Trieste documentation#
Trieste is a research toolbox built on TensorFlow, dedicated to Bayesian optimization, the process of finding the optimal values of an expensive, black-box objective function by employing probabilistic models over observations.
Without loss of generality, Trieste only supports minimizing the objective function. In the simplest case of an objective function with one-dimensional real output \(f: X \to \mathbb R\), this is
When the objective function has higher-dimensional output, we can still talk of finding the minima, though the optimal values will form a Pareto set rather than a single point. Trieste provides functionality for optimization of single-valued objective functions, and supports extension to the higher-dimensional case. It also supports optimization over constrained spaces, learning the constraints alongside the objective.
Trieste (pronounced tree-est) 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.
Installation#
To install Trieste, run
$ pip install trieste
The library supports Python 3.9 onwards, and uses semantic versioning.
Getting help#
We welcome contributions. 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.