importerror: libcudart.so.11.0: cannot open shared object file: no such file or directory

HaC 2025

3 min read

·

25-10-2024

120

0

Share

"ImportError: libcudart.so.11.0: cannot open shared object file: no such file or directory" - Solved!

This frustrating error message pops up when your Python application, which uses a CUDA-based library (like TensorFlow or PyTorch), cannot locate the essential libcudart.so.11.0 file. This file is a critical component of the NVIDIA CUDA toolkit, providing the core runtime for CUDA-enabled applications.

Understanding the Error

The error essentially means your system can't find the libcudart.so.11.0 library file, which is necessary for CUDA-enabled applications to function correctly. This can happen for several reasons:

• Missing CUDA installation: You haven't installed the CUDA Toolkit or the specific version required by your libraries.
• Incorrect CUDA version: The CUDA Toolkit version installed on your system doesn't match the version expected by your Python libraries.
• Incorrect environment variables: The LD_LIBRARY_PATH environment variable, which tells the system where to find shared libraries, might not include the CUDA installation directory.
• Incorrect installation paths: The CUDA libraries might be installed in a location different from the default one expected by your Python libraries.

Troubleshooting Steps

Here's a step-by-step guide to resolve the ImportError: libcudart.so.11.0: cannot open shared object file: no such file or directory error, based on insights from various GitHub discussions:

1. Verify CUDA Installation:

• Check if CUDA is installed: Use the command nvidia-smi in your terminal. If it returns information about your GPU, CUDA is likely installed.
• Find the installed CUDA version: Use nvcc -V to check the installed CUDA version. Make sure it matches the version required by your libraries.

2. Set up Environment Variables:

• Check LD_LIBRARY_PATH:
  • Linux: Use echo $LD_LIBRARY_PATH to view the current path.
  • macOS: Use echo $DYLD_LIBRARY_PATH to view the current path.
• Add CUDA path:
  • Linux: Append the CUDA installation directory to LD_LIBRARY_PATH. For example: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64.
  • macOS: Append the CUDA installation directory to DYLD_LIBRARY_PATH. For example: export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/usr/local/cuda/lib.

3. Correct CUDA Version:

• Install the correct version: If your CUDA version doesn't match the requirements of your libraries, download and install the correct version from NVIDIA's website (https://developer.nvidia.com/cuda-downloads).
• Update libraries: If possible, update your Python libraries (like TensorFlow or PyTorch) to ensure they're compatible with the installed CUDA version.

4. Check Installation Paths:

• Verify default paths: If the CUDA libraries were installed in a location other than the default path (/usr/local/cuda/lib64 for Linux, /usr/local/cuda/lib for macOS), ensure your environment variables reflect the correct paths.
• Install libraries in the correct location: If the default paths are used, reinstall your libraries to make sure they're installed in the right location.

5. Additional Tips:

• Restart your terminal: After making changes to your environment variables, restart your terminal to apply the changes.
• Use virtual environments: Employ virtual environments to isolate your project's dependencies and avoid potential conflicts.

Example:

Let's say you're using TensorFlow and the error occurs because of a mismatch between your CUDA installation (version 10.0) and the version expected by TensorFlow (version 11.0). The solution would involve installing CUDA 11.0, updating TensorFlow to be compatible, and ensuring the correct LD_LIBRARY_PATH is set for your system.

Important Notes:

• CUDA installation guide: For detailed instructions on installing the CUDA toolkit, refer to the official NVIDIA documentation: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
• Library documentation: Consult the documentation of your specific libraries (TensorFlow, PyTorch, etc.) for any specific instructions or recommendations related to CUDA compatibility.

By following these steps, you'll be able to resolve the ImportError: libcudart.so.11.0: cannot open shared object file: no such file or directory error and get back to using your CUDA-based applications smoothly.