EPISODE · Jan 26, 2026 · 39 MIN
Karpenter Lifecycle: How GPU Pods Get Unstuck
from DevOps & Cloud Interview Questions and Answers - Part 1 · host devopsinterviewcloud
A pending ML training job needing 8 GPUs is a classic Karpenter interview scenario — here's the exact four-step lifecycle an interviewer expects you to walk through. You'll learn: Why the K8s scheduler marks pods unschedulable and how Karpenter's controller watches for that signal How Karpenter evaluates all pod constraints at once — resource requests, nodeSelector, nodeAffinity, tolerations, and topology spread How it calls the EC2 API to select the right instance (p3.16xlarge for 8 GPUs) in the correct availability zone Why Karpenter provisions the node but the K8s scheduler still does the final pod binding — a gotcha that trips up a lot of candidates Keywords: Karpenter node provisioning, Kubernetes GPU scheduling, pending pods interview question, Karpenter vs cluster autoscaler, K8s scheduler lifecycle 🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
What this episode covers
A pending ML training job needing 8 GPUs is a classic Karpenter interview scenario — here's the exact four-step lifecycle an interviewer expects you to walk through.You'll learn:Why the K8s scheduler marks pods unschedulable and how Karpenter's controller watches for that signalHow Karpenter evaluates all pod constraints at once — resource requests, nodeSelector, nodeAffinity, tolerations, and topology spreadHow it calls the EC2 API to select the right instance (p3.16xlarge for 8 GPUs) in the correct availability zoneWhy Karpenter provisions the node but the K8s scheduler still does the final pod binding — a gotcha that trips up a lot of candidatesKeywords: Karpenter node provisioning, Kubernetes GPU scheduling, pending pods interview question, Karpenter vs cluster autoscaler, K8s scheduler lifecycle🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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Karpenter Lifecycle: How GPU Pods Get Unstuck
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