How should the ML engineer configure the SageMaker AMT data types to meet these requirements?
Set Strategy to the Bayesian value.
Set RetryStrategy to a value of 1.
Set ParameterRanges to the narrow range Inferred from previous hyperparameter jobs.
Set TrainingJobEarlyStoppingType to the AUTO value.
Explanations:
Setting the Strategy to Bayesian may improve sampling efficiency but does not directly speed up training jobs or prevent unnecessary jobs from running. It is designed to optimize the search process but won’t necessarily halt jobs that aren’t improving significantly.
Setting the RetryStrategy to a value of 1 does not impact the overall execution time of tuning jobs. This option controls how many times SageMaker retries failed training jobs, which does not directly relate to the speed of tuning or early stopping of non-improving jobs.
Narrowing the ParameterRanges can lead to faster tuning by reducing the search space; however, this option does not directly influence the stopping of jobs that do not improve. It is still possible for the tuning jobs to run without significant improvements, depending on the search strategy employed.
Setting TrainingJobEarlyStoppingType to AUTO allows SageMaker to automatically stop training jobs that are not showing significant improvement against the objective metric. This can significantly speed up the tuning process by preventing unnecessary jobs from running, thereby optimizing resource utilization and time spent on non-beneficial training runs.
From my point of view, the answer is:
Set TrainingJobEarlyStoppingType to the AUTO value.