Setting Up TensorFlow GPU without Conda: A Step-by-Step Guide

In this tutorial, we will walk through the steps of setting up TensorFlow on your GPU without using Conda. Note that this tutorial assumes you already have a good understanding of how to install and use Python packages on your computer.

Step 1: Install CUDA Toolkit

First, you will need to install the CUDA Toolkit on your computer. The CUDA Toolkit is a set of libraries and tools that enable GPU-accelerated computing. You can download the CUDA Toolkit from the NVIDIA website (https://developer.nvidia.com/cuda-toolkit).

Once the download is complete, follow the installation instructions provided by NVIDIA. Make sure to select the appropriate options for your operating system and GPU.

Step 2: Install cuDNN

Next, you will need to install the cuDNN library on your computer. cuDNN is a GPU-accelerated library for deep neural networks. You can download cuDNN from the NVIDIA website (https://developer.nvidia.com/cudnn).

After downloading cuDNN, follow the installation instructions provided by NVIDIA. Make sure to select the appropriate options for your operating system and CUDA Toolkit version.

Step 3: Install TensorFlow

Now that you have installed the necessary GPU libraries, you can install TensorFlow. To do this, you will need to open a terminal window and use pip to install TensorFlow. Run the following command:

pip install tensorflow-gpu

This command will download and install the TensorFlow package for GPU support on your computer.

Step 4: Verify Installation

To verify that TensorFlow is using your GPU for computations, you can run the following Python code snippet:

import tensorflow as tf

print(tf.config.list_physical_devices('GPU'))

If the output of this code includes information about your GPU, then TensorFlow is successfully using your GPU for computations.

Step 5: Test Installation

To further test the installation, you can run a simple TensorFlow program that performs some computations using your GPU. Here is an example of a simple TensorFlow program that performs matrix multiplication:

import tensorflow as tf

x = tf.constant([[1.0, 2.0], [3.0, 4.0]])
y = tf.constant([[5.0, 6.0], [7.0, 8.0]])

z = tf.matmul(x, y)

print(z)

If the output of this program shows the result of the matrix multiplication, then TensorFlow is successfully using your GPU for computations.

That’s it! You have successfully set up TensorFlow to use your GPU without using Conda. Now you can start using TensorFlow for deep learning projects that require GPU acceleration.