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Building Data Science Applications with FastAPI

You're reading from   Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781837632749
Length 422 pages
Edition 2nd Edition
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Author (1):
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François Voron François Voron
Author Profile Icon François Voron
François Voron
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Introduction to Python and FastAPI
2. Chapter 1: Python Development Environment Setup FREE CHAPTER 3. Chapter 2: Python Programming Specificities 4. Chapter 3: Developing a RESTful API with FastAPI 5. Chapter 4: Managing Pydantic Data Models in FastAPI 6. Chapter 5: Dependency Injection in FastAPI 7. Part 2: Building and Deploying a Complete Web Backend with FastAPI
8. Chapter 6: Databases and Asynchronous ORMs 9. Chapter 7: Managing Authentication and Security in FastAPI 10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI 11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX 12. Chapter 10: Deploying a FastAPI Project 13. Part 3: Building Resilient and Distributed Data Science Systems with FastAPI
14. Chapter 11: Introduction to Data Science in Python 15. Chapter 12: Creating an Efficient Prediction API Endpoint with FastAPI 16. Chapter 13: Implementing a Real-Time Object Detection System Using WebSockets with FastAPI 17. Chapter 14: Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model 18. Chapter 15: Monitoring the Health and Performance of a Data Science System 19. Index 20. Other Books You May Enjoy

Installing Python packages with pip

As we said earlier, pip is the built-in Python package manager that will help us install third-party libraries.

A word on alternate package managers such as Poetry, Pipenv, and Conda

While exploring the Python community, you may hear about alternate package managers such as Poetry, Pipenv, and Conda. These managers were created to solve some issues posed by pip, especially around sub-dependencies management. While they are very good tools, we’ll see in Chapter 10, Deploying a FastAPI Project, that most cloud hosting platforms expect dependencies to be managed with the standard pip command. Therefore, they may not be the best choice for a FastAPI application.

To get started, let’s install FastAPI and Uvicorn:

(venv) $ pip install fastapi "uvicorn[standard]"

We’ll talk about it in later chapters, but Uvicorn is required to run a FastAPI project.

What does “standard” stand for after “uvicorn”?

You probably noticed the standard word inside square brackets just after uvicorn. Sometimes, some libraries have sub-dependencies that are not required to make the library work. Usually, they are needed for optional features or specific project requirements. The square brackets are here to indicate that we want to install the standard sub-dependencies of uvicorn.

To make sure the installation worked, we can open a Python interactive shell and try to import the fastapi package:

(venv) $ python>>> from fastapi import FastAPI

If it passes without any errors, congratulations, FastAPI is installed and ready to use!

You have been reading a chapter from
Building Data Science Applications with FastAPI - Second Edition
Published in: Jul 2023
Publisher: Packt
ISBN-13: 9781837632749
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