Surrogate models and active learning for scientific applications.
The binaries for the latest version are available at https://github.com/NREL/bbopt/releases/latest. They can be installed through standard installation, e.g.,
using pip (https://pip.pypa.io/en/stable/cli/pip_install/):
python -m pip install
https://github.com/NREL/bbopt/archive/refs/tags/v0.4.2.tar.gzusing conda (https://docs.anaconda.com/working-with-conda/packages/install-packages/):
conda install
https://github.com/NREL/bbopt/archive/refs/tags/v0.4.2.tar.gzThis package contains a pyproject.toml with the list of requirements and dependencies (More about pyproject.toml at https://packaging.python.org/en/latest/specifications/pyproject-toml/). This project is configured to use the package manager pdm. With pdm installed, run pdm install at the root of this repository to install the dependencies. The file pyproject.toml has the list of dependencies and configurations for the project. Use pdm build to build the packages binaries in the dist directory. Then follow the steps in the Binaries section to install.
Install the package manager pdm. To install all the dependencies listted in the pyproject.toml, run pdm install.
This project uses Sphinx to generate the documentation. The latest documentation is available at https://nrel.github.io/bbopt. To generate the documentation locally, run make html in the docs directory. The homepage of the documentation will then be found at docs/_build/html/index.html.
This project uses pytest to run the tests. To run the tests, run pytest at the root of this repository. Run pytest --help to see the available options.
Please, read the contributing guidelines before contributing to this project.
This project is licensed under the GPL-3.0 License. See the LICENSE file for details.
NREL Software Record number: SWR-24-57