TensorFlow AutoGraph automatically converts plain Python code into its 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.
Brian Lee and Priya Gupta demonstrate how to distribute your training in TensorFlow easily across multiple accelerators and machines. They also offer an overview of the new DistributionStrategy class that you can now use with Keras and Estimator APIs and cover the programming abstractions that allow you to run your models on CPUs, GPUs, and Cloud TPU from single devices up to entire Cloud TPU pods.
This session is sponsored by Google.
Brian Lee is a software engineer at Google Brain. He works on AutoGraph, a tool for converting plain Python code into its TensorFlow equivalent, and MiniGo, an open source replication of AlphaGoZero. Previously, Brian worked at Verily Life Sciences.
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.
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