# Evidence: Resilient distributed training

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

## Evidence required

Keep the checkpoint path, saved/restored full state, subsequent sample IDs and losses through a new epoch. Diagnose missing momentum and a changed shuffle key; state the untested multi-host boundary.

- Command: `python3 phases/09-distributed/04-resilient-distributed-training/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