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

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781800209145

Length 314 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

KC Tung

View More author details

Table of Contents (15) Chapters

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

Leave a review - let other readers know what you think

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...