FastAPI 애플리케이션 컨테이너화

Welcome! In this lesson, we'll learn how to package your FastAPI application using Docker. This makes your app incredibly consistent and easy to deploy anywhere.

• What is Docker? It's a platform that uses OS-level virtualization to deliver software in packages called containers.
• Why use it for FastAPI? It solves the "it works on my machine" problem by packaging your app and its dependencies together.

Before we dive into Dockerizing, let's understand two key concepts:

• Docker Image: Think of an image as a blueprint or a template. It's a static, immutable file that contains your application code, libraries, dependencies, and configuration.
• Docker Container: A container is a runnable instance of an image. It's an isolated environment where your application runs, completely separate from your host system.

You build an image, then run a container from it.

Let's start with a simple FastAPI application that we'll containerize. This file will be named main.py.

It has a single endpoint that returns a 'Hello' message.

A Dockerfile is a text file that contains all the commands a user could call on the command line to assemble an image. It's your recipe for building the image.

We start by picking a base image and setting our working directory:

• FROM: Specifies the base image (e.g., Python version).
• WORKDIR: Sets the current working directory inside the container.

Your FastAPI app needs specific Python packages to run. We list these in a requirements.txt file. This file tells Docker which packages to install inside the container.

Here's a typical requirements.txt for our simple FastAPI app:

Now, let's add commands to our Dockerfile to copy and install these dependencies:

• COPY requirements.txt .: Copies the requirements.txt from your local machine to the container's /app directory.
• RUN pip install ...: Executes the command to install all packages listed in requirements.txt. --no-cache-dir helps keep the image size small.

Finally, we add our application code and tell Docker how to run it:

• COPY . .: Copies all remaining files from your current directory (including main.py) into the container's /app directory.
• EXPOSE 8000: Informs Docker that the container listens on port 8000. It's documentation, not a firewall rule.
• CMD [...]: Specifies the command to run when the container starts. This is how Uvicorn will serve your FastAPI app.

With your Dockerfile and FastAPI app ready, it's time to build the Docker image!

Open your terminal in the same directory as your Dockerfile and main.py, then run:

• docker build .: Tells Docker to build an image using the Dockerfile in the current directory.
• -t myfastapi-app: Tags the image with a name (myfastapi-app) for easy reference.

Once the image is built, you can run your FastAPI application in a container!

Use the docker run command:

• -p 8000:8000: This maps port 8000 on your host machine to port 8000 inside the container.
• myfastapi-app: The name of the image we just built.

After running, open your browser or use curl to visit http://localhost:8000.

For applications with multiple services (like a FastAPI app and a database), Docker Compose simplifies management. It lets you define and run multi-container Docker applications using a YAML file.

Instead of running multiple docker run commands, you define everything in docker-compose.yml and use docker-compose up.

Which Dockerfile command is used to copy files or directories from your local machine into the Docker image?

Great job! You've learned the fundamentals of Dockerizing your FastAPI application. We covered:

• The difference between Docker Images and Containers.
• Writing a Dockerfile with commands like FROM, WORKDIR, COPY, RUN, EXPOSE, and CMD.
• Building and running your FastAPI app in a Docker container.
• A brief introduction to Docker Compose for multi-service apps.

This skill is crucial for consistent and scalable deployments!