Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is impractical in HPC environments due to compute intensity and resource constraints. We present a workflow framework that enables asynchronous human-AI collaboration across hybrid infrastructures, including HPC clusters, local machines, and cloud platforms. Workflows can pause at defined checkpoints for human input without halting underlying compute jobs, preventing idle resources and enabling non-blocking supervision. The framework supports interaction with SLURM-based scheduling, containerized and native tasks, and is customized for scenarios requiring human judgment and adaptability. We demonstrate its application in model training on systems like MareNostrum 5, highlighting benefits in portability, efficiency, and oversight in operational AI workflows.

翻译：在高风险的国防与安全领域，人工智能系统的训练与部署离不开人类参与。然而，在高性能计算环境中，由于计算密集度和资源限制，实时交互并不现实。我们提出一个工作流框架，支持在包括HPC集群、本地机器和云平台在内的混合基础设施上实现异步人机协作。该工作流可在指定检查点暂停以等待人工输入，而无需中断底层计算任务，从而避免资源闲置并实现非阻塞式监督。该框架兼容基于SLURM的调度系统、容器化任务及原生任务，并针对需要人类判断与适应性的场景进行了定制。我们展示了其在MareNostrum 5等系统上的模型训练应用，凸显了可移植性、效率及操作监督方面的优势。