That’s cool. I was wondering how this compares to ray (which I use with my institutions slurm-based clusters). The scheduler system that determined.ai has seems

That’s cool. I was wondering how this compares to ray (which I use with my institutions slurm-based clusters). The scheduler system that determined.ai has seems a lot more granular which suits the workloads you get with a team of people doing a bunch of deep learning model prototyping. Our debug queue has a five minute preempt time which sometimes adds a lot of friction for quick debugging iteration when utilization is maxed out