You're reading from Generative AI with Python and TensorFlow 2

Product typeBook

Published inApr 2021

PublisherPackt

ISBN-139781800200883

Edition1st Edition

Concepts

Artificial Intelligence

Authors (2):

Joseph Babcock

Raghav Bali

View More author details

VSCode

Visual Studio Code (VSCode) is an open-source code editor developed by Microsoft Corporation which can be used with many programming languages, including Python. It allows debugging and is integrated with version control tools such as Git; we can even run Jupyter notebooks (which we will describe later in this chapter) within VSCode. Instructions for installation vary by whether you are using a Linux, macOS, or Windows operating system: please see individual instructions at https://code.visualstudio.com for your system. Once installed, we need to clone a copy of the source code for the projects in this book using Git, with the command:

git clone git@github.com:PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2.git

This command will copy the source code for the projects in this book to our laptop, allowing us to locally run and modify the code. Once you have the code copied, open the GitHub repository for this book using VSCode (Figure 2.1). We are now ready to start installing some of the tools we will need; open the file install.sh.

Figure 2.1: VSCode IDE

One feature that will be of particular use to us is the fact that VSCode has an integrated (Figure 2.2) terminal where we can run commands: you can access this by selecting View, then Terminal from the drop-down list, which will open a command-line prompt:

Figure 2.2: VSCode terminal

Select the TERMINAL tab, and bash for the interpreter; you should now be able to enter normal commands. Change the directory to Chapter_2, where we will run our installation script, which you can open in VSCode.

The installation script we will run will download and install the various components we will need in our end-to-end TensorFlow lab; the overarching framework we will use for these experiments will be the Kubeflow library, which handles the various data and training pipelines that we will utilize for our projects in the later chapters of this volume. In the rest of this chapter, we will describe how Kubeflow is built on Docker and Kubernetes, and how to set up Kubeflow on several popular cloud providers.

Kubernetes, the technology which Kubeflow is based on, is fundamentally a way to manage containerized applications created using Docker, which allows for reproducible, lightweight execution environments to be created and persisted for a variety of applications. While we will make use of Docker for creating reproducible experimental runtimes, to understand its place in the overall landscape of virtualization solutions (and why it has become so important to modern application development), let us take a detour to describe the background of Docker in more detail.

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Joseph Babcock

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