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

arXiv: https://arxiv.org/abs/1812.01329 · HTML: https://mlsanity.com/paper/1812.01329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [Query Processing on Tensor Computation Runtimes](https://mlsanity.com/paper/2203.01877/) (2022)
- [Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs](https://mlsanity.com/paper/2201.09210/) (2022)
- [ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time](https://mlsanity.com/paper/2305.10611/) (2023)
- [Trident: Unifying Guarded Dispatch and Host Execution for PyTorch Triton Workloads](https://mlsanity.com/paper/2609.37241/) (2026)

## Abstract (arXiv)

The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to support straightforward programming models for quickly 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 both sides by transparently converting an imperative DL program written in Python, the de-facto scripting language for DL, into an efficiently executable symbolic dataflow graph. JANUS can convert various dynamic features of Python, including dynamic control flow, dynamic types, and impure functions, into elements of a symbolic dataflow graph. Experiments demonstrate that JANUS can achieve fast DL training by exploiting the techniques imposed by symbolic graph-based DL frameworks, while maintaining the simple and flexible programmability of imperative DL frameworks at the same time.