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MLWM v1 Traceability Rules

Branching

Do not develop directly on master
Use feature branches for MLWM changes
Merge feature branches into the MLWM integration branch before merging to master

Commits

Keep commits single-topic and atomic
Use conventional prefixes such as feat(mlwm), feat(ui), docs(mlwm), test(mlwm)

Run manifests

Every training run writes a run_manifest.json with:

git branch
git commit SHA
dirty flag
Python version
CUDA version
GPU name
config hash
dataset manifest hash
timing data
model digests

Git tracking rules

Tracked in Git:

source code
docs
configs
promoted model manifests
benchmark summaries

Tracked through Git LFS:

promoted ONNX files
promoted checkpoints

Ignored:

raw datasets
intermediate checkpoints
temporary exports
tensorboard logs
large per-run artifacts outside promoted releases