Distributed TensorFlow training (Google I/O '18)
Автор: TensorFlow
Загружено: 2018-05-11
Просмотров: 37684
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;
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