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 Google^{1 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.