A data scientist is tuning a deep learning object-detection model and must run many parallel hyperparameter trials, stop poorly performing trials early, and allocate more resources to promising trials. Which hyperparameter search technique will minimize total compute time while meeting these requirements?

Hyperband is the most suitable technique because it efficiently allocates resources by running many configurations for a short time, then pruning poorly performing ones and allocating more resources to promising configurations. This iterative approach minimizes total compute time while meeting the requirements of parallel trials, early stopping, and resource allocation. Grid search and random search do not incorporate early stopping or adaptive resource allocation, making them less efficient for this scenario. Bayesian optimization is effective for finding optimal hyperparameters but typically doesn't include the aggressive early stopping and resource allocation mechanisms of Hyperband, which are crucial for minimizing compute time in this context.