القائمة
Train an ML model in the QuantConnect ML-Training-Pipeline with thermal-safe GPU usage and rigorous validation. Arguments: <architecture|script> [--dry-run] [--seeds 0,1,7,42,99] [--folds 5] [--bg]
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | train-model |
| description | Train an ML model in the QuantConnect ML-Training-Pipeline with thermal-safe GPU usage and rigorous validation. Arguments: <architecture|script> [--dry-run] [--seeds 0,1,7,42,99] [--folds 5] [--bg] |
Entrainer un modele ML du pipeline MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/ avec securite thermique GPU et validation rigoureuse. Couvre l'Epic #1454 (Training & Post-Training : RL/PPO, Decision Transformer, GenAI fine-tuning/LoRA).
Pour les cycles longs/batch, deleguer a l'agent training-specialist en async (run_in_background: true).
<architecture|script> : nom court (lstm, transformer, mamba, patchtst, itransformer, moe, gnn, dqn-rl, decision-transformer) ou nom de script train_*.py.--dry-run : smoke test CPU via validate_training_package.py --dry-run, sans GPU. Toujours commencer par la.--seeds 0,1,7,42,99 : seeds pour le multi-seed (defaut >=4 parmi 0/1/7/42/99).--folds 5 : walk-forward folds (defaut 5).--bg : lancer le training en background (recommande pour les longs runs).& "C:\Users\MYIA\miniconda3\envs\coursia-ml-training\python.exe".REGISTRY.md + CURRICULUM.md : le modele est-il deja un keeper valide ? deprecated ? Ne pas refaire.python validate_training_package.py --dry-run # smoke test, pas de GPU
Si le dry-run echoue : reparer (pas contourner). Ne pas passer en GPU sur un package casse.
python -c "from gpu_training import batch_thermal_check, get_gpu_temp; print('temp:', get_gpu_temp())"
cool_sleep=30, AMP).ImportError ou temp 0 => protection thermique en no-op silencieux (gpu_training.py absent). NE PAS lancer un long run GPU sans protection : restaurer le module ou monitorer nvidia-smi manuellement. Documenter.python scripts/train_<arch>.py (via env coursia-ml-training), en --bg pour les longs runs.tail -50 <log> | grep -E "FINAL|RESULT|ERROR|temp". Pas event-par-event.WalkForwardSplitter), pas single split.dm_test.py / diebold_mariano.py).majority_class_baseline + couts de transaction (5bps SPY, 10bps crypto).BEATS / NO BEATS / INCONCLUSIVE. Jamais "promising". Single-seed => [POC] explicite.python registry_update.py pour mettre a jour REGISTRY.md..pt volumineux/transients : ne pas stager dans la PR..pt lourds.Detect cell-ordering / enchainement problems in Jupyter notebooks (canonical-order slippage, misplaced or forgotten cells). Arguments: [target] [--severity HIGH|MED|LOW] [--json]
Resume multi-agent coordination session. Reads memory, RooSync inbox, GitHub issues, and produces a situational briefing with recommended actions. Arguments: [--dispatch] [--focus <topic>] [--reply-all]
Iterate on a GenAI notebook against the self-hosted stack via the genai-stack CLI (config dirs, auth, subdomains, quantization, GPU/VRAM). Arguments: <notebook|service> [--service comfyui|forge|vllm] [--quant int4|fp8] [--validate] [--bg]
Execute iterative improvement workflow for QuantConnect strategies. Arguments: [strategy|issue#] [--iterations=N] [--no-backtest] [--commit]
Review and merge student exercise PRs during TP sessions. Arguments: <repo-url> [--class <class-id>] [--timeslot <HH:MM-HH:MM>] [--dry-run]
Analyze PowerPoint slides qualitatively using vision AI. Arguments: <deck_path> [--render] [--slides 1,5,10]