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Lowering costs by driving high utilization across deep learning workloads isa crucial lever for cloud providers. We present Singularity, Microsoft'sglobally distributed scheduling service for highly-efficient and reliableexecution of deep learning training and inference workloads. At the heart ofSingularity is a novel, workload-aware scheduler that can transparently preemptand elastically scale deep learning workloads to drive high utilization withoutimpacting their correctness or performance, across a global fleet of AIaccelerators (e.g., GPUs, FPGAs).  All jobs in Singularity are preemptable, migratable, and dynamicallyresizable (elastic) by default: a live job can be dynamically and transparently(a) preempted and migrated to a different set of nodes, cluster, data center ora region and resumed exactly from the point where the execution was preempted,and (b) resized (i.e., elastically scaled-up/down) on a varying set ofaccelerators of a given type. Our mechanisms are transparent in that they donot require the user to make any changes to their code or require using anycustom libraries that may limit flexibility. Additionally, our approachsignificantly improves the reliability of deep learning workloads. We show thatthe resulting efficiency and reliability gains with Singularity are achievedwith negligible impact on the steady-state performance. Finally, our designapproach is agnostic of DNN architectures and handles a variety of parallelismstrategies (e.g., data/pipeline/model parallelism).

Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads