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：高等研究（HDR）候选人越来越多地依赖云预配的虚拟机和本地 GPU 硬件进行计算实验，但在拥有计算资源与有效使用资源之间，仍存在一个持续且未得到充分解决的差距。云和基础设施团队可以预配虚拟机，但从原始虚拟机到可复现、支持 GPU 的研究环境，对于主要作为领域专家而非系统工程师的研究人员来说，仍是一个重大障碍。我们将这一差距识别为云预配与交互式研究工作之间缺失的适配层。我们提出一个轻量级、开源解决方案，基于 k3s 和 Coder 实现该适配层，该方案已在我们研究工作站环境中积极使用。我们的 CI/CD 流水线将 GitHub 直接连接到本地集群，在五分钟内即可部署研究项目。我们定义了一个具体指标框架来评估该层——涵盖部署延迟、环境可复现性、入职摩擦和资源利用率——并建立可衡量改进效果的基础基线。