntu-drivers autoinstall

    • Install a specific version:

      apt-get install nvidia-driver-XXX

    • Install CUDA

      # https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu
$ apt-get install cuda

    • After installation, restart the system

      nvidia-smi
nvcc -V

    • Check the driver version

      $ cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module  525.105.17  Tue Mar 28 18:02:59 UTC 2023

  2. Install the cuda toolkit

    • https://github.com/NVIDIA/nvidia-container-toolkit

    • https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

    • Conditions for running nvidia-container-toolkit:

      • Operating system kernel version > 3.10
      • Docker >= 19.03 or use Containerd.
      • NVIDIA GPU architecture above version 3.0
      • NVIDIA Linux drivers >= 418.81.07

    • The following demonstrates how to install using containerd:

      • Set nvidia-container-runtime in the /etc/containerd/config.toml configuration file and restart

            [plugins."io.containerd.grpc.v1.cri".containerd]
    default_runtime_name = "nvidia"
    [plugins."io.containerd.grpc.v1.cri".containerd.runtimes]
    [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options]
            SystemdCgroup = true
    [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
        privileged_without_host_devices = false
        runtime_engine = ""
        runtime_root = ""
        runtime_type = "io.containerd.runc.v1"
        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
            BinaryName = "/usr/bin/nvidia-container-runtime"
            SystemdCgroup = true

      • Set the package repository nvidia-container-toolkit

            $ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && curl -s -L https://nvidia.github.io/libnvidia-container/gpgkey | sudo apt-key add - && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

      • Install:

        sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

  3. Install the device plugin

    kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.0/nvidia-device-plugin.yml

Installation using gpu-operator

Alt text

GPU Operator can perform operations related to GPU underlying dependencies on top of Kubernetes, including GPU drivers, Container ToolKit, automatic reporting of Device Plugin resources, and more. You no longer need to worry about driver installation and other troublesome tasks. In theory, as long as you have a GPU card installed, K8s is installed, and you use GPU Operator, you can utilize all the capabilities.

  • Check if the GPU card is mounted correctly

    $ lspci | grep -i nvidia
1b:00.0 VGA compatible controller: NVIDIA Corporation GP102 [TITAN Xp] (rev a1)

  • Determine the kernel version (this step is important, the nvidia driver image version needs to be compatible with the node's kernel version, including the minor version number)

    To check if the driver version corresponds to the kernel, please visit the following website: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/driver/tags

    $ hostnamectl | grep Kernel
Kernel: Linux 5.15.0-75-generic

  • Install GPU Operator

    $ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia && helm repo update
$ helm install --wait --generate-name \
    -n gpu-operator --create-namespace \
    nvidia/gpu-operator \
    --set driver.version=525-5.15.0-69-generic

Install vGPU Driver

After installing the above two methods, the pod can only use the entire GPU resources, and cannot utilize the vGPU functionality. If you need to use vGPU, you need to search for the nvidia-vgpu module in the Helm repository managed by DCE 5.0 container management and install it.

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