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Reproducing, comparing and reusing results from machine learning and systemspapers is a very tedious, ad hoc and time-consuming process. I will demonstratehow to automate this process using open-source, portable, customizable andCLI-based Collective Knowledge workflows and pipelines developed by thecommunity. I will help participants run several real-world non-virtualized CKworkflows from the SysML'19 conference, companies (General Motors, Arm) andMLPerf benchmark to automate benchmarking and co-design of efficientsoftware/hardware stacks for machine learning workloads. I hope that ourapproach will help authors reduce their effort when sharing reusable andextensible research artifacts while enabling artifact evaluators toautomatically validate experimental results from published papers in a standardand portable way.

SysML'19 demo: customizable and reusable Collective Knowledge pipelines to automate and reproduce machine learning experiments