Neural Networks (NNs) serve as the backbone for various applications, including computer vision, speech recognition, and natural language processing. Due to their iterative nature, training NNs is a highly compute-intensive task that is typically executed using a statically allocated set of devices (e.g., CPUs or GPUs). This static allocation prevents adjusting priorities, making it impossible to reassign resources to urgent tasks and potentially causing high-priority training jobs to miss their expected completition times.

This paper proposes DECOR-NN (DEadline COnstrained Resource allocation for Neural Networks), a control mechanism for NN training that dynamically allocates resources according to a user-defined deadline (i.e., a Service Level Agreement), ensuring the training phase completes within the specified time. The solution leverages control theory and has been developed on top of PyTorch, a widely-used framework for training NNs. DECOR-NN dynamically allocates either GPUs or fractions of CPUs to meet user deadlines and also allows users to modify the deadline at runtime to accommodate changes in job priorities. A comprehensive empirical evaluation using three benchmark applications demonstrates that DECOR-NN successfully completes training jobs with an average deviation from the deadline of only 1.75%.