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A Bayesian optimization research toolbox built on TensorFlow

Project description


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.


To install trieste, run

$ pip install trieste

or to install from sources, run

$ pip install .

in the repository root.


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.


Apache License 2.0

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