[Opus47-D576] WEVAL pool +2 GPUs T4 free — USER action 5min
Le notebook contient 7 cellules : install deps, load Llama 3.2 1B, FastAPI server, cloudflared tunnel, push URL auto.
Click + Create → New Notebook → Import (icon haut droite) → upload weval-kaggle-gpu-notebook.ipynb
Click Run All (▶▶ icon ou Ctrl+Shift+Enter)
~3 min : install deps + load model + start FastAPI + cloudflared tunnel
La cellule 4 imprime: ✅ TUNNEL_URL: https://xxxxx.trycloudflare.com
La cellule 5 push automatiquement vers WEVAL S204.
Tester l'endpoint:
curl -X POST https://weval-consulting.com/api/wevia-kaggle-gpu.php \
-H "Content-Type: application/json" \
-d '{"message":"hi WEVAL","max_tokens":30}'
Identical architecture but uses Qwen 2.5 1.5B (no auth required, fits T4 13GB).
File → Upload notebook → select weval-colab-gpu-notebook.ipynb
~2 min de setup. Cellule 4 imprime TUNNEL_URL + cellule 5 push auto.
curl -X POST https://weval-consulting.com/api/wevia-colab-gpu.php \
-H "Content-Type: application/json" \
-d '{"message":"hi","max_tokens":30}'
1. Le notebook lance FastAPI sur port 8000 dans la VM Kaggle/Colab
2. Cloudflared tunnel expose un URL public *.trycloudflare.com
3. Le notebook POST l'URL à /api/{kaggle|colab}-tunnel-push.php qui l'écrit dans tunnel-url.txt
4. Les bridges wevia-{kaggle|colab}-gpu.php lisent ce fichier et proxy les requests
5. Le mass-orchestrator (D569) appelle ces bridges en parallèle avec les autres 91 agents