Read through the homepage, but not entirely sure --

Why not just train on Spot Instances with a retry implemented?

I see that SpotML has a configurable fall back to On-Demand instances, and perhaps their value prop is that it saves the state of your run up to the interruption + resumes it on the On-Demand instance, but why not just set a retry on the Spot Instance if its interrupted?

I'm failing to see what is different about SpotML vs Metaflow's @retry decorator and using AWS Batch: https://docs.metaflow.org/metaflow/failures#retrying-tasks-w...

If you're in the comment still, Vishnu, would love to hear your thoughts