Who is this for?
This is an advanced topic for developers exploring neural graphics and interested in training and deploying upscaling models like Neural Super Sampling (NSS) using PyTorch and Arm’s hardware-aware backend.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Understand the principles of neural graphics and how it’s applied to game performance
- Learn how to fine-tune and evaluate a neural network for Neural Super Sampling (NSS)
- Use the Model Gym Python API and CLI to configure and train neural graphics models
- Fine-tune an NSS model with quantization-aware training (QAT) and export it to .vgf
- Inspect the graph of exported .vgf models using Model Explorer
Prerequisites
Before starting, you will need the following:
- Basic understanding of PyTorch and machine learning concepts
- A development machine running Ubuntu 22.04 or later, with a CUDA-capable NVIDIA® GPU
- CUDA Toolkit v13.1.1 or later