Book Image

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
3 (1)
Book Image

Building Data Science Applications with FastAPI - Second Edition

3 (1)
By: François Voron

Overview of this book

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.
Table of Contents (21 chapters)
1
Part 1: Introduction to Python and FastAPI
7
Part 2: Building and Deploying a Complete Web Backend with FastAPI
13
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

Generating images from text prompts with Stable Diffusion

Recently, a new generation of AI tools has emerged and fascinated the whole world: image-generation models, such as DALL-E or Midjourney. Those models are trained on huge amounts of image data and are able to generate completely new images from a simple text prompt. These AI models are very good use cases for background workers: they take seconds or even minutes to process, and they need lots of resources in the CPU, RAM, and even the GPU.

To build our system, we’ll rely on Stable Diffusion, a very popular image-generation model that was released in 2022. This model is available publicly and can be run on a modern gaming computer. As we did in the previous chapter, we’ll rely on Hugging Face tools for both downloading the model and running it.

Let’s first install the required tools:

(venv) $ pip install accelerate diffusers

We’re now ready to use diffuser models thanks to Hugging Face.

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