Latest release from PyPI
pip install gpflow
The latest release requires TensorFlow ≥ 2.1 and TensorFlow Probability ≥ 0.9 (as well as Python ≥ 3.6).
Latest development release from GitHub
Be aware that the
develop branch may change regularly, and new commits may break your code.
In a check-out of the
develop branch of the GPflow GitHub repository, run
pip install -e .
Alternatively, you can install the latest GitHub
develop version using
pip install git+https://github.com/GPflow/GPflow.git@develop#egg=gpflow
This will automatically install all required dependencies.
We document the version history on GitHub.
Bugs, feature requests, pain points, annoying design quirks, etc:
Please use GitHub issues
to flag up bugs/issues/pain points, suggest new features, and discuss
anything else related to the use of GPflow that in some sense involves
changing the GPflow code itself.
You can make use of the labels such as
We positively welcome comments or concerns about usability, and suggestions for changes at any level of design.
We aim to respond to issues promptly, but if you believe we may have
forgotten about an issue, please feel free to add another comment to
Please use Stack Overflow (gpflow tag)
to ask questions that relate to "how to use GPflow", i.e. questions of
understanding rather than issues that require changing GPflow code. (If
you are unsure where to ask, you are always welcome to open a GitHub
issue; we may then ask you to move your question to Stack Overflow.)
All constructive input is gratefully received. For more information, see the notes for contributors.
GPflow heavily depends on TensorFlow and as far as
TensorFlow supports forward compatibility, GPflow should as well. The
version of GPflow can give you a hint about backward compatibility. If
the major version has changed then you need to check the release notes
to find out how the API has been changed.
Unfortunately, there is no such thing as backward
compatibility for GPflow models, which means that a model implementation
can change without changing interfaces. In other words, the TensorFlow
graph can be different for the same models from different versions of