# Deep Learning Setup

Easy deep learning setup for my home office 👨‍💻

## Overview

I have tried setting up deep learning on my system quite a few times now. There have been times when I had to format my whole system just because I messed up some step in a process I found online. There are so many different deep learning setups found on the internet right now that it has become too confusing. The best tutorial that I found was the one by Dr Donald Kinghorn1

## Prerequisites

• This tutorial is for deep learning machines with NVIDIA GPUs
• This setup can be done on both Ubuntu (only tested on 18.04) and Windows 10. Since I use both these operating systems in dual boot on my laptop, I have done this setup on both.

#### For Ubuntu machine

• Find out which nvidia driver is needed for your machine and operating system.
• Use nvidia-smi for more information and install the appropriate version of the driver.

#### For Windows 10 machine

• Find out the NVIDIA graphics driver required for your system and install it.

## Setting up

• Install anaconda for your platform
• After complete anaconda setup, create a new conda environment. Let’s call it tf-gpu in our case. Run this command for creating a new environment: conda create --name tf-gpu
• Activate the environment using source activate tf-gpu or through the anaconda navigator
• Install tensorflow-gpu like this: conda install tensorflow-gpu

You can install other packages that you need (opencv, keras, etc) according to your need.

## Testing the setup

Now that everything is set up, let’s test our system. Activate the conda environment and run the following python script:

import tensorflow as tf

# If this program does not throw any error while running, it means GPU acceleration is being used.
# If there is an error message after running this program, you need to check you graphics driver settings

with tf.device('/gpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)

with tf.Session() as sess:
print (sess.run(c))


If running this script does not throw any errors, then your setup was successful. Now, enjoy training your models! 💻

##### Man Parvesh Singh Randhawa
###### M.S. C.S. student @ UT Dallas | Ex-Works Applications Singapore | IIT Guwahati

I am a Software Engineer interested in efficient large-scale distributed software systems.

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