Scaling Tensorflow data processing with tf.data (TF Dev Summit '20)

As model training becomes more distributed in nature, tf.data has evolved to be more distribution aware and performant. This talk presents tf.data tools for scaling TensorFlow data processing. In particular: tf.data service that allows your tf.data pipeline to run on a cluster of machines, and tf.data.snapshot that materializes the results to disk for reuses across multiple invocations.

Speaker:
Rohan Jain – Staff Software Engineer

Resources:
GitHub Distributed tf.data service → https://goo.gle/2VrYDi2
tf.data: Build TensorFlow input pipelines → https://goo.gle/2VTnnjk
Better performance with the tf.data API → https://goo.gle/38wyKAy
GitHub tf.data snapshot → https://goo.gle/2v42Ai8

Watch all TensorFlow Dev Summit 2020 sessions → https://goo.gle/TFDS20
Subscribe to the TensorFlow YouTube channel → https://goo.gle/TensorFlow

event: TensorFlow Dev Summit 2020; re_ty: Publish; product: TensorFlow – General; fullname: Rohan Jain;

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