Kubeflow Pipeline GPU Stalls: 5 Pod Resource Limit Fixes

#kubeflow #kubernetes #gpu #mlops

GPU Pods Show 0% Utilization While Training Jobs Stall

Your Kubeflow pipeline is running, the pod is scheduled, nvidia-smi shows the GPU is attached — but kubectl top pod reports 0% GPU utilization and your training script hasn't moved past epoch 1 in twenty minutes.

This isn't a code bug. It's a resource limit mismatch that Kubernetes won't tell you about until you dig into the pod's resource requests, limits, and actual GPU allocation. The default Kubeflow pipeline component settings assume you're running on a cluster with generous resource headroom. Most of us aren't.

I've seen this break pipelines in three ways: (1) the pod never schedules because the resource request exceeds node capacity, (2) the pod schedules but the container gets OOMKilled mid-training because memory limits are too low, or (3) the pod runs but GPU utilization is throttled because the limit doesn't match the request. Here's what actually fixes it.