The high resource demand of deep learning training (DLT) workloads necessitates the design of efficient schedulers. While most existing schedulers expedite DLT workloads by considering GPU sharing and elastic training, they neglectlayer elasticity, which dynamically freezes certain layers of a network. This technique has been shown to significantly speed up individual workloads. In this paper, we explore how to incorporatelayer elasticityinto DLT scheduler designs to achieve higher cluster-wide efficiency. A key factor that hinders the application of layer elasticity in GPU clusters is the potential loss in model accuracy, making users reluctant to enable layer elasticity for their workloads. It is necessary to have an efficient layer-elastic system, which can well balance training accuracy and speed for layer elasticity. We introduceIceFrog, the first scheduling system that utilizes layer elasticity to improve the efficiency of DLT workloads in GPU clusters. It achieves this goal with superior algorithmic designs and intelligent resource management. In particular, (1) we model the frozen penalty and layer-aware throughput to measure the effective progress metric of layer-elastic workloads. (2) We design a novel scheduler to further improve the efficiency of layer elasticity. We implement and deployIceFrogin a physical cluster of 48 GPUs. Extensive evaluations and large-scale simulations show thatIceFrogreduces average job completion times by 36-48% relative to state-of-the-art DL schedulers.