How to Fix ‘could not load dynamic library libcudnn.so.8’ Error in TensorFlow on Ubuntu 20.04

Are you facing the frustrating issue of ‘Could not load dynamic library libcudnn.so.8‘ when running TensorFlow on Ubuntu 20.04? This error can be a significant roadblock in utilizing your GPU for deep learning tasks. The cuDNN library, essential for GPU-accelerated neural network computations, is crucial for optimized performance.

In this article, we will delve into troubleshooting steps to help you resolve this error and get your TensorFlow setup running smoothly.

Resolving TensorFlow libcudnn.so.8 Error on Ubuntu 20.04

When you’re trying to run TensorFlow on Ubuntu 20.04, it’s frustrating when you encounter an error message like “Could not load dynamic library libcudnn.so.8”. This issue can prevent you from harnessing the power of your GPU for deep learning tasks, and it’s essential to resolve it quickly.

The “Could not load dynamic library libcudnn.so.8” error typically occurs when TensorFlow is unable to find or access the cuDNN library, which is a crucial component for GPU-accelerated neural network computations. This library provides optimized implementations of common math operations, such as matrix multiplication and convolution, making it essential for many deep learning applications.

To troubleshoot this issue, you need to ensure that your system has the correct version of cuDNN installed and configured correctly. Here are some steps you can follow:

• Check the version of CUDA and cuDNN installed on your system by running the commands `nvidia-smi` and `nvcc –version`, respectively.
• Verify that the cuDNN library is installed in the correct location. Typically, it should be located in `/usr/lib/x86_64-linux-gnu/` or `/usr/local/cuda/lib64/`.
• If you’re using a virtual environment (venv), ensure that the cuDNN library is installed and accessible within that environment.

If none of these steps resolve the issue, you may need to reinstall cuDNN or update your CUDA installation. Additionally, make sure that your system has the necessary dependencies installed, such as libgcc and libstdc++.

By following these troubleshooting steps, you should be able to resolve the “Could not load dynamic library libcudnn.so.8” error and get back to working with TensorFlow on your Ubuntu 20.04 system.

In conclusion, dealing with the ‘Could not load dynamic library libcudnn.so.8 when running TensorFlow on Ubuntu 20.04’ error requires careful attention to the CUDA and cuDNN installations, library locations, and system dependencies. By verifying the correct setup and configuration, you can overcome this obstacle and continue your deep learning projects seamlessly. Don’t let technical glitches hinder your progress – follow the outlined steps and get back to harnessing the power of TensorFlow for your neural network endeavors.