A Bayesian optimization research toolbox built on TensorFlow
Project description
Trieste
A Bayesian optimization toolbox built on TensorFlow. Trieste supports Python 3.7 onwards and uses semantic versioning.
We welcome contributions. See the guidelines to get started.
Installation
To install trieste, run
$ pip install trieste
or to install from sources, run
$ pip install .
in the repository root.
Documentation
Trieste has a documentation site with tutorials on how to use the library, and an API reference. You can also run the tutorials interactively. They can be found in the notebooks directory, and are written as Python scripts for running with Jupytext. To run them, first install trieste from sources as above, then install additional dependencies with
$ pip install -r notebooks/requirements.txt
Finally, run the notebooks with
$ jupyter-notebook notebooks
Getting help
- To submit a pull request, file a bug report, or make a feature request, see the contribution guidelines.
- For more open-ended questions, or for anything else, join the discussions on Trieste channels in Secondmind Labs' community Slack workspace.
License
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