Why Isn't Kubernetes Detecting My NVIDIA GPU on Docker Desktop with WSL2?

Below is an overview of possible reasons and suggested steps based on Docker and Kubernetes documentation regarding GPU support:

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1. Why is Kubernetes not detecting the GPU?

• Runtime Exposure:
  Docker Desktop with WSL2 supports CUDA containers via the --gpus flag. However, Kubernetes (running on Docker Desktop) depends on exposing GPUs as allocatable resources through the NVIDIA device plugin. If the container runtime isn’t fully configured to pass through the NVIDIA runtime to Kubernetes, the node may not advertise any GPUs.

• Limited Official Support:
  GPU support in Docker Desktop under WSL2 is documented for running GPU-accelerated containers. Extending that support to Kubernetes via the GPU Operator and device plugin may require additional configuration that isn’t automatically provided in a WSL2 setup.

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2. Why does the nvidia-device-plugin fail with "could not load NVML library"?

• Missing NVML in Plugin Environment:
  The error indicates that the plugin container can’t find the NVML library, which is essential for managing NVIDIA GPUs. This can happen if:

  • The NVML library isn’t available in the container’s filesystem because it hasn’t been mounted from the host.
  • The container lacks the required NVIDIA drivers/libraries because the NVIDIA Container Toolkit isn’t correctly propagating these libraries from the host to the container.

• Mount or Configuration Issues:
  In environments like WSL2, special care is needed to ensure that the underlying GPU libraries (including NVML) are visible inside the container where the device plugin runs.

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3. Special configuration needed for WSL2 and the GPU Operator?

• Docker Desktop Settings:
  When running on Windows with WSL2, ensure that Docker Desktop’s settings for WSL integration and GPU support are up to date. The official documentation notes that GPU support on Docker Desktop is available on Windows with the WSL2 backend for CUDA containers—but sometimes additional steps may be necessary for Kubernetes.

• NVIDIA Container Toolkit:
  Verify that the NVIDIA Container Toolkit is installed and configured so that the correct GPU runtime is used (as reflected in your /etc/docker/daemon.json settings). Also, confirm that these settings are propagated to the Kubernetes environment.

• Operator Limitations:
  While the GPU Operator is designed to deploy NVIDIA drivers and the device plugin automatically, its functionality in a WSL2-based Kubernetes may be constrained if the environment doesn’t fully mimic a native Linux GPU setup.

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4. Alternative debugging steps to confirm NVML is correctly installed

• Run a Diagnostic Container:
  Since your CUDA container runs fine, you can manually check that NVML is available by running:

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  docker run --rm --gpus all nvidia/cuda:12.6.2-base-ubuntu22.04 bash -c "ldd \$(which nvidia-smi)"

  Look for an entry for libnvidia-ml.so to confirm that NVML is being linked correctly.

• Inspect the Device Plugin Pod:
  If possible, open a shell in the device plugin container:

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  kubectl exec -it <device-plugin-pod> -n gpu-operator -- bash

  Then run:

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  ldd /usr/local/nvidia/lib64/libnvidia-ml.so

  (Replace the path if your setup uses a different location.) This will show if the library dependencies are met inside the pod.

• Review NVIDIA Container Toolkit Logs:
  Ensure that the NVIDIA Container Toolkit is successfully mounting the necessary drivers and libraries. Often the toolkit’s diagnostic messages can reveal if files are missing or paths are incorrect.

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Summary

Although your host and standalone Docker containers correctly detect and use the GPU, Kubernetes is not exposing it as an allocatable resource because:

• The NVIDIA runtime might not be fully integrated with the Kubernetes side of Docker Desktop on WSL2.
• The device plugin’s error ("could not load NVML library") suggests that the necessary GPU libraries are not available in its container environment.

It can help to review Docker Desktop’s WSL2 and GPU settings and consult updated NVIDIA GPU Operator guidance—bearing in mind that some GPU Operator features may have limitations under WSL2. These steps should help you narrow down whether the issue lies with missing mounts, runtime configuration, or inherent limitations of your environment.

Feel free to ask additional Kubernetes or Docker-related questions if needed!