Fix ImportError: Libcudnn.so.6 - Step-by-Step Guide to Resolve 'Cannot Open Shared Object File' Issue

This guide will provide you with a step-by-step solution to resolve the ImportError involving Libcudnn.so.6, which is a common issue faced by developers when using CUDA Deep Neural Network library (cuDNN) on Linux-based systems.

Table of Contents

1. Introduction
2. Prerequisites
3. Step 1: Check if cuDNN is Installed
4. Step 2: Download and Install cuDNN
5. Step 3: Update the Environment Variables
6. Step 4: Test the Installation
7. FAQ
8. Related Links

Introduction

The ImportError involving Libcudnn.so.6 is usually encountered when the cuDNN library is either not installed or not properly configured on your system. This guide will help you resolve the issue by providing step-by-step instructions to check, install, and configure cuDNN library on your system.

Prerequisites

Before proceeding, make sure you have the following:

1. An NVIDIA GPU with CUDA Compute Capability of 3.0 or higher.
2. NVIDIA CUDA Toolkit installed on your system.
3. A valid NVIDIA Developer Account to download cuDNN library.

Step 1: Check if cuDNN is Installed

First, let's check if cuDNN is already installed on your system. You can use the following command to search for the libcudnn.so file:

sudo find /usr -name "libcudnn*"

If the command returns any results, it means cuDNN is already installed on your system. However, if it returns nothing, you will need to download and install cuDNN.

Step 2: Download and Install cuDNN

Log in to your NVIDIA Developer Account and navigate to the cuDNN download page.

Select the appropriate cuDNN version that is compatible with your installed CUDA Toolkit version. For example, if you have CUDA Toolkit 8.0, choose cuDNN v6.x.

Download the cuDNN Library for Linux (either .tar or .deb format).

Extract the downloaded cuDNN archive or install the .deb package:

For .tar file:

tar -xzvf cudnn-<version>-linux-x64-v<version>.tgz

For .deb file:

sudo dpkg -i libcudnn<version>_<_architecture>.deb

Copy the extracted cuDNN files to the appropriate directories:

sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

Step 3: Update the Environment Variables

Update the environment variables to include the cuDNN library paths by adding the following lines to your ~/.bashrc file:

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

After saving the changes to the ~/.bashrc file, run the following command to reload the environment variables:

source ~/.bashrc

Step 4: Test the Installation

To test the cuDNN installation, you can use the NVIDIA cuDNN sample code available in the NVIDIA Deep Learning SDK Documentation. Follow the instructions provided to compile and run the test.

FAQ

Q1: How do I know if my GPU supports cuDNN?

You can verify if your GPU supports cuDNN by checking its CUDA Compute Capability. A list of NVIDIA GPUs and their corresponding CUDA Compute Capability can be found on the NVIDIA CUDA GPUs page.

Q2: How do I know which cuDNN version is compatible with my installed CUDA Toolkit?

You can find the cuDNN version compatibility information in the cuDNN Archive page on the NVIDIA Developer website. Make sure to match the cuDNN version with your installed CUDA Toolkit version.

Q3: How do I uninstall cuDNN from my system?

To uninstall cuDNN, remove the installed cuDNN files using the following commands:

sudo rm /usr/local/cuda/include/cudnn.h
sudo rm /usr/local/cuda/lib64/libcudnn*

Q4: Can I install multiple versions of cuDNN on my system?

Yes, you can install multiple versions of cuDNN on your system. However, you need to manage the environment variables and library paths accordingly to avoid conflicts between the different versions.

Q5: ImportError still persists after following the guide. What should I do?

If the ImportError still persists, double-check the installation steps and make sure you have installed the correct cuDNN version compatible with your installed CUDA Toolkit. Also, verify if the environment variables are set correctly. If the issue still persists, consider posting your problem on forums like NVIDIA Developer Forums or Stack Overflow for further assistance.

Related Links

1. NVIDIA cuDNN Homepage
2. NVIDIA Developer Program
3. NVIDIA CUDA Toolkit Download
4. NVIDIA CUDA GPUs
5. NVIDIA Deep Learning SDK Documentation