7a92652289
Set up project policy files, baseline best-practice tracking, and a pre-commit performance gate so future train.py commits require measured RMSD improvement. Made-with: Cursor
1.3 KiB
1.3 KiB
ai_rfm
RFM overfitting sandbox for a single ligand sample, with hard quality gates.
Environment first (UV, cu126 only)
- Ensure Python 3.12 is available.
- Install env and deps:
uv sync
- Install git hooks:
uv run pre-commit install
This repository is intentionally pinned to CUDA 12.6 PyTorch wheels and matching PyG wheels.
Repository policy
- Every attempt must update this README (append a short entry in
## Attempt Log). - Commits touching
train.pymust include:reports/latest_eval.jsonBEST_PRACTICE.json- better or equal
mean_rmsd_100compared to previous best (enforced by pre-commit).
Evaluation target
- Metric: mean RMSD over 100 runs (
batchsize=100style aggregated evaluation). - Success criterion:
mean_rmsd_100 <= 1.0.
Key files
train.py: training/evaluation entry point.GUIDELINES.md: operating rules and workflow.BEST_PRACTICE.json: current best-known metric and config.reports/latest_eval.json: most recent measured metric.scripts/precommit_performance_gate.py: pre-commit guard for train-related commits.
Attempt Log
- 2026-04-16: Bootstrapped docs/environment policy and cu126 UV config. Added best-practice/performance gating scaffolding before the next training run.