Skip to main content

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

Apache License 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

trieste-0.10.0.tar.gz (121.1 kB view hashes)

Uploaded source

Built Distribution

trieste-0.10.0-py3-none-any.whl (168.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page