Have you spent a fortune on a high-end system with top-of-the-line graphics cards just to train your deep neural network, but don't know if the GPU is being utilized? Perhaps you were under the impression that simply having a GPU in your system automatically accelerates your deep learning training without any additional setup or configuration. Unfortunately, however, this is not the case. In order to fully utilize the power of the GPU and accelerate deep neural network training, you need to have CUDA and cuDNN properly set up on your system.
If you've been trying to install CUDA and cuDNN to speed up your deep learning training, but found the process overwhelming or confusing, don't worry—you're not alone. Getting these libraries up and running can be a daunting task, especially if you're new to deep learning and GPU acceleration. However, the benefit of using a GPU to accelerate the training process is worth the effort.
In this blog post, I'll walk you through the installation process step by step to make sure everything is set up correctly. After reading this article, you'll be able to unleash the full potential of your GPU and accelerate deep neural network training. I'm going to break this process down into the following parts:
Step 1: Check system compatibility with CUDA and cuDNN
Step 2: Set up a Python virtual environment and install TensorFlow and TensorFlow-GPU
Step 3: Install Microsoft Visual C++ compiler for Python.
Step 4: Install the CUDA toolkit for the GPU.
Step 5: Install the cuDNN library.
Step 1: Ensure Hardware Compatibility
Before starting to install the CUDA and cuDNN libraries, it is important to ensure that your hardware is compatible with these software libraries. To avoid any potential compatibility issues or errors during installation, please