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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

Creating a Python virtual environment

As for many programming languages of today, the power of Python comes from the vast ecosystem of third-party libraries, including FastAPI, of course, that help you build complex and high-quality software very quickly. The Python Package Index (PyPi) (https://pypi.org) is the public repository that hosts all those packages. This is the default repository that will be used by the built-in Python package manager, pip.

By default, when you install a third-party package with pip, it will install it for the whole system. This is different from some other languages, such as Node.js’ npm, which by default creates a local directory for the current project to install those dependencies. Obviously, this may cause issues when you work on several Python projects with dependencies having conflicting versions. It also makes it difficult to retrieve only the dependencies necessary to deploy a project properly on a server.

This is why Python developers generally use virtual environments. Basically, a virtual environment is just a directory in your project containing a copy of your Python installation and the dependencies of your project. This pattern is so common that the tool to create them is bundled with Python:

  1. Create a directory that will contain your project:
    $ mkdir fastapi-data-science$ cd fastapi-data-science

Tip for Windows with WSL users

If you are on Windows with WSL, we recommend that you create your working folder on the Windows drive rather than the virtual filesystem of the Linux distribution. It’ll allow you to edit your source code files in Windows with your favorite text editor or integrated development environment (IDE) while running them in Linux.

To do this, you can access your C: drive in the Linux command line through /mnt/c. You can thus access your personal documents using the usual Windows path, for example, cd /mnt/c/Users/YourUsername/Documents.

  1. You can now create a virtual environment:
    $ python -m venv venv

Basically, this command tells Python to run the venv package of the standard library to create a virtual environment in the venv directory. The name of this directory is a convention, but you can choose another name if you wish.

  1. Once this is done, you have to activate this virtual environment. It’ll tell your shell session to use the Python interpreter and the dependencies in the local directory instead of the global ones. Run the following command:
    $ source venv/bin/activatee

After doing this, you may notice the prompt adds the name of the virtual environment:

(venv) $

Remember that the activation of this virtual environment is only available for the current session. If you close it or open other command prompts, you’ll have to activate it again. This is quite easy to forget, but it will become natural after some practice with Python.

You are now ready to install Python packages safely in your project!

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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