Installing the NVIDIA driver for your GPU

Installing the correct NVIDIA driver is incredibly important. A key component to all of these implementations is the usage of CUDA in TensorFlow. NVIDIA has this description for the CUDA library:

"CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs." (Source: https://developer.nvidia.com/cuda-zone).

Using CUDA, TensorFlow can achieve drastic speedups in terms of processing power. In order to make this happen, we need to have a certain type of GPU and driver installed on the host machine.

So, let's start installing the things that we require.

In this section, a recommended driver will be specified and a few options for installation will be proposed. It's hard to ensure that the installation will be the same for each developer because the installation can vary for each machine it's installed on. Instead, we'll show some methods on how to get it done but will rely on the reader to figure out the nitty-gritty for their application.

You can run the nvidia-smi command to know which version of driver is installed on your system.

The following is an example of the nvidia-smi command:

The output of nvidia-smi will show your GPU, any processes you have running, and the current driver version installed
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