Fair Scheduling of Parameter-Efficient Fine-Tuning Jobs on Shared GPU Clusters

Abstract

We study scheduling for clusters shared by many small fine-tuning jobs and propose a preemption-aware scheduler that improves median job completion time by 2.3× on the institute’s cluster traces, which we release.