Question

You are running a hyperparameter tuning sweep job in Azure Machine Learning to optimize a deep learning model. The sweep uses random sampling over a large search space, and each trial is expensive to run on GPU compute. You want to automatically stop poorly performing trials early to reduce cost, while still allowing promising trials to complete. Which configuration should you add to the sweep job?

Accepted Answer

Configure a bandit early termination policy with a slack factor and evaluation interval

Answer

Set the sampling algorithm to grid sampling instead of random sampling

Answer

Increase max_total_trials so more trials run in parallel

Answer

Set the primary metric goal to minimize and lower the timeout on the compute cluster