We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Dissecting tf.function to discover AutoGraph strengths and subtleties

Formal Metadata

Title
Dissecting tf.function to discover AutoGraph strengths and subtleties
Title of Series
Number of Parts
118
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
AutoGraph is one of the most exciting new features of Tensorflow 2.0: it allows transforming a subset of Python syntax into its portable, high-performance and language agnostic graph representation bridging the gap between Tensorflow 1.x and the 2.0 release based on eager execution. Using AutoGraph with the code@tf.fuction/code decorator seems easy, but in practice, writing efficient and correctly graph-convertible code requires to know in detail how AutoGraph and tf.function work. In particular, knowing how: A graph is created and when it is re-used; To deal with functions that create a state; To correctly use the Tensorflow codetf.Tensor/code object instead of using the Python native types to speed-up the computation; defines the minimum skill-set required to write correct graph-accelerable code. The talk will guide you trough AutoGraph and codetf.function/code highlighting all the peculiarities that are worth knowing to build the right skill-set.
Keywords