Fix ImportError: - Resolving Cannot Open Shared Object File Error in No Time

The ImportError: error is a common issue faced by developers when working with CUDA and cuDNN libraries. This error occurs when the shared object file cannot be found or accessed by the system. To resolve this issue, we will walk you through a step-by-step process to fix the error and prevent it from occurring in the future.

Table of Contents

Step 1: Verify CUDA and cuDNN Installation

Before we proceed to fix the error, it is essential to confirm that both CUDA and cuDNN are installed correctly on your system. You can check this by running the following commands:

nvcc --version
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

These commands will display the version information for both CUDA and cuDNN. If you see the version details printed on the terminal, it indicates that the installation is correct.

Step 2: Update the LD_LIBRARY_PATH Environment Variable

The ImportError: error usually occurs when the system is unable to locate the shared object file. To fix this, you need to update the LD_LIBRARY_PATH environment variable with the correct path to the CUDA library.

To update the LD_LIBRARY_PATH variable, add the following lines to your ~/.bashrc file (replace /usr/local/cuda with the correct path to your CUDA installation if it's different):

export CUDA_HOME=/usr/local/cuda

Once you have added these lines, save and close the file. Then, run the following command to reload the ~/.bashrc file and update the environment variables:

source ~/.bashrc

Step 3: Verify the Updated LD_LIBRARY_PATH Variable

To ensure that the LD_LIBRARY_PATH variable has been updated correctly, run the following command:


This command should display the updated path, including the CUDA library path. If you see the correct path, it means that the environment variable has been updated successfully.

Step 4: Reinstall CUDA and cuDNN (Optional)

If you have followed the steps above and still encounter the ImportError: error, it is recommended to reinstall both CUDA and cuDNN on your system. You can refer to the official installation guides for CUDA and cuDNN to perform a clean installation.


Q1: What is the purpose of the file? is a shared object file that is part of the NVIDIA cuBLAS library. The cuBLAS library provides GPU-accelerated linear algebra operations and is commonly used in deep learning and scientific computing applications.

Q2: Can I resolve the ImportError: error by manually copying the file to the required location?

While manually copying the file to the required location may temporarily fix the error, it is not a recommended approach. Updating the LD_LIBRARY_PATH environment variable as described in Step 2 ensures that the system can access all required shared object files, preventing potential errors in the future.

Q3: How do I fix the ImportError: error on a Windows system?

On Windows, the equivalent of the LD_LIBRARY_PATH environment variable is the PATH variable. To fix the error, you need to update the PATH variable with the correct path to the CUDA library. You can do this by following these steps:

  1. Right-click on 'My Computer' or 'This PC' and select 'Properties'.
  2. Click on 'Advanced system settings'.
  3. Click on the 'Environment Variables' button.
  4. Under 'System variables', find the 'Path' variable and click on 'Edit'.
  5. Add the path to the CUDA library (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64) to the 'Path' variable.
  6. Click 'OK' to save the changes.

Q4: I have multiple versions of CUDA installed on my system. How do I ensure that the correct version is being used?

To use a specific version of CUDA, update the CUDA_HOME and LD_LIBRARY_PATH environment variables in your ~/.bashrc file with the appropriate paths to the desired version. For example:

export CUDA_HOME=/usr/local/cuda-10.0

Q5: Can I use the cuBLAS library without installing the entire CUDA toolkit?

The cuBLAS library is a part of the CUDA toolkit, and it is recommended to install the entire toolkit to ensure compatibility and avoid potential issues. However, if you only need the cuBLAS library, you can try installing it separately using the NVIDIA cuBLAS Debian package or the NVIDIA cuBLAS RPM package. Keep in mind that this approach may not be compatible with all systems and applications.

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