@Dmitri @aguo I’m on Ray 1.12.0
My head pod has pretty decent size with 20G memory and 12 CPUs. It’s not caused by the head pod resource constraint. I configured my head node to not schedule the workloads on it. It’s the python process that runs the dashboard application, which is running at 100% capacity all the time. The job I’m running is quite big that triggers 20K tasks in parallel with 25 workers (8 CPUs and 32G memory)