How to Enable GPU Acceleration in TensorFlow A Step-by-Step Guide

To enable GPU acceleration in TensorFlow, you need to follow these steps:

1. Install the GPU version of TensorFlow: You can install TensorFlow with GPU support by running the following command in your terminal:

pip install tensorflow-gpu

2. Verify that you have a compatible NVIDIA GPU: TensorFlow requires an NVIDIA GPU with CUDA compute capability 3.0 or higher.
3. Install the NVIDIA GPU drivers: Visit the NVIDIA website and download the appropriate GPU drivers for your system.
4. Install CUDA toolkit and cuDNN library: CUDA is a parallel computing platform that allows you to use NVIDIA GPUs for general-purpose computing. cuDNN is a library that provides GPU-accelerated primitives for deep neural networks. Visit the NVIDIA website to download the CUDA toolkit and cuDNN library that are compatible with your version of TensorFlow.
5. Verify that TensorFlow is using GPU: Once you have installed TensorFlow with GPU support and all the necessary drivers and libraries, you can verify that TensorFlow is using GPU by running the following code:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

This will output a list of available GPUs on your system.

6. Run your TensorFlow code with GPU acceleration: Once you have enabled GPU acceleration in TensorFlow, you can run your TensorFlow code with GPU acceleration by creating a TensorFlow session and specifying the device to use:

import tensorflow as tf

with tf.device('/device:GPU:0'):
    # Your TensorFlow code here

In this example, the code will be executed on the first available GPU. If you have multiple GPUs, you can specify a different device using /device:GPU:n, where n is the index of the GPU you want to use.