Why is Kubernetes on Docker Desktop with WSL2 Not Detecting my NVIDIA GPU After Setting Up the GPU Operator and Device Plugin?

Below are some potential causes and debugging steps to consider:

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1. Why is Kubernetes not detecting the GPU?

• Device Plugin Registration:
  Kubernetes relies on the device plugin to “advertise” GPU resources to the scheduler. If the NVIDIA device plugin isn’t successfully communicating with the GPU driver, the new custom resource (for example, nvidia.com/gpu) won’t be registered.

• NVML Availability:
  The error you see in the logs (“NVML not found”) means that the device plugin isn’t able to load the NVIDIA Management Library (NVML) from within its container. Without NVML, the plugin cannot query or expose GPU status, so the node won’t report GPU capacity.

• Container Runtime Configuration:
  Although you verified that nvidia-smi works and a CUDA container can access the GPU, the Kubernetes workloads run as pods may not be using the correct runtime settings. The device plugin container itself must have access to the NVIDIA libraries that were installed on the host.

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

• Library Mount/Availability:
  The error typically occurs because the container does not have the NVML library available. The NVIDIA device plugin expects the host’s GPU libraries (including NVML) to be accessible inside the container. On WSL2, if the driver installation or library mount paths differ from a standard Linux host, the plugin may not find the library.

• Runtime Arguments:
  Ensure that any volume mounts or device mappings required to expose the GPU driver libraries to the container are set. For example, verify that paths like /usr/lib/x86_64-linux-gnu/libnvidia-ml.so.* are being mounted into the device plugin container.

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3. Is there a special configuration needed for WSL2 to work with Kubernetes GPU Operator?

• WSL2 Considerations:
  When running Kubernetes via Docker Desktop on WSL2, there are a few subtleties:

  • Ensure that Docker Desktop is set up with GPU support and that the nvidia-container-toolkit is correctly integrated with the runtime settings.
  • Even though GPU access in standalone Docker containers (as you’ve verified with CUDA images) works, the GPU Operator and corresponding device plugin must be able to access host driver libraries. This might require adjustments in how the NVIDIA runtime and driver paths are made available in the Kubernetes pod specifications.

• Operator Limitations:
  Some of the NVIDIA GPU Operator functionalities may assume a standard Linux host environment. In a WSL2 environment, double-check any operator-specific documentation and issues on the NVIDIA GitHub repositories for WSL2-specific guidance.

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4. Alternative debugging steps to confirm NVML is correctly installed

• Run an Interactive CUDA Container:
  Launch a container with the CUDA image interactively and inspect the availability of NVML. For example:

  BASHCopied
  docker run --rm --gpus all -it nvidia/cuda:12.6.2-base-ubuntu22.04 bash

  Inside the container, use commands like:

  BASHCopied
  ldd $(which nvidia-smi)

  Look for references to libnvidia-ml.so.1 to confirm that NVML is linked.

• Check Host Library Locations:
  On your WSL2 host, verify that the NVML libraries exist in the standard locations. For instance:

  BASHCopied
  locate libnvidia-ml.so

• Review Docker Runtime Settings:
  Confirm that your /etc/docker/daemon.json settings are actually being applied by restarting Docker Desktop after making changes. You might also verify that when Kubernetes launches pods, the proper runtime (using NVIDIA) is being selected.

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Summary

The primary issue appears to be that the NVIDIA device plugin is not able to load NVML inside its container, preventing it from registering GPU resources with Kubernetes. Even though your GPU and CUDA containers work correctly in WSL2, verify that the container runtime for your Kubernetes pods is configured to pass the necessary GPU driver libraries. This may involve adjusting volume mounts or runtime arguments so that the NVML library is available in the device plugin’s environment. If you continue to encounter issues, checking Docker Desktop’s integration with WSL2 and the NVIDIA GPU Operator’s guidance for nonstandard environments could be the next steps.

Feel free to update or check the operator’s documentation and related GitHub issues for any WSL2-specific caveats.