Goal

Automate hyperparameter search with pruning and distributed study support.

Tasks

• Add tuning/optuna_runner.py which:
  • accepts a configuration (search space, objective)
  • returns best params and trial history
• Integrate pruning callbacks for early stop of bad trials.
• Add optional RDB storage config for distributed tuning runs.
• Provide example YAML and CLI to run tuning.

Motivation

Automates efficient hyperparameter search and prevents exhaustive grid scanning.