Sherpa: Robust Hyperparameter Optimization for Machine Learning
This provides a practical solution for machine learning practitioners dealing with time-consuming hyperparameter optimization, though it is incremental as it builds on existing optimization methods.
The authors tackled the problem of hyperparameter optimization for computationally expensive machine learning models by introducing Sherpa, a library that enables efficient tuning using various algorithms and parallel execution, resulting in an automated tool that reduces the tedious aspects of model tuning.
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Sherpa can be run on either a single machine or in parallel on a cluster. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning practitioners by automating the more tedious aspects of model tuning. Its source code and documentation are available at https://github.com/sherpa-ai/sherpa.