Train and evaluate Neural Frame Rate Upscaling models using Model Gym
Introduction
Understand neural graphics and Model Gym
Set up your environment
Launch the training notebook
Fine-tune and export a quantized model
Visualize your model with Model Explorer
Next Steps

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Install dependencies on Ubuntu

Install a few machine learning and system dependencies on your Ubuntu environment.

Start by making sure Python is installed and the version is later than 3.10:

    

        
        
python3 --version

Next, install dependency packages:

    

        
        
sudo apt update
sudo apt install python3-venv python-is-python3 gcc make python3-dev -y

Set up the examples repository

The example notebooks are open-sourced in a GitHub repository.

Start by cloning the repository:

    

        
        
git clone https://github.com/arm/neural-graphics-model-gym-examples.git
cd neural-graphics-model-gym-examples

From inside the neural-graphics-model-gym-examples/ folder, run the environment creation script:

    

        
        
python create_env.py

The script does the following:

  • Creates a Python virtual environment called nb-env
  • Installs the ng-model-gym package and required dependencies
  • Downloads the datasets and weights needed to run the notebooks

Activate the virtual environment:

    

        
        
source nb-env/bin/activate

Run the following in a Python shell to confirm that the script was successful:

    

        
        
import torch
import ng_model_gym

print("Torch version:", torch.__version__)
print("Model Gym version:", ng_model_gym.__version__)

What you’ve accomplished and what’s next

You’ve now completed your environment setup by installing dependencies and setting up the example NFRU notebooks repository.

Next, you’ll train the neural graphics model.

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