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)

<INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES>

## Breaking Changes

* `gpflow.utilities.utilities` has been removed. It was scheduled for deletion in `2.3.0`. Use `gpflow.utilities` instead.

## Known Caveats

* <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>
* <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>
* <KNOWN LACK OF SUPPORT ON SOME PLATFORM 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>
* <NOTES SHOULD BE GROUPED PER AREA>

## Thanks to our Contributors

This release contains contributions from:

<INSERT>, <NAME>, <HERE>, <USING>, <GITHUB>, <HANDLE>


# Release 2.5.0 (next upcoming release in progress)

<INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES>

## Breaking Changes

* Removed method `Likelihood.predict_density`, which has been deprecated since March 24, 2020.
* Removed property `ScalarLikelihood.num_gauss_hermite_points`, which has been deprecated since September 30, 2020.

## 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

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

## 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