Description: Mathematical optimization is a critical aspect of DoD research and engineering workflows. Large scale optimization efforts with expensive modeling, simulation and machine learning (ML) tasks can benefit from scalable optimization algorithms. Optuna is a Python library that supports distributed optimization and hyperparameter tuning that can scale across large HPC systems. Global multi-objective and constrained optimization studies can be created with ease. Studies can be monitored in real-time, and results can be visualized immediately using Optuna’s integrated dashboard. In this Webinar, users will learn how to use Optuna at scale for general optimization and ML hyperparameter tuning on HPCMP systems.