The Missing Adapter Layer for Research Computing

TL;DR

This paper introduces a lightweight, open-source adapter layer built on k3s and Coder that streamlines deploying GPU-ready research environments from cloud resources, reducing setup time and improving reproducibility.

Contribution

It presents a novel, practical solution that bridges the gap between cloud provisioning and research work, with a metrics framework for evaluation.

Findings

Deployment time reduced to under five minutes

Environment reproducibility improved

Onboarding friction minimized

Abstract

Higher Degree by Research (HDR) candidates increasingly depend on cloud-provisioned virtual machines and local GPU hardware for their computational experiments, yet a persistent and under-addressed gap exists between having compute resources and using them productively. Cloud and infrastructure teams can provision virtual machines, but the path from a raw VM to a reproducible, GPU-ready research environment remains a significant barrier for researchers who are domain experts, not systems engineers. We identify this gap as a missing adapter layer between cloud provisioning and interactive research work. We present a lightweight, open-source solution built on k3s and Coder that implements this adapter layer and is already in active use in our research workspace environment. Our CI/CD pipeline connects GitHub directly to the local cluster, deploying research projects in under five minutes. We define a concrete metrics framework for evaluating this layer -- covering deployment latency, environment reproducibility, onboarding friction, and resource utilisation -- and establish baselines against which improvements can be measured.