Surviving the eviction: How to build interrupt-resilient AI workloads on GKE
You did everything right. You containerized your massive model training job, deployed it to Google Kubernetes Engine (GKE), and cleverly routed it to a Spot VM node pool to save up to 90% on compute costs. Everything is humming along perfectly for 38 hours. Then, a priority on-demand customer needs capacity, Google Cloud reclaims your underlying Spot VM, and your node vanishes. Whether you are using preemptible Spot VMs to save money, or leveraging the Dynamic Workload Scheduler (DWS) to queue...
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