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arXiv:1903.01855cs.PL

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. TensorFlow, which TensorFlow Eager extends, requires users to represent computations as dataflow graphs; this permits compiler optimizations and simplifies deployment but hinders rapid prototyping and run-time dynamism. TensorFlow Eager eliminates these usability costs without sacrificing the benefits furnished by graphs: It provides an imperative front-end to TensorFlow that executes operations immediately and a JIT tracer that translates Python functions composed of TensorFlow operations into executable dataflow graphs. TensorFlow Eager thus offers a multi-stage programming model that makes it easy to interpolate between imperative and staged execution in a single package.

arXiv comments
Journal reference
Proc. of the 2nd SysML Conference, 2019
DOI
Subjects
cs.PLProgramming Languages cs.LGMachine Learning

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Mar 2021 to Mar 2021. Found in 1 HN submission from Mar 2019 to Mar 2019. Open submission discussions.

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Discussed in comments1 thread

  1. A Year at Google Brain (2020) 1 comment

    • This guy is [1], if I found the right paper. Though the order appears to be alphabetical. [1] https://arxiv.org/abs/1903.01855

      EvgeniyZhView on HNParent context

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