Paper Info
Title
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning.
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.
Year
Venue
DocType
2019
arXiv: Programming Languages
Journal
Volume
ISSN
Citations 
abs/1903.01855
Proc. of the 2nd SysML Conference, 2019
0
PageRank 
References 
Authors
0.34
15
11
Name
Order
Citations
PageRank
Akshay Agrawal1263.71
Akshay Naresh Modi2291.48
Passos, Alexandre34083167.18
Allen Lavoie400.68
Ashish Agarwal5111067.41
Asim Shankar620.70
Igor Ganichev711.03
Josh Levenberg8144446.60
Mingsheng Hong9372.15
Rajat Monga10165460.93
Shanqing Cai1111.04