# Evidence: Deploy at the edge: conversion, budgets and device checks

Copy this template into your own portfolio and fill it after running your modified exercise.

## Evidence required

Keep the CPU request report, preprocessing equivalence checks, chosen runtime/operator/precision matrix and an unfilled target-device report until actual device measurements exist. For the optional converter, retain the artifact, quantization metadata and held-out comparison.

- Command: `python3 phases/15-deployment/06-edge-ai-conversion-and-device-validation/code/main.py`
- Working directory: repository root
- Python / JAX / NumPy versions: 
- Hardware and device: 
- My prediction before running: 
- My change to the exercise: 
- Exit code and meaningful output: 
- Why the result supports or contradicts my prediction: 
- Checkpoint result: 
- Artifact link or saved filename: 
- Portfolio status: saved in local workspace