Colab GPU Integration
Quick Reference
bash
# First time setup ./scripts/colab_sync.sh init # Every iteration cycle ./scripts/colab_sync.sh push # Push src/ to Drive # → User runs Colab notebook (Run All) ./scripts/colab_sync.sh watch # Auto-poll until complete ./scripts/colab_sync.sh pull # Pull results/ back # Check status manually ./scripts/colab_sync.sh status
How It Works
code
Local (Claude Code) Google Drive Colab (GPU)
───────────────── ──────────── ───────────
src/ ──push──────────→ research-fleet/src/ ──mount──→ /content/workspace/src/
↓
GPU Training
↓
results/ ←──pull───── research-fleet/results/ ←──sync── writes results + _colab_complete.json
Completion Detection
The Colab notebook writes _colab_complete.json when finished:
json
{
"iteration": 1,
"status": "complete",
"gpu": "Tesla T4",
"files_synced": 5
}
colab_sync.sh watch polls for this file every 30 seconds.
Troubleshooting
| Symptom | Cause | Fix |
|---|---|---|
push fails | Drive not mounted/configured | Run rclone config or install Google Drive Desktop |
status never completes | Colab disconnected | Reconnect Colab, re-run cells |
| Results missing | Train.py errored | Check Colab output cells for Python errors |
| Wrong iteration | State file stale | Check orchestrator_state.json iteration number |
Local GPU Fallback
If GPU is available locally, skip Colab entirely:
bash
cd workspace/src && python train.py
The rest of the pipeline doesn't care where results came from.