A Data-Centric Optimization Framework for Machine Learning. (arXiv:2110.10802v2 [cs.LG] UPDATED)

Rapid progress in deep learning is leading to a diverse set of quickly
changing models, with a dramatically growing demand for compute. However, as
frameworks specialize performance optimization to patterns in popular networks,
they implicitly constrain novel and diverse models that drive progress in
research. We empower deep learning researchers by defining a flexible and
user-customizable pipeline for optimizing training of arbitrary deep neural
networks, based on data movement minimization. The pipeline begins with
standard networks in PyTorch or ONNX …

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