swh:1:snp:6298e170750eb9cfb13fbd6f84b11ca6a6738b27
Tip revision: ab041d2f98e60c8d24a4ecf474baa9978c6c76c7 authored by Jesper Nielsen on 22 July 2022, 13:51:26 UTC
Make tf_decorator.make_decorator configurable. (#1942)
Make tf_decorator.make_decorator configurable. (#1942)
Tip revision: ab041d2
RELEASE.md
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>
# Release 2.6.0 (next upcoming release in progress)
<INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES>
## Breaking Changes
* All likelihood methods now take an extra `X` argument. If you have written custom likelihoods or
you have custom code calling likelihoods directly you will need to add this extra argument.
* On the `CGLB` model `xnew` parameters have changed name to `Xnew`, to be consistent with the other
models.
* On the `GPLVM` model the variance returned by `predict_f` with `full_cov=True` has changed shape
from `[batch..., N, N, P]` to `[batch..., P, N, N]` to be consistent with the other models.
* `gpflow.likelihoods.Gaussian.DEFAULT_VARIANCE_LOWER_BOUND` has been replaced with
`gpflow.likelihoods.scalar_continuous.DEFAULT_LOWER_BOUND`.
* Change to `InducingVariables` API. `InducingVariables` must now have a `shape` property.
* `gpflow.experimental.check_shapes.get_shape.register` has been replaced with
`gpflow.experimental.check_shapes.register_get_shape`.
* `check_shapes` will, no longer automatically wrap shape checking in
`tf.compat.v1.flags.tf_decorator.make_decorator`. This is likely to affect you if you use
`check_shapes` with Keras custom models. If you require the decorator you can manually enable it
with `check_shapes(..., tf_decorator=True)`.
## Known Caveats
* Shape checking is now, by default, disabled within `tf.function`. Use `set_enable_check_shapes` to
change this behaviour.
## Major Features and Improvements
* Improved handling of variable noise
- All likelihood methods now take an `X` argument, allowing you to easily implement
heteroscedastic likelihoods.
- The `Gaussian` likelihood can now be parametrized by either a `variance` or a `scale`
- Some existing likelihoods can now take a function (of X) instead of a parameter, allowing them
to become heteroscedastic. The parameters are:
- `Gaussian` `variance`
- `Gaussian` `scale`
- `StudentT` `scale`
- `Gamma` `shape`
- `Beta` `scale`
* `gpflow.experimental.check_shapes`
- Can now be in three different states - ENABLED, EAGER_MODE_ONLY, and DISABLE.
- Now support multiple variable-rank dimensions at the same time, e.g. `cov: [n..., n...]`.
- Now uses custom function `register_get_shape` instead of `get_shape.register`, for better
compatibility with TensorFlow.
## 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>
* Add support for Apple Silicon Macs (`arm64`) via the `tensorflow-macos` dependency. (#1850)
## Thanks to our Contributors
This release contains contributions from:
jesnie, corwinpro
# Release 2.5.2
This release fixes a performance regression introduced in `2.5.0`. `2.5.0` used features of Python
that `tensorfow < 2.9.0` do not know how to compile, which negatively impacted performance.
## Bug Fixes and Other Changes
* Fixed some bugs that prevented TensorFlow compilation and had negative performance impact. (#1882)
* Various improvements to documentation. (#1875, #1866, #1877, #1879)
## Thanks to our Contributors
This release contains contributions from:
jesnie
# Release 2.5.1
Fix problem with release process of 2.5.0.
## Bug Fixes and Other Changes
* Fix bug in release process.
## Thanks to our Contributors
This release contains contributions from:
jesnie
# Release 2.5.0
The focus of this release has mostly been bumping the minimally supported versions of Python and
TensorFlow; and development of `gpflow.experimental.check_shapes`.
## Breaking Changes
* Dropped support for Python 3.6. New minimum version is 3.7. (#1803, #1859)
* Dropped support for TensorFlow 2.2 and 2.3. New minimum version is 2.4. (#1803)
* Removed sub-package `gpflow.utilities.utilities`. It was scheduled for deletion in `2.3.0`.
Use `gpflow.utilities` instead. (#1804)
* Removed method `Likelihood.predict_density`, which has been deprecated since March 24, 2020.
(#1804)
* Removed property `ScalarLikelihood.num_gauss_hermite_points`, which has been deprecated since
September 30, 2020. (#1804)
## Known Caveats
* Further improvements to type hints - this may reveal new problems in your code-base if
you use a type checker, such as `mypy`. (#1795, #1799, #1802, #1812, #1814, #1816)
## Major Features and Improvements
* Significant work on `gpflow.experimental.check_shapes`.
- Support anonymous dimensions. (#1796)
- Add a hook to let the user register shapes for custom types. (#1798)
- Support `Optional` values. (#1797)
- Make it configurable. (#1810)
- Add accesors for setting/getting previously applied checks. (#1815)
- Much improved error messages. (#1822)
- Add support for user notes on shapes. (#1836)
- Support checking all elements of collections. (#1840)
- Enable stand-alone shape checking, without using a decorator. (#1845)
- Support for broadcasts. (#1849)
- Add support for checking the shapes of intermediate computations. (#1853)
- Support conditional shapes. (#1855)
* Significant speed-up of the GPR posterior objects. (#1809, #1811)
* Significant improvements to documentation. Note the new home page:
https://gpflow.github.io/GPflow/index.html
(#1828, #1829, #1830, #1831, #1833, #1841, #1842, #1856, #1857)
## Bug Fixes and Other Changes
* Minor improvement to code clarity (variable scoping) in SVGP model. (#1800)
* Improving mathematical formatting in docs (SGPR derivations). (#1806)
* Allow anisotropic kernels to have negative length-scales. (#1843)
## Thanks to our Contributors
This release contains contributions from:
ltiao, uri.granta, frgsimpson, st--, 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