What Is a Container, Really? Five Years of GPU Infrastructure (www.beam.cloud)

A tech company has evolved its GPU infrastructure over five years from a basic cloud IDE for machine learning to a sophisticated system that efficiently deploys models using containers. Initially built on Amazon ECS, the architecture struggled with performance issues like the cold-start problem. This prompted a shift to Kubernetes and Knative, but persistent latency with container start times and request handling led the team to rethink their approach. By developing a new container runtime that utilizes FUSE (Filesystem in Userspace), they enabled lazy loading of container files, drastically reducing startup times to subsecond levels. Their innovative system allows model images to be accessed and cached dynamically without needing to store entire images locally, enhancing efficiency across users by leveraging shared resources. The final version, V2, indexed within individual layers of container images, improving build times and optimizing request handling, solidifying the infrastructure’s performance and reliability for handling large inference workloads. This represents a significant leap forward for the AI/ML community, as it streamlines the deployment process of complex models and enhances scalability.