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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About this Learning Path

Skill level:  Advanced
Reading time:  45 min
Last updated:  28 Jul 2026
Skill level:
  Advanced
Reading time:
  45 min
Last updated:
  28 Jul 2026
Author:Annie Tallund, Arm
Arm IP:Mali
Tags: ML Linux PyTorch Jupyter Notebook Vulkan NX
Author:
  • Annie Tallund, Arm
Arm IP:
Mali
Tags:
ML Linux PyTorch Jupyter Notebook Vulkan NX

Who is this for?

This is an advanced topic for developers exploring neural graphics and interested in training and deploying frame generation models such as Neural Frame Rate Upscaling (NFRU) 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
  • Fine-tune and evaluate a neural network for Neural Frame Rate Upscaling (NFRU)
  • Use the Model Gym Python API and CLI to configure and train neural graphics models
  • Fine-tune an NFRU 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, with a CUDA-capable NVIDIA GPU
  • CUDA Toolkit version 11.8 or later
  • A working environment with a Python version later than 3.10

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll set up an Ubuntu development environment, explore Arm’s Neural Graphics Model Gym, and fine-tune a Neural Frame Rate Upscaling (NFRU) model using PyTorch. First, you’ll launch a training notebook to configure and run NFRU training. Then, you’ll train and evaluate a model using PyTorch and Model Gym, fine-tune it with quantization-aware training, and export it to VGF (.vgf) for real-time deployment. You’ll also visualize and inspect the model’s structure using Model Explorer.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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How do I confirm my Python setup is ready before opening the notebooks?
Run python3 --version and verify it reports a version later than 3.10. Ensure the listed Ubuntu packages are installed so virtual environments and builds can complete.
Which notebook do I open to start training NFRU?
Open the NFRU training notebook in the examples repository you cloned. Follow the notebook cells to configure training and run the workflow.
What output should I expect after running training and export?
Expect an exported .vgf model produced by the training and export pipeline. You can open the file with Model Explorer using the VGF adapter.
How do I know the VGF adapter is working in Model Explorer?
Load a .vgf file and check that the model graph renders with layers, tensor shapes, and connectivity visible. If the graph opens without errors, the adapter is set up correctly.
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