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HPC is an enabling platform for AI. The introduction of AI workloads in theHPC applications basket has non-trivial consequences both on the way ofdesigning AI applications and on the way of providing HPC computing. This isthe leitmotif of the convergence between HPC and AI. The formalized definitionof AI pipelines is one of the milestones of HPC-AI convergence. If wellconducted, it allows, on the one hand, to obtain portable and scalableapplications. On the other hand, it is crucial for the reproducibility ofscientific pipelines. In this work, we advocate the StreamFlow WorkflowManagement System as a crucial ingredient to define a parametric pipeline,called "CLAIRE COVID-19 Universal Pipeline," which is able to explore theoptimization space of methods to classify COVID-19 lung lesions from CT scans,compare them for accuracy, and therefore set a performance baseline. Theuniversal pipeline automatizes the training of many different Deep NeuralNetworks (DNNs) and many different hyperparameters. It, therefore, requires amassive computing power, which is found in traditional HPC infrastructurethanks to the portability-by-design of pipelines designed with StreamFlow.Using the universal pipeline, we identified a DNN reaching over 90% accuracy indetecting COVID-19 lesions in CT scans.

Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 AI diagnosis