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Generative AI with Python and TensorFlow 2

You're reading from   Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
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Joseph Babcock
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Table of Contents (16) Chapters Close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab FREE CHAPTER 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

Setting Up a TensorFlow Lab

Now that you have seen all the amazing applications of generative models in Chapter 1, An Introduction to Generative AI: "Drawing" Data from Models, you might be wondering how to get started with implementing these projects that use these kinds of algorithms. In this chapter, we will walk through a number of tools that we will use throughout the rest of the book to implement the deep neural networks that are used in various generative AI models. Our primary tool is the TensorFlow 2.0 framework, developed by Google1 2; however, we will also use a number of additional resources to make the implementation process easier (summarized in Table 2.1).

We can broadly categorize these tools:

  • Resources for replicable dependency management (Docker, Anaconda)
  • Exploratory tools for data munging and algorithm hacking (Jupyter)
  • Utilities to deploy these resources to the cloud and manage their lifecycle (Kubernetes, Kubeflow, Terraform)

Tool

Project site

Use

Docker

https://www.docker.com/

Application runtime dependency encapsulation

Anaconda

https://www.anaconda.com/

Python language package management

Jupyter

https://jupyter.org/

Interactive Python runtime and plotting / data exploration tool

Kubernetes

https://kubernetes.io/

Docker container orchestration and resource management

Kubeflow

https://www.kubeflow.org/

Machine learning workflow engine developed on Kubernetes

Terraform

https://www.terraform.io/

Infrastructure scripting language for configurable and consistent deployments of Kubeflow and Kubernetes

VSCode

https://code.visualstudio.com/

Integrated development environment (IDE)

Table 2.1: Tech stack for generative adversarial model development

On our journey to bring our code from our laptops to the cloud in this chapter, we will first describe some background on how TensorFlow works when running locally. We will then describe a wide array of software tools that will make it easier to run an end-to-end TensorFlow lab locally or in the cloud, such as notebooks, containers, and cluster managers. Finally, we will walk through a simple practical example of setting up a reproducible research environment, running local and distributed training, and recording our results. We will also examine how we might parallelize TensorFlow across multiple CPU/GPU units within a machine (vertical scaling) and multiple machines in the cloud (horizontal scaling) to accelerate training. By the end of this chapter, we will be all ready to extend this laboratory framework to tackle implementing projects using various generative AI models.

First, let's start by diving more into the details of TensorFlow, the library we will use to develop models throughout the rest of this book. What problem does TensorFlow solve for neural network model development? What approaches does it use? How has it evolved over the years? To answer these questions, let us review some of the history behind deep neural network libraries that led to the development of TensorFlow.

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Generative AI with Python and TensorFlow 2
Published in: Apr 2021
Publisher: Packt
ISBN-13: 9781800200883
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