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Date of Award
Scientific workflow has become a common practice for scientists to effectively formalize and structure complex scientific processes, which in turn has accelerated scientific discoveries in numerous research fields. With the recent thriving of deep learning in broad scientific projects, there is a rising need for deep learning support in scientific workflow infrastructures SWFMSs. However, current GPU-enabled deep learning frameworks are developed separately, not suitable for direct exploitation in SWFMSs, which forces scientists to handle deep learning outside of SWFMSs and then integrate in workflows in an ad-hoc manner. What workflow users pressingly need today is a user-friendly and well-integrated SWFMS to facilitate GPU-enabled deep learning as native workflows so that they can conveniently design, train, reuse, and share deep learning models. In this dissertation, We demonstrate our research outcome in supporting GPU-enabled deep learning at infrastructure-level in a popular SWFMS - DATAVIEW, which facilitates: 1) fast design, train and reuse neural networks as native workflows per Deep-Learning-as-a-Workflow (DLaaW) via JAVA API or WebBench GUI; 2) flexibly leverage various types of GPU resources for executing deep learning workflows. 3) conveniently integrate NNTasks with ordinary Tasks in one comprehensive workflow through JAVA API or in web interface. Our approach and implementations are thoroughly evaluated through experiments that demonstrate the efficacy and efficiency as compared to conventional PyTorch-based and Keras-based implementations.
Liu, Junwen, "Deep Learning As Native Scientific Workflows In The Modern Swfms - Dataview" (2022). Wayne State University Dissertations. 3638.