Gpflow

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

Bugfix for creating the new posterior objects with `PrecomputeCacheType.VARIABLE`.

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--

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

5. You are almost done now! Go to https://circleci.com and monitor that tests for your newly-created
tag passed and the job for pushing the pip package succeeded. CircleCI matches on the
“v{VERSION}” tag to kick-start the release process.
- [example CI workflow: 2434](https://app.circleci.com/pipelines/github/GPflow/GPflow/2434/workflows/f1274aa7-18c6-45a3-8d59-cab573305b64)

6. Take a break; wait until the new release
[shows up on PyPi](https://pypi.org/project/gpflow/#history).


Done done! Go and celebrate our hard work :)


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

* <DOCUMENT BREAKING CHANGES HERE>
* <THIS SECTION SHOULD CONTAIN API AND BEHAVIORAL BREAKING CHANGES>

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>

Small bugfix release