ATFT Training Skill

Mission

• Launch production-grade training for the Graph Attention Network forecaster with correct dataset/version parity.
• Tune hyper-parameters (LR, batch size, horizons, latent dims) exploiting 80GB GPU headroom.
• Safely resume, stop, or monitor long-running jobs and record experiment metadata.

Engagement Triggers

• Requests to “train”, “fine-tune”, “HP optimize”, “resume training”, or “monitor training logs”.
• Need to validate new dataset compatibility with model code.
• Investigations into training stalls, divergence, or GPU under-utilization.

Preflight Safety Checks

1. Dataset freshness: ls -lh output/mldatasetlatestfull.parquet then python scripts/utils/datasetguard.py --assert-recency 72.
2. Environment health: tools/project-health-check.sh --section training.
3. GPU allocation: nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv (target >60% util, <76GB used baseline).
4. Git hygiene: git status --short ensure working tree state is understood (avoid accidental overrides during long runs).

Training Playbooks

1. Production Optimized Training (default 120 epochs)

1. make train-optimized DATASET=output/mldatasetlatest_full.parquet — compiles TorchInductor + FlashAttention2.
2. make train-monitor — tails _logs/training/train-optimized.log.
3. make train-status — polls background process; ensure ETA < 7h.
4. Post-run validation:

- python scripts/eval/aggregatemetrics.py runs/latest — compute Sharpe, RankIC, hit ratios. - Update results/latesttraining_summary.md.

2. Quick Validation / Smoke

1. make train-quick EPOCHS=3 — run in foreground.
2. python scripts/smoke_test.py --max-epochs 1 --subset 512 for additional regression guard.
3. pytest tests/integration/testtrainingloop.py::testforwardbackward if suspicious gradients.

3. Safe Mode / Debug

1. make train-safe — disables compile, single-worker dataloading.
2. make train-stop if hung jobs detected (consult _logs/training/pids/).
3. python scripts/integratedmltrainingpipeline.py --profile --epochs 2 --no-compile — capture flamegraph to benchmarkoutput/.

4. Hyper-Parameter Exploration

1. Ensure mlflow backend running if required (make mlflow-up).
2. make hpo-run HPOTRIALS=24 HPOSTUDY=atftprodlr_sched — uses Optuna integration.
3. make hpo-status — track trial completions.
4. Promote winning config → configs/training/atftprod.yaml and document in EXPERIMENTSTATUS.md.

Monitoring & Telemetry

• Training logs: _logs/training/*.log (includes gradient norms, learning rate schedule, GPU temp).
• Metrics JSONL: runs/<timestamp>/metrics.jsonl.
• Checkpoint artifacts: models/checkpoints/<timestamp>/epoch_###.pt.
• GPU telemetry: watch -n 30 nvidia-smi or python tools/gpumonitor.py --pid $(cat logs/training/pids/train.pid).

Failure Handling

• NaN loss → run make train-safe with FP32=1, inspect runs/<ts>/nan_batches.json.
• Slow dataloading → regenerate dataset with make dataset-gpu GRAPH_WINDOW=90 or enable PyTorch compile caching.
• OOM → set GRADIENTACCUMULATIONSTEPS=2 or reduce BATCHSIZE; confirm memory fragments via python tools/gpumemory_report.py.
• Divergent metrics → verify configs/training/schedule.yaml; run pytest tests/unit/testlossfunctions.py.

Codex Collaboration

• Invoke ./tools/codex.sh --max "Design a new learning rate policy for ATFT-GAT-FAN" when novel optimizer or architecture strategy is required.
• Use codex exec --model gpt-5-codex "Analyze runs/<timestamp>/metrics.jsonl and suggest fixes" for automated postmortems.
• Share Codex-discovered tuning insights in results/training_runs/ and update config files/documents accordingly.

Post-Training Handoff

• Persist summary in results/training_runs/<timestamp>.md noting dataset hash and commit SHA.
• Push model weights to models/artifacts/ with naming gatfan<date>Sharpe<score>.pt.
• Notify research team via docs/research/changelog.md.