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Learn TensorFlow Enterprise

You're reading from   Learn TensorFlow Enterprise Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800209145
Length 314 pages
Edition 1st Edition
Languages
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Author (1):
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KC Tung KC Tung
Author Profile Icon KC Tung
KC Tung
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1 – TensorFlow Enterprise Services and Features
2. Chapter 1: Overview of TensorFlow Enterprise FREE CHAPTER 3. Chapter 2: Running TensorFlow Enterprise in Google AI Platform 4. Section 2 – Data Preprocessing and Modeling
5. Chapter 3: Data Preparation and Manipulation Techniques 6. Chapter 4: Reusable Models and Scalable Data Pipelines 7. Section 3 – Scaling and Tuning ML Works
8. Chapter 5: Training at Scale 9. Chapter 6: Hyperparameter Tuning 10. Section 4 – Model Optimization and Deployment
11. Chapter 7: Model Optimization 12. Chapter 8: Best Practices for Model Training and Performance 13. Chapter 9: Serving a TensorFlow Model 14. Other Books You May Enjoy

Chapter 5: Training at Scale

When we build and train more complex models or use large amounts of data in an ingestion pipeline, we naturally want to make better use of all the compute time and memory resources at our disposal in a more efficient way. This is the major purpose of this chapter, as we are going to integrate what we learned in previous chapters with techniques for distributed training running in a cluster of compute nodes.

TensorFlow has developed a high-level API for distributed training. Furthermore, this API integrates with the Keras API very well. As it turns out, the Keras API is now a first-class citizen in the TensorFlow ecosystem. Compared to the estimator API, Keras receives the most support when it comes to a distributed training strategy. Therefore, this chapter will predominantly focus on using the Keras API with a distributed training strategy. We will leverage Google Cloud resources to demonstrate how to make minimal changes to the Keras API code we are...

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