This extensive survey deals with the challenges and visions of deploying deep learning workloads in GPU datacenters. Understanding these key points and strategic recommendations includes:

• The development of DL models demands high computational resources, GPU datacenters facilitate this need.
• The importance of tailored scheduling approaches to maximize resource utilization.
• Current technologies are behind in supporting dynamic DL workloads efficiently.

** Future Perspectives **

• Development of adaptive scheduling algorithms.
• Integration of real-time analytics for better workload distribution.
• Enhanced framework designs to support next-gen DL models.

This paper shows the importance of specific scheduler designs which significantly reduce operational costs and optimize resource utilization. Such insights pave the way for future research in adaptive and predictive models for more intricate DL tasks.