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The rapid evolution of deep neural networks is demanding deep learning (DL)frameworks not only to satisfy the requirement of quickly executing largecomputations, but also to support straightforward programming models forquickly implementing and experimenting with complex network structures.However, existing frameworks fail to excel in both departments simultaneously,leading to diverged efforts for optimizing performance and improving usability.  This paper presents JANUS, a system that combines the advantages from bothsides by transparently converting an imperative DL program written in Python,the de-facto scripting language for DL, into an efficiently executable symbolicdataflow graph. JANUS can convert various dynamic features of Python, includingdynamic control flow, dynamic types, and impure functions, into elements of asymbolic dataflow graph. Experiments demonstrate that JANUS can achieve fast DLtraining by exploiting the techniques imposed by symbolic graph-based DLframeworks, while maintaining the simple and flexible programmability ofimperative DL frameworks at the same time.

JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs