Distributed TensorFlow training (Google I/O '18)

To efficiently train machine learning models, you will often need to scale your training to multiple GPUs, or even multiple machines. TensorFlow now offers rich functionality to achieve this with just a few lines of code. Join this session to learn how to set this up.

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Distribution Strategy API:

ResNet50 Model Garden example with MirroredStrategy API:

Performance Guides:

Commands to set up a GCE instance and run distributed training:

Multi-machine distributed training with train_and_evaluate:

Watch more TensorFlow sessions from I/O ’18 here → https://goo.gl/GaAnBR
See all the sessions from Google I/O ’18 here → https://goo.gl/q1Tr8x

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#io18 event: Google I/O 2018; re_ty: Publish; product: TensorFlow – General; fullname: Priya Gupta, Anjali Sridhar; event: Google I/O 2018;

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