OpenAI: Scaling Kubernetes to 2,500 Nodes

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.

The data infrastructure at your org probably doesn't come anywhere close to a 2,500-node Kubernetes cluster(!), but it's fascinating to know how one of the most bleeding-edge AI research organizations in the world sets up their experimental environments. This stuff is hard.


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