Error: /usr/local/cuda/lib64/libcudnn_cnn_train.so.8: Undefined Symbol

MIster jashon

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Oct 03, 2024

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Troubleshooting the "error: /usr/local/cuda/lib64/libcudnn_cnn_train.so.8: undefined symbol" Error

This error message usually occurs when you're attempting to run applications that rely on the NVIDIA cuDNN library, specifically the libcudnn_cnn_train.so.8 library, and there's a mismatch or inconsistency in the setup of your CUDA and cuDNN environments. Here's a breakdown of the common causes and solutions for this error.

Understanding the Error

The error message itself indicates that your system is trying to access a specific function or variable within the libcudnn_cnn_train.so.8 library, but that function or variable is not present or correctly defined. This usually points to one of the following problems:

1. Incompatible cuDNN Version: The cuDNN version you're using might not be compatible with the CUDA toolkit version installed on your system. Different CUDA versions require specific versions of cuDNN.
2. Missing Dependencies: The cuDNN library relies on other libraries (such as CUDA libraries) to function correctly. If these dependencies are missing or outdated, the error can occur.
3. Incorrect Installation: The cuDNN installation might not have been performed correctly, leading to missing or corrupted files.
4. Library Path Issues: The environment variable LD_LIBRARY_PATH might not be properly configured to point to the correct directory where cuDNN libraries reside.

Troubleshooting Steps

Here's a step-by-step approach to resolve the "error: /usr/local/cuda/lib64/libcudnn_cnn_train.so.8: undefined symbol" issue:

1. Verify CUDA and cuDNN Versions:

   • Determine your CUDA version:

     nvcc --version
     

   • Determine your cuDNN version:
     • You can typically find this information in the cuDNN installation directory, usually located in /usr/local/cuda.

2. Check Compatibility:

   • Ensure that your cuDNN version is compatible with your CUDA version. Refer to the official NVIDIA cuDNN documentation for compatibility charts.

3. Reinstall cuDNN (if necessary):

   • If you suspect a corrupted or incomplete cuDNN installation, reinstall it:
     • Download the correct cuDNN version: Download the version compatible with your CUDA toolkit from the NVIDIA website.
     • Extract the archive: Unzip the downloaded archive.
     • Copy the cuDNN libraries: Copy the appropriate cuDNN libraries (e.g., libcudnn_cnn_train.so.8) to the correct location. This location is usually /usr/local/cuda/lib64 or /usr/local/cuda/lib depending on your system architecture.
     • Set Environment Variables: You might need to set environment variables to point to the cuDNN directory. This usually involves adding the following to your shell's configuration (e.g., .bashrc or .zshrc):

       export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
       

4. Verify Library Path:

   • Make sure the LD_LIBRARY_PATH environment variable correctly points to the directory containing the cuDNN libraries.
   • Check if the LD_LIBRARY_PATH is properly set in your shell environment.

5. Rebuild Your Application:

   • Once you've addressed potential issues with your CUDA and cuDNN setup, recompile your application to ensure that it links against the correct cuDNN libraries.

Example Scenario

Imagine you're using a deep learning framework (like TensorFlow) that relies on cuDNN, and you've installed CUDA 11.0 but are using cuDNN 7.6. This mismatch could be the root cause of the error. Reinstall the appropriate cuDNN version (7.6.5 or later for CUDA 11.0), ensuring it's compatible with your CUDA setup.

Conclusion

The "error: /usr/local/cuda/lib64/libcudnn_cnn_train.so.8: undefined symbol" error often signals inconsistencies or incompatibilities in your CUDA and cuDNN environment. By meticulously checking compatibility, verifying your installation, and ensuring proper library path configurations, you can resolve this error and enable your applications to leverage the power of NVIDIA's cuDNN library.