  Efficient GPU resource scheduling is essential to maximize resource
utilization and save training costs for the increasing amount of deep learning
workloads in shared GPU clusters. Existing GPU schedulers largely rely on
static policies to leverage the performance characteristics of deep learning
jobs. However, they can hardly reach optimal efficiency due to the lack of
elasticity. To address the problem, we propose ONES, an ONline Evolutionary
Scheduler for elastic batch size orchestration. ONES automatically manages the
elasticity of each job based on the training batch size, so as to maximize GPU
utilization and improve scheduling efficiency. It determines the batch size for
each job through an online evolutionary search that can continuously optimize
the scheduling decisions. We evaluate the effectiveness of ONES with 64 GPUs on
TACC's Longhorn supercomputers. The results show that ONES can outperform the
prior deep learning schedulers with a significantly shorter average job
completion time.
