Package Private ML Artifacts

Preserve reproducibility while keeping credentials, restricted sources, and large assets out of source control.

Confirm authorization and scope

Obtain explicit approval before uploading datasets, weights, adapters, checkpoints, or other large artifacts. Confirm the destination is private and list exactly what will be uploaded. Never infer that permission to train also grants permission to publish artifacts.

Classify each artifact by source, license, sensitivity, retention need, and whether it belongs in Git, private object storage, or a private model/dataset repository.

Build a manifest

Run:

python scripts/build_artifact_manifest.py experiment-output \
  --output experiment-output/artifact-manifest.json \
  --max-file-mb 5000