February 2017
Intermediate to advanced
370 pages
8h 11m
English
Content preview from TensorFlow Machine Learning CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Using Multiple Executors
It should be apparent to the reader that there are many features of TensorFlow and computational graphs that lend itself naturally to being computed in parallel. The computational graph can be broken up on different processors as well as processing different batches. We will address how to access different processors on the same machine in this recipe.
Getting ready
For this recipe, we will show how to access multiple devices on the same system and train on them. This is a very common occurrence, as along with a CPU, a machine may have one or more GPUs that can share the computationl load. If TensorFlow can access these devices, it will automatically distribute the computations to the multiple devices via a greedy process. ...
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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.