It's now time to run the appropriate shell script, which is as follows:

#/bin/bash

# Training Step
xhost +
docker run -it 
   --runtime=nvidia 
   --rm 
   -e DISPLAY=$DISPLAY 
   -v /tmp/.X11-unix:/tmp/.X11-unix 
   -v /home/jk/Desktop/book_repos/Chapter4/DCGAN/data:/data 
   -v /home/jk/Desktop/book_repos/Chapter4/DCGAN/src:/src 
   ch4 python3 /src/run.py

Ensure that the script is executable (use chmod, as in other recipes) and place it at the appropriate level. At this point, your directory structure should look like the following:

DCGAN
├── data
├── docker
├── README.md
├── run.sh
├── scripts
└── src
    ├── discriminator.py
    ├── gan.py
    ├── generator.py
    ├── run.py
    ├── save_to_npy.py
    └── train.py

To run training, make sure that your Python run script is up to date, and then execute the shell run script at the root of DCGAN, as follows:

sudo ./run.sh

Now, let's talk about the results!