Troubleshoot ImportError: Fixing Cannot Open Shared Object File Error

In this guide, we will discuss how to troubleshoot and fix the ImportError: cannot open shared object file: No such file or directory error that you may encounter while running a deep learning project. This error typically occurs when the CuDNN library is not installed properly or the system is unable to locate the required shared libraries. We will provide a step-by-step solution to resolve this issue.

Table of Contents


Before proceeding with the troubleshooting steps, ensure that you have the following prerequisites in place:

  1. NVIDIA GPU with CUDA support
  2. NVIDIA CUDA Toolkit installed
  3. NVIDIA CuDNN library installed

Step 1: Verify CuDNN Installation

First, make sure that the CuDNN library is installed on your system. You can download the appropriate version of CuDNN from the official NVIDIA CuDNN page. Follow the installation guide provided by NVIDIA to install the CuDNN library.

Step 2: Locate

Next, we need to locate the shared library file on your system. You can use the find command to search for the file:

$ find /usr -iname ''

This command will search for the file in the /usr directory and its subdirectories. If the file is found, the command will display the file path, such as /usr/local/cuda/lib64/

If you cannot find the file, you may need to re-install the CuDNN library or check other directories for the file.

Step 3: Update LD_LIBRARY_PATH

Once you have located the file, you need to update the LD_LIBRARY_PATH environment variable to include the directory containing the file. This will allow the system to find the shared library when running your deep learning project.

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

Replace /usr/local/cuda/lib64 with the path to the directory containing the file if it is different on your system.

To make this change permanent, you can add the export command to your ~/.bashrc file:

$ echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64' >> ~/.bashrc

Again, replace /usr/local/cuda/lib64 with the appropriate directory path if necessary.

Step 4: Verify the Solution

After updating the LD_LIBRARY_PATH, restart your terminal or run source ~/.bashrc to apply the changes. Now, try running your deep learning project again. The ImportError: cannot open shared object file: No such file or directory error should be resolved.


1. What is is a shared library file that is part of the NVIDIA CuDNN library. It is required to run deep learning projects that utilize NVIDIA GPUs for computation.

2. What is the LD_LIBRARY_PATH variable?

LD_LIBRARY_PATH is an environment variable that specifies the directories where shared libraries should be searched for when running an executable.

3. How do I check my CUDA and CuDNN versions?

To check your CUDA version, run the nvcc --version command. To check your CuDNN version, you can look for the cudnn.h header file in the /usr/include directory and inspect its contents.

4. Can I have multiple CuDNN versions on my system?

Yes, you can have multiple CuDNN versions installed on your system. However, you should ensure that the LD_LIBRARY_PATH environment variable points to the correct version that you want to use.

5. Can I use CuDNN without an NVIDIA GPU?

No, CuDNN is specifically designed to work with NVIDIA GPUs. If you do not have an NVIDIA GPU, you may consider using other deep learning libraries that do not require GPU acceleration.

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