Question 1

How to use Optuna with PyTorch Lightning?

Accepted Answer

Optuna provides integration examples for PyTorch Lightning in its optuna-examples repository. You can define an objective function that uses Lightning's Trainer and suggest hyperparameters via trial methods, then optimize over multiple trials. This allows automated tuning of parameters like learning rate or batch size.

Question 2

Optuna vs Hyperopt: which is better?

Accepted Answer

Optuna often excels with its define-by-run API for dynamic search spaces and efficient pruning, while Hyperopt uses a define-and-run approach with more focus on Bayesian optimization. For complex, conditional parameter spaces, Optuna is more flexible, but Hyperopt might be simpler for static setups. The choice depends on your need for modularity versus ease of use.

Question 3

Can Optuna handle multi-objective optimization?

Accepted Answer

Yes, Optuna supports multi-objective optimization, as mentioned in the news about AutoSampler and GPSampler. You can define multiple metrics in the objective function and use samplers tailored for balancing trade-offs, like NSGA-II. This is useful for tasks requiring optimization of conflicting goals, such as accuracy and model size.

Question 4

How to prune trials early in Optuna?

Accepted Answer

Optuna offers built-in pruners like MedianPruner or HyperbandPruner that you can integrate into your objective function. You need to report intermediate values during training, and the pruner will stop unpromising trials based on these values. This saves resources by avoiding full training runs for poor hyperparameter sets.

Question 5

How to visualize Optuna study results?

Accepted Answer

Use Optuna's visualization module with functions like plot_optimization_history or plot_param_importances. Alternatively, the Optuna Dashboard provides a web-based interface for real-time visualization without coding. These tools help analyze trial progress and identify key hyperparameters quickly.

Question 6

How to run Optuna on a cluster?

Accepted Answer

Optuna supports distributed optimization through storage backends like SQLite or Redis. Set up a shared storage for the study, then launch multiple worker processes or nodes that access it. The documentation provides examples with Dask or simple multiprocessing, enabling scaling to many parallel trials.

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