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.

翻译：研究型高等学位（HDR）候选人日益依赖云配置虚拟机与本地GPU硬件开展计算实验，然而从获得计算资源到高效利用之间始终存在一个未被充分关注的关键断层。云端与基础设施团队可配置虚拟机，但原始虚拟机要转变为可复现、支持GPU的研究环境，这对身为领域专家而非系统工程师的研究人员而言仍是重大障碍。我们将其定位为云配置与交互式研究工作之间缺失的适配层。本文提出基于k3s和Coder的轻量级开源解决方案，该方案已在我们研究工作站环境中得到实际应用。我们的CI/CD流水线将GitHub直接连接至本地集群，可在五分钟内完成研究项目部署。我们定义了评估该适配层的具体指标框架（涵盖部署延迟、环境可复现性、新人上手难度及资源利用率），并建立了可供改进基准参照的基线标准。