October 28–31, 2019
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Performant, scalable models in TensorFlow 2.0 with tf.data, tf.function, and tf.distribute

Taylor Robie (Google), Priya Gupta (Google)
2:30pm3:10pm Wednesday, October 30, 2019
Location: Great American Ballroom J/K
Average rating: ****.
(4.25, 4 ratings)

Who is this presentation for?

  • Anyone who needs a lot of compute for their ML projects




TensorFlow’s tf.distribute library helps you scale your model from a single GPU to multiple GPUs and to multiple machines using simple APIs that require very few changes to your existing code.

Join Taylor Robie and Priya Gupta to learn how you can use tf.distribute to scale your machine learning model on a variety of hardware platforms ranging from commercial cloud platforms to dedicated hardware. You’ll learn tools and tips to get the best scaling for your training in TensorFlow.

Prerequisite knowledge

  • Familiarity with TensorFlow

What you'll learn

  • Learn how to distribute TensorFlow using best practices in 2.0 on a variety of equipment
Photo of Taylor Robie

Taylor Robie


Taylor Robie is software engineer at Google, where he’s a member of the TensorFlow high-level APIs team focusing on performance with a particular emphasis on out-of-the-box performance of Keras. Previously, he was a maintainer of the TensorFlow official models repository and optimized several of the Google MLPerf submissions.

Photo of Priya Gupta

Priya Gupta


Priya Gupta is a software engineer on the TensorFlow team at Google, where she works on making it easier to run TensorFlow in a distributed environment. She’s passionate about technology and education and wants machine learning to be accessible to everyone. Previously, she worked at Coursera and on the mobile ads team at Google.

  • O'Reilly
  • TensorFlow
  • Google Cloud
  • IBM
  • Databricks
  • Tensor Networks
  • VMware
  • Amazon Web Services
  • One Convergence
  • Quantiphi
  • Lambda Labs
  • Tech Mahindra
  • cnvrg.io
  • Determined AI
  • Inferencery
  • Manceps, Inc.
  • PerceptiLabs
  • Valohai

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