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Deep learning (DL) models have become core modules for many applications.However, deploying these models without careful performance benchmarking thatconsiders both hardware and software's impact often leads to poor service andcostly operational expenditure. To facilitate DL models' deployment, weimplement an automatic and comprehensive benchmark system for DL developers. Toaccomplish benchmark-related tasks, the developers only need to prepare aconfiguration file consisting of a few lines of code. Our system, deployed to aleader server in DL clusters, will dispatch users' benchmark jobs to followerworkers. Next, the corresponding requests, workload, and even models can begenerated automatically by the system to conduct DL serving benchmarks.Finally, developers can leverage many analysis tools and models in our systemto gain insights into the trade-offs of different system configurations. Inaddition, a two-tier scheduler is incorporated to avoid unnecessaryinterference and improve average job compilation time by up to 1.43x(equivalent of 30\% reduction). Our system design follows the best practice inDL clusters operations to expedite day-to-day DL service evaluation efforts bythe developers. We conduct many benchmark experiments to provide in-depth andcomprehensive evaluations. We believe these results are of great values asguidelines for DL service configuration and resource allocation.

InferBench: Understanding Deep Learning Inference Serving with an Automatic Benchmarking System