Bayesian Optimization for Anything: A high-level Bayesian optimization framework and model wrapping tool. It provides an easy-to-use interface between models and the python libraries Ax and BoTorch.
Project description
Bayesian Optimization for Anything
BOA is a high-level Bayesian optimization framework and model wrapping tool for providing an easy-to-use interface between models and the python libraries Ax and BoTorch
Key features
-
Model agnostic
- Can be used for models in any language (not just python)
- Can be used for Wrappers in any language (You don't even need to write any python!) See
Script Wrapper
for details on how to do that. - Simple to implement for new models, with minimal coding required
-
Scalable
- Can be used for simple models or complex models that require a lot of computational resources
- Scheduler to manage individual model runs
- Supports parallelization
-
Modular & customizable
- Can take advantages of the many features of Ax/BoTorch
- Customizable objective functions, multi-objective optimization, acquisition functions, etc
- Choice of built-in evaluation metrics, but it’s also easy to implement custom metrics
Install requirements
Docs | |
DOI | |
Conda Install | |
PyPI Install | |
Github Latest release | |
Github dev release | |
Build Status | |
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