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After upgrading my Ubuntu from 18 to 22, I was left with no Nvidia drivers on my computer. When I type nvidia-smi and nvcc -v there was no good output (see the image below). So, I planned to share my method for setting up GPU drivers on my computer with the world. If your situation is the same, then follow along.
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In the last blog “How to use TensorFlow with GPU on Windows for minimal tasks — in the most simple way(2024)” I discussed how to use Microsoft TensorFlow-DirectML Plugin to make GPU available to TensorFlow(TF) in Windows, it is good for lightweight programming tasks that requiring GPU, but not good for programs that take too much graphics memory and requires heavy loading(as these frameworks need drivers and applications like CUDA and cuDNN to allocate memory for processing the data and task). So in this blog, we are going to deal with downloading and installing the correct versions of TensorFlow, CUDA, cuDNN, Visual Studio Integration, and other driver files to make GPU accessible to TensorFlow, for doing heavy tasks like Video Processing, High-Resolution Image Processing, and Deep Learning*.
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Accelerating machine learning code using your system’s GPU will make the code run much faster and save a lot of time. In this blog we are going to do a quick setup to make TensorFlow access our PC/Laptop’s GPU, just like installing 3–4 libraries in python using pip.