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Container GPU setup

Vespa supports using GPUs to evaluate ONNX models, as part of its stateless model evaluation feature. When running Vespa inside a container engine such as Docker or Podman, special configuration is required to make GPU(s) available inside the container.

The following guide explains how to do this for Nvidia GPUs, using Podman on RHEL8. This should also work on plain Rocky Linux 8.8 and AlmaLinux 8.8 on x86_64. For other platforms and container engines, see the Nvidia container toolkit installation guide. Commands below need to run as root (use sudo bash first).

Run a script

Fetch and run our script for RHEL8 / x86_64 and run it as follows:

sudo dnf -y install wget
wget https://raw.githubusercontent.com/vespa-engine/docker-image/master/experimental/gpu-setup-rhel8-x86.sh
sh gpu-setup-rhel8-x86.sh

This will follow the steps below and check if a sample application is able to utilise the GPU. For more details see the steps below.

Configuration steps

  1. Check that SELinux is disabled with getenforce; edit /etc/selinux/config and reboot if necessary. To temporarily avoid SELinux interfering, it's possible to run setenforce Permissive instead.

  2. Ensure that Nvidia drivers are installed on your host where you want to run the vespaengine/vespa container image. On RHEL 8 this can be done as follows:

    dnf config-manager \
    dnf module install -y --enablerepo cuda-rhel8-x86_64 nvidia-driver:530
    ls -ld /dev/nvidia*

    You should have (at least) these devices listed after running the above commands:

    crw-rw-rw-. 1 root root 195,   0 Aug 16 11:24 /dev/nvidia0
    crw-rw-rw-. 1 root root 195, 255 Aug 16 11:24 /dev/nvidiactl
    crw-rw-rw-. 1 root root 238,   0 Aug 16 11:24 /dev/nvidia-uvm
    crw-rw-rw-. 1 root root 238,   1 Aug 16 11:24 /dev/nvidia-uvm-tools

    See Device Node Verification in the CUDA installation guide for more details.

  3. Install nvidia-container-toolkit. This grants the container engine access to your GPU device(s). On RHEL 8 this can be done as follows:

    dnf config-manager \
    dnf install -y --enablerepo libnvidia-container nvidia-container-toolkit
  4. Generate a "Container Device Interface" config:

    nvidia-ctk cdi generate --device-name-strategy=type-index --format=json --output /etc/cdi/nvidia.json
  5. Verify that the GPU device is exposed to the container:

    podman run --rm -it --device nvidia.com/gpu=all docker.io/nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi

    This should print details about your GPU(s) if everything is configured correctly.

  6. Start the Vespa container with the --device option:

    podman run --detach --name vespa --hostname vespa-container \
      --publish 8080:8080 --publish 19071:19071 \
      --device nvidia.com/gpu=all \
  7. The vespaengine/vespa image does not currently include the necessary CUDA libraries by default, due to their large size. These libraries must be installed inside the container manually:
    podman exec -u 0 -it vespa /bin/bash
    dnf -y install dnf-plugins-core
    dnf config-manager \
      --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
    dnf -y install vespa-onnxruntime-cuda

    Instead of the above installation of vespa-onnxruntime-cuda inside the running container, you might want to build your own container image using the following Dockerfile as it avoids having to run the container image with install privileges.

    FROM vespaengine/vespa
    USER root
    RUN dnf -y install 'dnf-command(config-manager)'
    RUN dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
    RUN dnf -y install $(rpm -q --queryformat '%{NAME}-cuda-%{VERSION}' vespa-onnxruntime)
    USER vespa

    Then instead run with your container image name:

    podman run --detach --name vespa --hostname vespa-container \
      --publish 8080:8080 --publish 19071:19071 \
      --device nvidia.com/gpu=all \
  8. All Nvidia GPUs on the host should now be available inside the container, with devices exposed at /dev/nvidiaN. See stateless model evaluation for how to configure the ONNX runtime to use a GPU for computation. Similar for embedding models using GPU, see embedder onnx reference.