Alexandre Passos and Frank Chen offer an overview of TensorFlow AutoGraph, which automatically converts plain Python code into the TensorFlow equivalent, using source code transformation. This approach complements the new TensorFlow Eager project and will allow using the imperative style of Eager mode while retaining the benefits of graph mode. By using automatic code conversion, developers can write code that’s more concise, efficient, and robust.
Alexandre and Frank then lead a technical deep dive into Google’s Cloud TPU accelerators and show you how to program them. They cover the programming abstractions that allow you to run your models on CPUs, GPUs, and Cloud TPUs, from single devices up to entire Cloud TPU pods.
This session is sponsored by Google.
Alexandre Passos is a software engineer on the TensorFlow team at Google, where most recently he worked on Eager execution and related usability projects. He studied at UMass under Andrew McCallum.
Frank Chen is a software engineer on the Google Brain team at Google, working to help make TensorFlow and TPUs faster and easier to use. Previously, he was one of the founding software engineers at Coursera, where he worked on online education platforms. When not working, Frank enjoys photography and musical theater and has seen over 30 Broadway shows. He holds both a bachelor’s and master’s degree in computer science from Stanford.
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