Which approach will run the new hyperparameter tuning job in the LEAST amount of time?
Use a warm start hyperparameter tuning job.
Use a checkpointing hyperparameter tuning job.
Use the same random seed for the hyperparameter tuning job.
Use multiple jobs in parallel for the hyperparameter tuning job.
Explanations:
A warm start hyperparameter tuning job allows SageMaker to reuse previous tuning results to speed up the process. Since the new hyperparameter range includes the previous range, a warm start can help the system build on the earlier search, minimizing the time needed for optimization.
Checkpointing involves saving intermediate results and resuming the job from those checkpoints, but it doesn’t directly relate to speeding up the process of tuning in the context of adjusting hyperparameter ranges. It helps in long-running jobs but is less relevant for optimizing the new metric and expanded range in this scenario.
Using the same random seed might produce similar results across multiple runs, but it doesn’t leverage prior optimization or reuse past results, so it does not reduce the tuning time when extending the hyperparameter search.
Running multiple jobs in parallel can speed up the search for optimal hyperparameters but doesn’t directly optimize the tuning process or reuse previous results, and thus may not be the most time-efficient approach in this specific context.