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. Rate this session by signing-in on the I/O website here → https://goo.gl/sBZMEm Distribution Strategy API: https://goo.gl/F9vXqQ https://goo.gl/Zq2xvJ ResNet50 Model Garden example with MirroredStrategy API: https://goo.gl/3UWhj8 Performance Guides: https://goo.gl/doqGE7 https://goo.gl/NCnrCn Commands to set up a GCE instance and run distributed training: https://goo.gl/xzwN4C Multi-machine distributed training with train_and_evaluate: https://goo.gl/kyikAC 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 Subscribe to the TensorFlow channel → https://goo.gl/ht3WGe #io18 event: Google I/O 2018; re_ty: Publish; product: TensorFlow - General; fullname: Priya Gupta, Anjali Sridhar; event: Google I/O 2018;