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Release notes for all past releases are available in the 'Releases' section of the GPflow GitHub Repo. HOWTO_RELEASE.md explains just that.

Release x.y.z (template for future releases)

Breaking Changes

Known Caveats

  • <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>
  • <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>

Major Features and Improvements

  • <INSERT MAJOR FEATURE HERE, USING MARKDOWN SYNTAX>
  • <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>

Bug Fixes and Other Changes

  • <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES>
  • <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE>

Thanks to our Contributors

This release contains contributions from:

, , , , ,

Release 2.2.1 (next upcoming release in progress)

Breaking Changes

Known Caveats

  • <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>
  • <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>

Major Features and Improvements

  • <INSERT MAJOR FEATURE HERE, USING MARKDOWN SYNTAX>
  • <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>

Bug Fixes and Other Changes

  • <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES>
  • <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE>

Thanks to our Contributors

This release contains contributions from:

, , , , ,

Release 2.2.0

The main focus of this release is the new "Posterior" object introduced by PR #1636, which allows for a significant speed-up of post-training predictions with the SVGP model (partially resolving #1599).

  • For end-users, by default nothing changes; see Breaking Changes below if you have written your own implementations of gpflow.conditionals.conditional.
  • After training an SVGP model, you can call model.posterior() to obtain a Posterior object that precomputes all quantities not depending on the test inputs (e.g. Choleskty of Kuu), and provides a posterior.predict_f() method that reuses these cached quantities. model.predict_f() computes exactly the same quantities as before and does not give any speed-up.
  • gpflow.conditionals.conditional() forwards to the same "fused" code-path as before.

Breaking Changes

  • gpflow.conditionals.conditional.register is deprecated and should not be called outside of the GPflow core code. If you have written your own implementations of gpflow.conditionals.conditional(), you have two options to use your code with GPflow 2.2:
    1. Temporary work-around: Instead of gpflow.models.SVGP, use the backwards-compatible gpflow.models.svgp.SVGP_deprecated.
    2. Convert your conditional() implementation into a subclass of gpflow.posteriors.AbstractPosterior, and register get_posterior_class() instead (see the "Variational Fourier Features" notebook for an example).

Known Caveats

  • The Posterior object is currently only available for the SVGP model. We would like to extend this to the other models such as GPR, SGPR, or VGP, but this effort is beyond what we can currently provide. If you would be willing to contribute to those efforts, please get in touch!
  • The Posterior object does not currently provide the GPModel convenience functions such as predict_f_samples, predict_y, predict_log_density. Again, if you're willing to contribute, get in touch!

Thanks to our Contributors

This release contains contributions from:

stefanosele, johnamcleod, st--

Release 2.1.5

Known Caveats

  • GPflow requires TensorFlow >= 2.2.

Deprecations

  • The gpflow.utilities.utilities submodule has been deprecated and will be removed in GPflow 2.3. User code should access functions directly through gpflow.utilities instead (#1650).

Major Features and Improvements

  • Improves compatibility between monitoring API and Scipy optimizer (#1642).
  • Adds _add_noise_cov method to GPR model class to make it more easily extensible (#1645).

Bug Fixes

  • Fixes a bug in ModelToTensorBoard (#1619) when max_size=-1 (#1619)

  • Fixes a dynamic shape issue in the quadrature code (#1626).

  • Fixes #1651, a bug in fully_correlated_conditional_repeat (#1652).

  • Fixes #1653, a bug in the "fallback" code path for multioutput Kuf (#1654).

  • Fixes a bug in the un-whitened code path for the fully correlated conditional function (#1662).

  • Fixes a bug in independent_interdomain_conditional (#1663).

  • Fixes an issue with the gpflow.config API documentation (#1664).

  • Test suite

    • Fixes the test suite for TensorFlow 2.4 / TFP 0.12 (#1625).
    • Fixes mypy call (#1637).
    • Fixes a bug in test_method_equivalence.py (#1649).

Thanks to our Contributors

This release contains contributions from:

johnamcleod, st--, vatsalaggarwal, sam-willis, vdutor