Creating a Deep Learning Environment with TensorFlow GPU

Creating a Deep Learning Environment with TensorFlow GPU

Hi! In this article, we will talk about how we can create a deep learning environment for our machine. Before starting the article, I will give information about the operating system and graphics processor I use. So let’s start!

Hi! In this article, we will talk about how we can create a deep learning environment for our machine. Before starting the article, I will give information about the operating system and graphics processor I use. So let’s start!

The machine I’m working with uses Windows 10 Pro as the operating system. If you want to learn the features of your graphics card, you can learn the features of your graphics card by typing dxdiag instead of searching.

⚙️OS: Windows 10 Pro

⚙️CUDA Toolkit: 10

⚙️cuDNN: 7.4

⚙️TensorFlow GPU: 1.14.0

⚙️Keras: 2.2.5

Step 1: Get Graphics Card Information

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If you wish, you can learn this step in a short time by writing the code below to the terminal.

wmic path win32_VideoController get name

Then we need to go to NVIDIA’s website and make the necessary downloads. On the output, you will see the name of your graphics cards as in the next image.

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artificial-intelligence tensorflow tensorflow-gpu deep-learning

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