libsigopt is SigOpt’s computational library for intelligent experimentation. This library holds the core computation elements for running hyperparameter optimization, multimetric optimization and intelligent search. It is the core computational engine of sigopt-server and sigoptlite.
libsigopt is organized into the following submodules:
aux: constants, low-level computation methods, json schema validations, etc.compute: core computational elements used in optimization, such asDomain,GaussianProcess,AcquisitionFunction, etc.views: high-level interface for conducting computation such as generating new suggestions, evaluating the acquisition function, and more.
We expect users and contributors of this library to have a certain level of familiarity with Bayesian Optimization, Kernel-Based Approximation Methods, Gaussian Process and Machine Learning concepts. If these words don't make sense to you, consider using our other higher-level services and tools: app.sigopt.com, sigopt-python, sigopt-server and sigoptlite.
We welcome contributions to libsigopt. Our goal for this library is to be lightweight, have minimal requirements, and implement the computation methods in NumPy. If you want to leverage pytorch, consider contributing to botorch.
We hope to expand our compute submodule so that more users can benefit from this library. Modifications and contributions to the views submodule might impact other repos such as sigoptlitte and sigopt-server, therefore they need to be thoroughly tested in all repositories.
libsigopt is licensed under the Apache 2.0 license. See the LICENSE file for more information.