(docker, nvidia-ctk) error while loading shared libraries: libcuda.so.1: cannot open shared object file: No such file or directory

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Summary

The issue at hand is the failure to load shared libraries, specifically libcuda.so.1, when running a Docker container with the NVIDIA Container Toolkit (nvidia-ctk) on a remote server with Redhat 9.1. The container is set up to use an NVIDIA A100 GPU and is based on the nvidia/cuda:13.1.0-devel-ubuntu24.04 image. Despite setting environment variables such as PATH, LD_LIBRARY_PATH, and LIBRARY_PATH to include the location of libcuda.so.1, the container is unable to find the library.

Root Cause

The root cause of the issue is likely due to the fact that the libcuda.so.1 library is not being properly mounted or exposed within the container. This can be caused by:

  • Incorrect or missing volume mounts for the library directory
  • Insufficient permissions for the container to access the library file
  • Version mismatch between the CUDA version used in the container and the version of the library on the host system

Why This Happens in Real Systems

This issue can occur in real systems due to:

  • Inconsistent environment configurations between the host and container
  • Versioning issues between different components of the NVIDIA stack (e.g., driver, CUDA, and container toolkit)
  • Insufficient understanding of how environment variables and library paths are handled within containers

Real-World Impact

The impact of this issue can be significant, including:

  • Failed container runs and inability to execute CUDA workloads
  • Difficulty in debugging and identifying the root cause of the issue
  • Inefficient use of resources, as containers may need to be rebuilt or reconfigured multiple times to resolve the issue

Example or Code

# Example of how to mount the library directory as a volume
docker run -it --runtime=nvidia --gpus "device=1" -v /usr/lib64:/usr/lib64:ro ubuntu nvidia-smi

This command mounts the /usr/lib64 directory from the host system as a read-only volume within the container, allowing the container to access the libcuda.so.1 library.

How Senior Engineers Fix It

Senior engineers can fix this issue by:

  • Verifying the environment configurations and ensuring consistency between the host and container
  • Checking the version compatibility between different components of the NVIDIA stack
  • Using volume mounts to expose the library directory within the container
  • Setting environment variables correctly to include the location of the library

Why Juniors Miss It

Junior engineers may miss this issue due to:

  • Lack of understanding of how environment variables and library paths are handled within containers
  • Insufficient experience with debugging and troubleshooting container-related issues
  • Overlooking the importance of version compatibility and consistency between different components of the NVIDIA stack