Question

Your team is tuning a deep neural network on Vertex AI Vizier hyperparameter tuning. The search space includes learning rate, number of layers, and dropout. Each trial takes about 40 minutes on a single GPU, and you have a fixed budget allowing roughly 60 total trials. You want to maximize validation AUC while avoiding wasting compute on trials that are clearly underperforming early in training. Which configuration best meets these goals?

Accepted Answer

Use the default Bayesian optimization algorithm, enable early stopping (enableTrialEarlyStopping), and run a moderate number of parallel trials to balance exploration and convergence.

Answer

Use a Grid Search algorithm with 60 trials and disable early stopping so every trial trains fully for fair comparison.

Answer

Use Random Search with maxParallelTrialCount set equal to maxTrialCount so all 60 trials run simultaneously, maximizing throughput.

Answer

Use Bayesian optimization but set maxParallelTrialCount equal to maxTrialCount to finish the study as fast as possible.