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
Who is this for?
This is an advanced topic for developers who want to understand and debug the model preparation flow used by Arm neural technology in neural graphics pipelines.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Build and export a PyTorch model for ExecuTorch
- Generate `.vgf` artifacts with the ExecuTorch VGF backend
- Visualize model structure and generated artifacts using Model Explorer
- Inspect Tensor Operator Set Architecture (TOSA) intermediate representation when you need to debug operator lowering
- Validate the generated model with an ExecuTorch runner and connect it to ML Extensions for Vulkan workflows
Prerequisites
Before starting, you will need the following:
- Basic PyTorch and Python experience
- A Linux machine or macOS machine with Apple Silicon
- Python version greater than 3.10 and less than 3.14, and Git installed
Summary
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.
AddSigmoid reference model with the ExecuTorch VGF backend. First, you’ll set up the required environment, generate .vgf and .pte artifacts, and optionally validate the .pte with the VKML runner. Then, you’ll inspect the artifacts in Model Explorer and extract TOSA artifacts to debug operator lowering, tensor layouts, and shape flow.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.
python3 --version and confirm that the reported version is in that range.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.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.