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Version: 1.18

Configure GPU support

Contrast supports running GPU workloads inside the confidential computing environment.

Applicability​

This step is optional and only necessary if your application includes a GPU workload (for example an AI model) that should run confidentially.

warning

Currently, confidential GPU workloads are supported only on bare-metal systems with AMD SEV-SNP. They're not supported on bare-metal systems using Intel TDX.

GPUs without Confidential Computing support can't be used with Contrast.

See the section on Supported GPU hardware for more information.

Prerequisites​

  1. Set up cluster
  2. Configure for GPU usage
  3. Install CLI
  4. Deploy the Contrast runtime
  5. Add Coordinator to resources
  6. Prepare deployment files

How-to​

If the cluster is configured for GPU usage, pods can use GPUs by adding an extended resource limit to the pod definition.

apiVersion: v1
kind: Pod
metadata:
  name: gpu-demo
spec:
  runtimeClassName: contrast-cc
  containers:
  - name: gpu-demo
    image: ghcr.io/edgelesssys/tensorflow@sha256:73fe35b67dad5fa5ab0824ed7efeb586820317566a705dff76142f8949ffcaff
    resources:
      limits:
        nvidia.com/pgpu: "1"

Contrast automatically detects if a pod requires a GPU and assigns the appropriate GPU-enabled runtime class (for example, contrast-cc-metal-qemu-snp-gpu) during the contrast generate step. For more details on manual runtime class selection, see the multi-runtime class documentation.

If you deployed the NVIDIA GPU operator as described above, the resource name is nvidia.com/pgpu. Otherwise, you need to consult your CDI definitions (in /var/run/cdi or /etc/cdi) to find the appropriate device class.

Finally, the environment variable NVIDIA_VISIBLE_DEVICES can be set to all to grant other containers access to the GPUs directly assigned to other containers in the pod. This can be useful for configuring a GPU in an init container, for example.

note

A pod configured to use GPU support may take a few minutes to come up, as the VM creation and boot procedure needs to do more work compared to a non-GPU pod.