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

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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

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Setting up a notebook environment

TensorFlow Enterprise is exclusively available in the JupyterLab environment hosted by Google Cloud. There are three ways to consume the JupyterLab with this TensorFlow distribution: Google Cloud AI Platform Notebook, Google Cloud Deep Learning Virtual Machine Images (DLVM), and Google Cloud Deep Learning Containers (Docker image) running on your local machine. No matter which one you choose, you will see the same interface of a standard JupyterLab environment like this:

Figure 2.1 – JupyterLab portal

So let's take a look at how to get started.

AI Platform Notebook

This is the easiest and least complicated way to start using TensorFlow Enterprise and get it running in Google Cloud:

Simply go to the Google Cloud portal, select AI Platform in the left panel, then select the Notebooks option:
Figure 2.2 – AI Platform starting portal
Then click on NEW INSTANCE, and you'll be offered...