December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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An example of distributed training for MNIST is available online on https://github.com/ischlag/distributed-tensorflow-example/blob/master/example.py
In addition, note that You can decide to have more than one parameter server for efficiency reasons. Using parameters the server can provide better network utilization, and it allows to scale models to more parallel machines. It is possible to allocate more than one parameter server. The interested reader can have a look to https://www.tensorflow.org/deploy/distributed
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.