docs/reproducibility_checklist.md

Reproducibility Checklist

• Record commit hash for every run.
• Log full config files for training and evaluation.
• Store random seed values for Python, NumPy, and PyTorch.
• Keep dataset split manifests identity-disjoint and immutable.
• Save model checkpoints with timestamp and metric tags.
• Track software versions (python3 --version, pip freeze).
• Archive key outputs: metrics JSON, confusion matrix, ROC, attention maps.
• Document hardware profile (GPU model, VRAM, CUDA/cuDNN versions).