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

arXiv · 1903.01855

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

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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.

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Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai. 2019-02-27. TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning. https://arxiv.org/abs/1903.01855

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