API Fundamentals and Design Labs

Practice core API concepts and design principles in three small Jupyter notebooks.

Contents

• 01_API_Fundamentals.ipynb : Introduction to APIs, HTTP methods (GET, POST), status codes, and consuming APIs with requests.
• 02_FastAPI_Intro.ipynb : Introduction to FastAPI, creating your first API, Path/Query parameters, and Swagger UI.
• 03_FastAPI_CRUD.ipynb : Building a complete CRUD API (Create, Read, Update, Delete) with FastAPI and Pydantic models.
• main.py : A ready-to-run FastAPI app that mirrors the CRUD notebook (root health check, create/read/update/delete/search tasks). Start it with uvicorn main:app --reload from this folder and open http://127.0.0.1:8000/docs to explore the endpoints.

Why we ship a dedicated main.py

Running an API requires a long-lived server process that can be invoked by an ASGI server such as uvicorn. While the notebooks walk through the concepts step by step, they are not an ideal place to keep a production-ready app: cells run in isolation, state is lost between executions, and there is no stable entry point for tooling, deployment, or automated tests. The standalone main.py file solves this by collecting the FastAPI app, routes, and data models in one importable module so that any environment (local dev, Docker, CI) can start the service with a single command (uvicorn main:app --reload). Keeping the app in a Python file also forces us to respect Python packaging conventions, ensures type-checkers and linters can analyze the code, and mirrors how APIs are structured in real-world projects.

Key Terms

• ASGI (Asynchronous Server Gateway Interface): A standard interface between web servers and Python web applications or frameworks. It allows for asynchronous capabilities (like handling many connections at once), which is essential for modern, high-performance APIs like those built with FastAPI.
• Uvicorn: A lightning-fast ASGI server implementation, using uvloop and httptools. It is the program that actually runs your FastAPI application, listening for incoming HTTP requests and passing them to your code.
• CI (Continuous Integration): A software development practice where code changes are automatically built, tested, and prepared for release. In a CI pipeline, automated scripts need to be able to start your API and run tests against it without human intervention, which is much easier with a standard python file than a notebook.

Set up your Environment

Please make sure you have forked the repo and set up a new virtual environment. For this purpose you can use the following commands:

The added requirements file contains all libraries and dependencies we need to execute the hands-on ml notebooks.

Note: If there are errors during environment setup, try removing the versions from the failing packages in the requirements file. M1/Apple Silicon issues.

macOS or Linux type the following commands :

• Install the virtual environment and the required packages by following commands:

pyenv local 3.11.3
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Windows type the following commands :

• Install the virtual environment and the required packages by following commands.

For PowerShell CLI :

pyenv local 3.11.3
python -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -r requirements.txt

For Git-bash CLI :

pyenv local 3.11.3
python -m venv .venv
source .venv/Scripts/activate
python -m pip install --upgrade pip
pip install -r requirements.txt

Note: If you encounter an error when trying to run pip install --upgrade pip, try using the following command:

python.exe -m pip install --upgrade pip