We’ve been running Kubernetes for deep learning research for over two years. While our largest-scale workloads manage bare cloud VMs directly, Kubernetes provides a fast iteration cycle, reasonable scalability, and a lack of boilerplate which makes it ideal for most of our experiments. We now operate several Kubernetes clusters (some in the cloud and some on physical hardware), the largest of which we’ve pushed to over 2,500 nodes. This cluster runs in Azure on a combination of D15v2 and NC24 VMs. On the path to this scale, many system components caused breakages, including etcd, the Kube masters, Docker image pulls, network, KubeDNS, and even our machines’ ARP caches. We felt it’d be helpful to share the specific issues we ran into, and how we solved them. etcd After passing 500 nodes in our cluster, our researchers started reporting regular timeouts from the kubectl command line tool. We tried adding more Kube masters (VMs running kube-apiserv...
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