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content/learning-paths/cross-platform/explore-model-artifacts-with-model-explorer/_index.md

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@@ -33,12 +33,17 @@ generated_summary_faq:
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faq_generated_at: '2026-07-24T17:13:27Z'
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faq_source_hash: 27a823fec7ad01beca56b6ad1d6ee8a07085686f5fbffb0b361ccb4f573ecf6e
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summary: >-
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You'll install and run Google Model Explorer with Arm extensions
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to inspect ExecuTorch deployment artifacts and relate graph structure to runtime data. First, you'll
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set up a clean environment, clone example models with Git LFS, and launch Model Explorer with
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the ExecuTorch, TOSA, and VGF adapters. Then, you'll open `.pte`, `.tosa`, `.vgf`, `.etrecord`, and `.etdp` files
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to examine delegate regions, work outside delegates, and graph fragmentation. You'll
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compare portable-kernel and XNNPACK PTEs for Cortex-A CPU paths and inspect Ethos-U delegation and Cortex-M targets. Finally, you'll correlate model structure with observed behavior using ETRecord and ETDump overlays that connect exported graphs with collected profiling.
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You'll use pre-generated `.pte`, `.tosa`, `.vgf`, `.etrecord`, and `.etdp`
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artifacts to understand how Google Model Explorer and Arm extensions expose
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model export, lowering, backend delegation, and runtime behavior. After
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installing the ExecuTorch extension and the TOSA and VGF adapters, you'll use
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a Cortex-M PTE to learn graph navigation, compare portable and XNNPACK
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Cortex-A programs, and diagnose no, clean, and fragmented Ethos-U delegation.
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You'll then inspect TOSA intermediate graphs, relate TOSA fragments to
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delegation boundaries, and compare a VGF-backed PTE with the standalone VGF
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graph used by workflows with the ML extensions for Vulkan. Finally, you'll
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open ETRecord files and load their matching ETDump data to identify whether
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delegate calls or non-delegated operators dominate runtime.
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faqs:
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- question: Which extensions should I enable to load the artifacts used here?
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answer: >-

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