Release notes for all past releases are available in the ['Releases' section](https://github.com/GPflow/GPflow/releases) of the GPflow GitHub Repo. [HOWTO_RELEASE.md](HOWTO_RELEASE.md) explains just that. # Release x.y.z (template for future releases) ## Breaking Changes * `gpflow.utilities.utilities` has been removed. It was scheduled for deletion in `2.3.0`. Use `gpflow.utilities` instead. ## Known Caveats * * * ## Major Features and Improvements * * ## Bug Fixes and Other Changes * * * ## Thanks to our Contributors This release contains contributions from: jesnie # Release 2.5.0 (next upcoming release in progress) ## Breaking Changes * * ## Known Caveats * Dropped support for Python 3.6. New minimum version is 3.7. * Dropped support for TensorFlow 2.2 and 2.3. New minimum version is 2.4 * Further improvements to type hints - this may reveal new problems in your code-base if you use a type checker, such as `mypy`. ## Major Features and Improvements * * ## Bug Fixes and Other Changes * Minor improvement to code clarity (variable scoping) in SVGP model (#1800) * Improving mathematical formatting in docs (SGPR derivations) (#1806) ## Thanks to our Contributors This release contains contributions from: ltiao, jesnie # Release 2.4.0 This release mostly focuses on make posterior objects useful for Bayesian Optimisation. It also adds a new `experimetal` sub-package, with a tool for annotating tensor shapes. ## Breaking Changes * Slight change to the API of custom posterior objects. `gpflow.posteriors.AbstractPosterior._precompute` no longer must return an `alpha` and an `Qinv` - instead it returns any arbitrary tuple of `PrecomputedValue`s. Correspondingly `gpflow.posteriors.AbstractPosterior._conditional_with_precompute` should no longer try to access `self.alpha` and `self.Qinv`, but instead is passed the tuple of tensors returned by `_precompute`, as a parameter. (#1763, #1767) * Slight change to the API of inducing points. You should no longer override `gpflow.inducing_variables.InducingVariables.__len__`. Override `gpflow.inducing_variables.InducingVariables.num_inducing` instead. `num_inducing` should return a `tf.Tensor` which is consistent with previous behaviour, although the type previously was annotated as `int`. `__len__` has been deprecated. (#1766, #1792) ## Known Caveats * Type hints have been added in several places - this may reveal new problems in your code-base if you use a type checker, such as `mypy`. (#1766, #1769, #1771, #1773, #1775, #1777, #1780, #1783, #1787, #1789) ## Major Features and Improvements * Add new posterior class to enable faster predictions from the VGP model. (#1761) * VGP class bug-fixed to work with variable-sized data. Note you can use `gpflow.models.vgp.update_vgp_data` to ensure variational parameters are updated sanely. (#1774). * All posterior classes bug-fixed to work with variable data sizes, for Bayesian Optimisation. (#1767) * Added `experimental` sub-package for features that are still under developmet. * Added `gpflow.experimental.check_shapes` for checking tensor shapes. (#1760, #1768, #1782, #1785, #1788) ## Bug Fixes and Other Changes * Make `dataclasses` dependency conditional at install time. (#1759) * Simplify calculations of some `predict_f`. (#1755) ## Thanks to our Contributors This release contains contributions from: jesnie, tmct, joacorapela # Release 2.3.1 This is a bug-fix release, primarily for the GPR posterior object. ## Bug Fixes and Other Changes * GPR posterior * Fix the calculation in the GPR posterior object (#1734). * Fixes leading dimension issues with `GPRPosterior._conditional_with_precompute()` (#1747). * Make `gpflow.optimizers.Scipy` able to handle unused / unconnected variables. (#1745). * Build * Fixed broken CircleCi build (#1738). * Update CircleCi build to use next-gen Docker images (#1740). * Fixed broken triggering of docs generation (#1744). * Make all slow tests depend on fast tests (#1743). * Make `make dev-install` also install the test requirements (#1737). * Documentation * Fixed broken link in `README.md` (#1736). * Fix broken build of `cglb.ipynb` (#1742). * Add explanation of how to run notebooks locally (#1729). * Fix formatting in notebook on Heteroskedastic Likelihood (#1727). * Fix broken link in introduction (#1718). * Test suite * Amends `test_gpr_posterior.py` so it will cover leading dimension uses. ## Thanks to our Contributors This release contains contributions from: st--, jesnie, johnamcleod, Andrew878 # Release 2.3.0 ## Major Features and Improvements * Refactor posterior base class to support other model types. (#1695) * Add new posterior class to enable faster predictions from the GPR/SGPR models. (#1696, #1711) * Construct Parameters from other Parameters and retain properties. (#1699) * Add CGLB model (#1706) ## Bug Fixes and Other Changes * Fix unit test failure when using TensorFlow 2.5.0 (#1684) * Upgrade black formatter to version 20.8b1 (#1694) * Remove erroneous DeprecationWarnings (#1693) * Fix SGPR derivation (#1688) * Fix tests which fail with TensorFlow 2.6.0 (#1714) ## Thanks to our Contributors This release contains contributions from: johnamcleod, st--, Andrew878, tadejkrivec, awav, avullo # Release 2.2.1 Bugfix for creating the new posterior objects with `PrecomputeCacheType.VARIABLE`. # 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