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Machine Learning Using TensorFlow Cookbook

You're reading from   Machine Learning Using TensorFlow Cookbook Create powerful machine learning algorithms with TensorFlow

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800208865
Length 416 pages
Edition 1st Edition
Languages
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Authors (3):
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Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Alexia Audevart Alexia Audevart
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Alexia Audevart
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with TensorFlow 2.x 2. The TensorFlow Way FREE CHAPTER 3. Keras 4. Linear Regression 5. Boosted Trees 6. Neural Networks 7. Predicting with Tabular Data 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Transformers 11. Reinforcement Learning with TensorFlow and TF-Agents 12. Taking TensorFlow to Production 13. Other Books You May Enjoy
14. Index

Using multiple executors

You will be aware that there are many features of TensorFlow, including computational graphs that lend themselves naturally to being computed in parallel. Computational graphs can be split over different processors as well as in processing different batches. We will address how to access different processors on the same machine in this recipe.

Getting ready

In this recipe, we will show you how to access multiple devices on the same system and train on them. A device is a CPU or an accelerator unit (GPUs, TPUs) where TensorFlow can run operations. This is a very common occurrence: along with a CPU, a machine may have one or more GPUs that can share the computational load. If TensorFlow can access these devices, it will automatically distribute the computations to multiple devices via a greedy process. However, TensorFlow also allows the program to specify which operations will be on which device via a name scope placement.

In this recipe, we will...

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