Finally, we have everything set up to run the training code—these steps will allow you to create the files and run the code:

1.  Create a file under the src folder called run.py and add the following code:

#!/usr/bin/env python3
from train import Trainer

# Command Line Argument Method
CUBE_SIDE=16
EPOCHS = 100000
BATCH = 64
CHECKPOINT = 10
LATENT_SPACE_SIZE = 256
DATA_DIR = "/3d-mnist/full_dataset_vectors.h5"

trainer = Trainer(side=CUBE_SIDE, 
                 latent_size=LATENT_SPACE_SIZE, 
                 epochs =EPOCHS,
                 batch=BATCH,
                 checkpoint=CHECKPOINT,
                 data_dir = DATA_DIR)
trainer.train()

This code simply defines the input needed for the training class and runs the training method for our GAN architecture.

2. We need to create the run.sh script at the root directory (make sure it's executable) and add the following to the file:

#/bin/bash

# Training Step
xhost +
docker run -it 
   --runtime=nvidia 
   --rm 
   -e DISPLAY=$DISPLAY 
   -v /tmp/.X11-unix:/tmp/.X11-unix 
   -v $HOME/3d-gan-from-images/out:/out 
   -v $HOME/3d-gan-from-images/src:/src 
   ch8 python3 /src/run.py

3. To run the code, execute the following command from a Terminal with the directory as the root directory of this code:

sudo ./run.sh

That's it! You're training this architecture now!