[Submitted on 25 Mar 2026 (v1), last revised 21 Jul 2026 (this version, v3)]

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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 separates having compute resources from using them productively. Cloud and infrastructure teams can provision a virtual machine in minutes, 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 argue that this gap is not a shortcoming of any particular tool but a missing architectural layer: an adapter layer that bridges cloud provisioning and interactive research work. We present a lightweight, open-source implementation of this layer, built on k3s and Coder and already in active use in our research workspace environment. A CI/CD pipeline connects GitHub directly to the local cluster, carrying a research project from commit to a running, accessible workspace in under five minutes. We then define a concrete metrics framework for evaluating any adapter layer covering deployment latency, environment reproducibility, onboarding friction, and resource utilisation and establish baselines against which improvements can be measured.

Submission history

From: Bowen Li [view email]
[v1] Wed, 25 Mar 2026 05:04:34 UTC (98 KB)
[v2] Tue, 31 Mar 2026 07:22:47 UTC (98 KB)
[v3] Tue, 21 Jul 2026 01:21:33 UTC (2,366 KB)