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A Bayesian optimization research toolbox built on TensorFlow

Project description


A Bayesian optimization toolbox built on TensorFlow. Trieste is named after the bathyscaphe Trieste, the first vehicle to take a crew to Challenger Deep in the Mariana Trench, the lowest point on the Earth's surface: the literal global minimum.

Supports Python 3.7 onwards. We use semantic versioning.

We welcome contributions. See the guidelines to get started.


To install trieste, run

$ pip install trieste


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 the library as above, then additional dependencies with

$ pip install -r notebooks/requirements.txt -c notebooks/constraints.txt

Finally, run the notebooks with

$ jupyter-notebook notebooks

Note: If you experience missing dependencies, check the notebooks are using the same python environment in which the dependencies were installed.

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 community discussions on our Slack workspace.


Apache License 2.0

Project details

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