modelstamp

Model files with receipts.

modelstamp verifies persisted Python machine-learning models, detects relevant dependency drift, and records reproducible environment metadata. Its sidecar manifest connects each artifact to its checksum, serialization backend, model details, runtime dependencies, and caller-provided metadata.

pip install modelstamp

import modelstamp as ms

ms.save(model, "model.joblib", metadata={"validation_auc": 0.91})
model, manifest = ms.load("model.joblib")

The artifact is hashed before deserialization. Dependency changes can warn or block loading, and optional HMAC authentication protects the model/manifest pair against unauthorized replacement.

Start with the quick start, then review the security boundary before loading persisted Python objects.

Choose a guide

• Detect dependency drift without loading the model.
• Verify a joblib artifact before deserialization.
• Add artifact checks to CI/CD.
• Understand when to use Modelstamp with lock files, registries, skops.io, or ONNX.
• Run the repository's complete examples.

Releases are published from protected v* tags using PyPI Trusted Publishing. Maintainers can use the release checklist for the protected main-to-PyPI workflow.