BentoML enables production deployment of DeepFloyd IF with multi-stage Runners on Kubernetes

AI Impact Summary

This article demonstrates packaging DeepFloyd IF into a BentoML Bento for production, illustrating how a Hugging Face Hub model can be served via BentoML Runners across three diffusion stages. It highlights explicit per-stage GPU allocation and multi-GPU orchestration using start-server.py, enabling scalable inference on Kubernetes with Yatai. Operational considerations include large model artifacts (tens of GBs per stage), dependency management via requirements.txt, and the need to login to Hugging Face Hub to download models into the BentoML Model Store. Teams should plan GPU quotas, image sizes, and monitoring when migrating these multi-stage pipelines to production.