PyHopper -- Hyperparameter optimization

TL;DR

PyHopper is a user-friendly, scalable hyperparameter optimization platform that simplifies the tuning process by using a single MCMC algorithm, reducing setup complexity and manual oversight for machine learning researchers.

Contribution

PyHopper introduces a minimalistic, scalable hyperparameter tuning tool based on a single MCMC algorithm, streamlining integration and reducing decision fatigue.

Findings

Scales to millions of dimensions.

Simplifies hyperparameter tuning workflow.

Open-source and easy to integrate.

Abstract

Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's goal is to integrate with existing code with minimal effort and run the optimization process with minimal necessary manual oversight. With simplicity as the primary theme, PyHopper is powered by a single robust Markov-chain Monte-Carlo optimization algorithm that scales to millions of dimensions. Compared to existing tuning packages, focusing on a single algorithm frees the user from having to decide between several algorithms and makes PyHopper easily customizable. PyHopper is publicly available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper.