August 2018
Intermediate to advanced
272 pages
7h 2m
English
book
Hands-On Convolutional Neural Networks with TensorFlow
by Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Content preview from Hands-On Convolutional Neural Networks with TensorFlow
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Distributed computing in TensorFlow
In this section, you will learn how to distribute computation in TensorFlow; the importance of knowing how to do this is highlighted as follows:
- Run more experiments in parallel (namely, finding hyperparameters, for example, gridsearch)
- Distribute model training over multiple GPUs (on multiple servers) to reduce training time
One famous use case was when Facebook published a paper that was able to train ImageNet in 1 hour (instead of weeks). Basically, it trained a ResNet-50 on ImageNet on 256 GPUs, distributed on 32 servers, with a batch size of 8,192 images.
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