Introduction

Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale.

Objective

We introduce ArchGym, an open-source gymnasium and easy-to-extend framework that connects a diverse range of search algorithms to architecture simulators. The results suggest that with an unlimited number of samples, ML algorithms are equally favorable to meet the user-defined target specification if its hyperparameters are tuned thoroughly; no one solution is necessarily better than another. We show how using a same structure of code for different algirthms, we can train the agent to generate optimal parameters just by varying the name of the algorithm.

Supported Algorithms

The following algorithms are currently supported the vizier version:

  1. Random Search (RANDOM_SEARCH): Flat Search Spaces.

  2. Quasi-Random Search (QUASI_RANDOM_SEARCH): Flat Search Spaces.

  3. Grid Search (GRID_SEARCH): Flat Search Spaces.

  4. Emukit Bayesian Optimization (EMUKIT_GP_EI): Flat Search Spaces.

  5. NSGA2 (NSGA2) : Flat Search Spaces.