GPflowOpt
A Python library for Bayesian optimization using GPflow and TensorFlow, designed for optimizing expensive black-box functions.
What is GPflowOpt?
GPflowOpt is a Python library for Bayesian optimization that uses GPflow and TensorFlow to optimize expensive black-box functions. It provides a framework for defining optimization domains, acquisition functions, and strategies to efficiently find optimal solutions with minimal evaluations. The library is particularly useful for tasks where function evaluations are costly, such as hyperparameter tuning or experimental design.
Machine learning researchers and practitioners who need to optimize expensive functions, such as hyperparameters of deep learning models or parameters in scientific simulations. It's also suitable for developers working on automated experimental design or black-box optimization problems.
GPflowOpt offers a modular and extensible Bayesian optimization framework built on the robust Gaussian process capabilities of GPflow, with TensorFlow enabling scalable computations. Its integration with GPflow allows for flexible probabilistic modeling, making it a powerful tool for efficient global optimization compared to generic optimization libraries.
Overview
Bayesian Optimization using GPflow
Use Cases
Best For
- Hyperparameter tuning for machine learning models
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