Keeping track of hyperparameter optimization becomes more difficult when multiple processes or machines contribute trials to the same experiment. Each worker needs access to a consistent study history, and the resulting trials need to be easy to inspect without manually parsing logs.

Optuna addresses these needs with persistent storage backends and Optuna Dashboard . JournalStorage records changes to study and trial state in an operation log, while Optuna Dashboard provides a browser interface for inspecting that history. Together, they offer a practical workflow for tracking experiments without writing custom visualization code.

This guide combines my notes on JournalStorage and Optuna Dashboard, with a small illustrative Python example added to make the workflow reproducible. The original notes discuss shared NFS/NAS storage in a research computing environment. For this article, I checked the toy script's study creation and journal reloading with Optuna 4.8.0 using a small local run.…