What's happening in Kubernetes to support AI workloads

This AWS-authored CNCF post explains how Kubernetes' separation between the scheduler (which places pods on existing nodes) and the autoscaler (which adds nodes) can leave GPU pods pending even when capacity exists, because the node the autoscaler would add is not one the scheduler can use. It describes how Dynamic Resource Allocation gives both loops a shared language for topology and device sharing, why that shared vocabulary is not enough for gang scheduling, and how SIG-Autoscaling proposals aim to let the scheduler see and request provisionable capacity.