VirtualFlow: Decoupling Deep Learning Model Execution from Underlying Hardware

20 Sep 2020 Andrew Or Haoyu Zhang Michael J. Freedman

State-of-the-art deep learning systems tightly couple model execution with the underlying hardware. This coupling poses important challenges in a world where the scale of deep learning workloads is growing rapidly: workloads with high resource requirements are inaccessible to most users, experimentation on smaller test beds is impossible, and results are difficult to reproduce across different hardware... (read more)

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