Prepare models for neural graphics with Arm neural technology
Introduction
Set up your environment
Create a reference model
Export the reference model with the ExecuTorch VGF backend
Inspect the model in Model Explorer
Inspect TOSA artifacts
Next Steps
Prepare models for neural graphics with Arm neural technology
Review frequently asked questions
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.
Use Python 3.10 or later and earlier than 3.14. Run
python3 --version and confirm that the reported version is in that range.Start with a small graph to make the export and conversion flow easier to inspect. You can validate PyTorch export, VGF generation, and artifact inspection before you move to a production NSS model.
After you run
python export_vgf.py, check for .vgf artifacts in executorch-model/ and the generated as-vgf.pte file. For optional runtime validation, build the VKML runner and run python run_vgf_pte.py.Install Model Explorer and the
pte-adapter-model-explorer, tosa-adapter-model-explorer, and vgf-adapter-model-explorer packages in your active virtual environment. Run model-explorer --extensions=pte_adapter_model_explorer,tosa_adapter_model_explorer,vgf_adapter_model_explorer, then open a .vgf artifact in executorch-model/ or as-vgf.pte.Use TOSA to check operator lowering before backend compilation, confirm tensor layout and shape flow, or compare behavior when different backends produce different results.