We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E. By Benjamin ChessEric Sigler.
Before we get too far, it’s important to describe our workload. The applications and hardware we run with Kubernetes are pretty different from what you may encounter at a typical company. Our problems and corresponding solutions may, or may not, be a good match to your own setup!
The article describes in detail:
- Our workload
- API Servers
- Time-series metrics with Prometheus and Grafana
- Quotas & resource usage
- Gang scheduling
One big strain on API Servers was WATCHes on Endpoints. There are a few services, such as ‘kubelet’ and ‘node-exporter’ of which every node in the cluster is a member. When a node would be added or removed from the cluster, this WATCH would fire. And because typically each node itself was watching the kubelet service via kube-proxy, the # and bandwidth required in these responses would be N^2 and enormous, occasionally 1GB/s or more. EndpointSlices, launched in Kubernetes 1.17, were a huge benefit that brought this load down 1000x.
We’ve found Kubernetes to be an exceptionally flexible platform for our research needs. It has the ability to scale up to meet the most demanding workloads we’ve put on it. There are many areas yet though where it needs improvement, and the Supercomputing team at OpenAI will continue to explore how Kubernetes can scale. Nice one![Read More]