> This page is for Confidential Containers, version v1.0.0. > For other versions, use one of these documentation indexes: > - v1.1.0 (default): https://docs.nvidia.com/confidential-computing/containers/v1.1.0/llms.txt > - v1.0.0: https://docs.nvidia.com/confidential-computing/containers/v1.0.0/llms.txt > - Dev: https://docs.nvidia.com/confidential-computing/containers/dev/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/confidential-computing/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/confidential-computing/_mcp/server. # Run a Sample Workload > As a Kubernetes Cluster Administrator, use this page to verify your installation and run a sample workload. As a [Kubernetes Cluster Administrator](/confidential-computing/containers/v1.0.0/personas#coco-persona-kubernetes-cluster-administrator), use this page to verify your installation and run a sample workload. [Container User](/confidential-computing/containers/v1.0.0/personas#coco-persona-container-user) personas can also run the sample workload to confirm the cluster is ready before deploying applications. Verify your Confidential Container setup by running a basic single-GPU sample workload inside a Confidential Container. This page assumes that you have completed [Prerequisites](/confidential-computing/containers/v1.0.0/prerequisites) and either [Quickstart Install](/confidential-computing/containers/v1.0.0/install-quickstart) or [Detailed Install Guide](/confidential-computing/containers/v1.0.0/confidential-containers-deploy). Your cluster should have `kata-qemu-nvidia-gpu-snp` and `kata-qemu-nvidia-gpu-tdx` runtime classes installed, and GPU Operator operands (including the Confidential Computing Manager, Kata Sandbox Device Plugin, and VFIO Manager) running on your nodes. This page intentionally uses the simplest possible manifest so that you can confirm the deployment end-to-end. It is not a production workload template. For runtime class selection, resource type naming, multi-GPU passthrough, and additional manifest patterns, refer to [Configuring Workloads](/confidential-computing/containers/v1.0.0/configure-workloads). 1. Create a file named `cuda-vectoradd-kata.yaml` with a sample manifest for your system: #### AMD-based system (SNP) ```yaml {7,14} apiVersion: v1 kind: Pod metadata: name: cuda-vectoradd-kata namespace: default spec: runtimeClassName: kata-qemu-nvidia-gpu-snp restartPolicy: Never containers: - name: cuda-vectoradd image: "nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0-ubuntu22.04" resources: limits: nvidia.com/pgpu: "1" # for single GPU passthrough memory: 16Gi ``` #### Intel-based system (TDX) ```yaml {7,14} apiVersion: v1 kind: Pod metadata: name: cuda-vectoradd-kata namespace: default spec: runtimeClassName: kata-qemu-nvidia-gpu-tdx restartPolicy: Never containers: - name: cuda-vectoradd image: "nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0-ubuntu22.04" resources: limits: nvidia.com/pgpu: "1" # for single GPU passthrough memory: 16Gi ``` The following is a brief list of the options available for the manifest: * Runtime class: Use `kata-qemu-nvidia-gpu-snp` on AMD-based systems or `kata-qemu-nvidia-gpu-tdx` on Intel-based systems. * GPU resource type: The sample requests `nvidia.com/pgpu`, which is the default resource name advertised by the NVIDIA Kata Sandbox Device Plugin. If your cluster was installed with the `P_GPU_ALIAS=""` setting, replace it with the model-specific name advertised on your node, for example `nvidia.com/GH100_H200_141GB`. Refer to [Configuring Confidential Container Workloads](/confidential-computing/containers/v1.0.0/configure-workloads) for additional guidance on each option. 1. Create the pod: ```console kubectl apply -f cuda-vectoradd-kata.yaml ``` *Example Output:* ```output pod/cuda-vectoradd-kata created ``` 2. Verify the pod is running: ```console kubectl get pod cuda-vectoradd-kata ``` *Example Output:* ```output NAME READY STATUS RESTARTS AGE cuda-vectoradd-kata 1/1 Running 0 10s ``` The pod could also say `Completed` if the container already completed successfully. If the pod stays `Pending` for more than a few minutes, use the [Troubleshooting](/confidential-computing/containers/v1.0.0/troubleshooting) guide for more details on viewing logs and potential causes for the pod being stuck. 3. View the logs from the pod after the container starts: ```console kubectl logs -n default cuda-vectoradd-kata ``` *Example Output:* ```output [Vector addition of 50000 elements] Copy input data from the host memory to the CUDA device CUDA kernel launch with 196 blocks of 256 threads Copy output data from the CUDA device to the host memory Test PASSED Done ``` The output should include `Test PASSED` if the container completed successfully. This means that your cluster is configured to deploy GPU workloads in the Kata container runtime on nodes configured for Confidential Computing. If you do not see any log output, the pod may still be running. Use the command in the previous step to check the pod status. 4. Optionally, you can verify the sample app was running the Kata container runtime by checking the pod details: ```console kubectl describe pod cuda-vectoradd-kata ``` *Example Output:* ```output Name: cuda-vectoradd-kata Namespace: default Priority: 0 Runtime Class Name: kata-qemu-nvidia-gpu-tdx ... Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal Scheduled 95s default-scheduler Successfully assigned default/cuda-vectoradd-kata to dellr760tdx Normal Pulled 71s kubelet Container image "nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0-ubuntu22.04" already present on machine Normal Created 71s kubelet Created container: cuda-vectoradd Normal Started 68s kubelet Started container cuda-vectoradd ``` The `Runtime Class Name` should match the runtime class used to create the pod and you will see that the pod ran successfully. The example output shows `Runtime Class Name: kata-qemu-nvidia-gpu-tdx`. To clean up, delete the pod: > ```console > kubectl delete -f cuda-vectoradd-kata.yaml > ``` ## Next Steps > **Note** > > You have verified that a GPU workload runs on the confidential runtime. This is the end of the > deployment path covered by this guide. > > Attestation is what cryptographically verifies the TEE and releases secrets to a > production workload. For attestation concepts and a local > connectivity test, see the [Attestation](/confidential-computing/containers/v1.0.0/attestation) quickstart. For production attestation > deployment, refer to the upstream [Confidential Containers NVIDIA attestation guide](https://confidentialcontainers.org/docs/examples/nvidia-nim-confidential-gpu-attestation/). If you’d like to continue to learn about Confidential Container configuraion, review the following pages: * Refer to [Configuring Workloads](/confidential-computing/containers/v1.0.0/configure-workloads) for runtime class selection, resource types, and multi-GPU passthrough manifests. * Refer to [Managing the Confidential Computing Mode](/confidential-computing/containers/v1.0.0/configure-cc-mode) to change the CC mode on GPUs at the cluster or node level.