The Importance of Machine Learning Training Run on CPU

The calibration set is typically one in done or one and the rest are very efficient, because often those workflows will be similar. The manual optimization step, you can write a script similar to what I have that would kick off a job, get runtime SLAs, and then return them to the user. So if you write code for that, it's pretty scalable. But if you're actually kicking them off manually, you just have like 15 tabs open corresponding to each job and you press play as soon as one completes. So it's not very scalable, but it's also not incredibly time consuming. It's more of a data gathering portion. And are you able to do all job types