🚀 BayesFlow v2.0.3 Release Notes
This release introduces significant changes to the serialization pipeline and is not backward-compatible with v2.0.2 or earlier. Models saved with older versions of BayesFlow will likely be incompatible with this release, but models trained in the future will be backward compatible with this release.
We strongly recommend updating your workflows and referring to the updated documentation to ensure a smooth transition.
📚 Documentation Updates
Enhanced the README.md with:
- A detailed guide for migrating from BayesFlow v1.x to v2.
- Clear warnings about breaking changes and missing features (e.g., hierarchical models, MCMC).
- Pointers for users seeking parity with legacy functionality.
See changes →
🔧 Backend Enhancements
- Simplified version management:
__version__is now dynamically set using importlib.metadata. - Removed redundant exception handling for unsupported package imports.
🌀 Transform Enhancements
New Feature: Added support for log_det_jac (log-determinant Jacobian) in multiple transform classes:
- Constrain
- Concatenate
- Drop
The Adapter class now optionally returns the log_det_jac alongside transformed data in both forward and inverse methods.
These improvements enhance support for probabilistic modeling workflows involving change-of-variable techniques.
🧬 Serialization Improvements
- Applied the @serializable decorator with explicit namespace="bayesflow.adapters" to key transform classes: AsSet, Broadcast, Constrain, and others. This improves the consistency and reliability of object serialization across the library, especially for save/load operations in custom pipelines.
✅ Summary
This release improves usability, extensibility, and transform robustness, especially for users of advanced transformations. While it introduces breaking changes, it also lays a more stable foundation for future developments. Stay tuned!
