We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models. Scaling a single Kubernetes cluster to this size is rarely done and requires some special care, but the upside is a simple infrastructure that allows our machine learning research teams to move faster and scale up without changing their code.
Since our last post on Scaling to 2,500 Nodes we’ve continued to grow our infrastructure to meet researcher needs, in the process learning many additional lessons. This …
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