Hyperparameter optimization (HPO) is essential for achieving state-of-the-art performance in machine learning, yet it is computationally demanding, particularly on shared or resource-constrained clusters. We present a system that integrates the Asynchronous Successive Halving Algorithm (ASHA) with SEML, the SLURM Experiment Management Library - an experiment orchestration layer that provides declarative configuration, provenance, metric tracking, and robust SLURM job management. The resulting open-source tool enables scalable, fault-tolerant HPO on SLURM-managed infrastructure: SEML handles experiment specification, versioning, and scheduling, while ASHA performs asynchronous early stopping and resource reallocation to concentrate computation on promising configurations. Overall, the system streamlines experiment lifecycle management, enables distributed evaluations with minimal manual effort, and reduces the time required to reach high-quality configurations compared to conventional Grid and Random Search methods under similar compute budgets.
Chattopadhyay et al. (Thu,) studied this question.