Inspect model artifacts and runtime profiles with Google Model Explorer and Arm extensions
Introduction
Understand Model Explorer and the artifacts you will inspect
Install Model Explorer and the Arm extensions
Open and inspect a Cortex-M PTE with Model Explorer
Compare portable and XNNPACK PTE files with Model Explorer
Inspect Ethos-U PTE delegation with Model Explorer
Inspect TOSA artifacts with Model Explorer
Inspect VGF artifacts with Model Explorer
Inspect ETRecord and ETDump overlays with Model Explorer
Next Steps
Inspect model artifacts and runtime profiles with Google Model Explorer and Arm extensions
Introduction
Understand Model Explorer and the artifacts you will inspect
Install Model Explorer and the Arm extensions
Open and inspect a Cortex-M PTE with Model Explorer
Compare portable and XNNPACK PTE files with Model Explorer
Inspect Ethos-U PTE delegation with Model Explorer
Inspect TOSA artifacts with Model Explorer
Inspect VGF artifacts with Model Explorer
Inspect ETRecord and ETDump overlays with Model Explorer
Next Steps
Who is this for?
This Learning Path is for Edge AI developers who need to inspect model artifacts after backend delegation, understand graph structure and delegate coverage, and use those insights to reason about performance and behavior.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Set up Google Model Explorer with the combined ExecuTorch extension and separate Tensor Operator Set Architecture (TOSA) and VGF adapters.
- Open ExecuTorch deployment graphs and inspect delegate regions, work outside delegates, graph fragmentation, and backend-specific changes
- Trace model transformations across PTE, TOSA, and VGF artifacts
- Use ETRecord and ETDump overlays to connect exported graph structure with runtime profiling data
Prerequisites
Before starting, you will need the following:
- Python 3.10, 3.11, or 3.12
- Basic familiarity with PyTorch, ExecuTorch, or model deployment workflows
- Git Large File Storage (LFS)
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.
.pte, .tosa, .vgf, .etrecord, and .etdp artifacts to understand how Google Model Explorer and Arm extensions expose model export, lowering, backend delegation, and runtime behavior. After installing the ExecuTorch extension and the TOSA and VGF adapters, you’ll use a Cortex-M PTE to learn graph navigation, compare portable and XNNPACK Cortex-A programs, and diagnose no, clean, and fragmented Ethos-U delegation. You’ll then inspect TOSA intermediate graphs, relate TOSA fragments to delegation boundaries, and compare a VGF-backed PTE with the standalone VGF graph used by workflows with the ML extensions for Vulkan. Finally, you’ll open ETRecord files and load their matching ETDump data to identify whether delegate calls or non-delegated operators dominate runtime.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.
.pte and .etrecord files and add .etdp profiling overlays. Enable the TOSA adapter for .tosa files and the VGF adapter for .vgf files. By running all three, you can switch between formats in one session..etrecord file and its .etdp profiling data with the ExecuTorch extension enabled. Model Explorer overlays align these data with graph nodes so you can correlate structure with runtime behavior.