What orchestration strategy would best ensure efficient training of multiple deep learning models in a shared GPU cluster?

Implementing a priority-based scheduling system that allocates more GPUs to high-priority models is particularly effective for ensuring efficient training of multiple deep learning models in a shared GPU cluster. This approach allows resource allocation to be done according to the importance or urgency of the tasks at hand. By prioritizing certain models, especially those that may be more critical for business objectives or have tighter deadlines, the system ensures that these models receive adequate resources to complete training in a timely manner. This form of orchestration can balance the demand for GPU resources among various models, preventing bottlenecks and optimizing overall utilization of the cluster.

Additionally, this strategy accommodates variations in model complexity and resource requirements. Some deep learning models may inherently demand more computational power due to their architecture, size, or the nature of the datasets they're trained on. Prioritizing these models not only enhances training efficiency but also allows for better resource management, as the system can dynamically adjust allocations based on real-time workloads and priorities.

In contrast, randomly assigning GPU resources or using a first-come, first-served policy can lead to inefficiencies. These methods tend to treat all tasks equally, which may result in less critical models utilizing resources that could have been better allocated to more significant models. Assigning equal