mlflow-falsify 0.1.2

MLflow plugin: automatic PRML manifest hash tagging for runs. Pre-registered ML evaluation claims.

mlflow-falsify — automatic PRML manifest hash tagging for MLflow runs

Drop a PRML manifest in your repo. Every MLflow run gets cryptographically bound to it. No workflow changes.

Install

pip install mlflow-falsify

The plugin is discovered automatically through MLflow's mlflow.run_context_provider entry point.

Usage

import mlflow

# .prml.yaml exists in CWD or any parent directory — that's all you need.
with mlflow.start_run():
    mlflow.log_metric("accuracy", 0.873)
    # The run now carries prml.manifest_hash and friends as tags.

What gets tagged

When a .prml.yaml or prml.yaml is found in the current directory or any ancestor, every run is tagged with:

• prml.manifest_hash — SHA-256 of the canonical manifest bytes (PRML v0.1 §3)
• prml.manifest_path — relative path to the discovered manifest
• prml.version — manifest schema version (e.g. prml/0.1)
• prml.metric — the pre-registered metric (e.g. accuracy)
• prml.comparator — one of >=, >, ==, <=, <
• prml.threshold — the numeric threshold, as a string
• prml.dataset_id — the pre-registered dataset identifier

Missing or malformed fields are silently skipped. The provider never raises into your run.

Why this matters

• EU AI Act Article 12 evidence layer. Every logged run carries a tamper-evident pointer to the claim it was meant to test.
• Eval reproducibility by default. The hash freezes metric, threshold, dataset, and seed before the experiment runs.
• Audit trails for free. Reviewers can recompute the manifest hash from the YAML and compare it against your tracked runs.
• No workflow change. Existing MLflow code is untouched — the plugin attaches via entry points.

Links

• PRML specification: spec.falsify.dev/v0.1
• Falsify reference implementation: github.com/studio-11-co/falsify
• Zenodo (this plugin): doi.org/10.5281/zenodo.20235451
• Zenodo (PRML spec): doi.org/10.5281/zenodo.20177839
• Registry: registry.falsify.dev

License

MIT. Copyright 2026 Studio 11 / Cüneyt Öztürk.