  Deep learning (DL) shows its prosperity in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure.
Hence, dedicated GPU accelerators have been collectively constructed into a GPU
datacenter. An efficient scheduler design for such GPU datacenter is crucially
important to reduce the operational cost and improve resource utilization.
However, traditional approaches designed for big data or high performance
computing workloads can not support DL workloads to fully utilize the GPU
resources. Recently, substantial schedulers are proposed to tailor for DL
workloads in GPU datacenters. This paper surveys existing research efforts for
both training and inference workloads. We primarily present how existing
schedulers facilitate the respective workloads from the scheduling objectives
and resource consumption features. Finally, we prospect several promising
future research directions. More detailed summary with the surveyed paper and
code links can be found at our project website:
https://github.com/S-Lab-System-Group/Awesome-DL-Scheduling-Papers
