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FastAPI Backend Development Bootcamp · レッスン

FastAPIアプリケーションのDocker化

Dockerを使ってFastAPIサービスをコンテナ化し、効率的で移植性の高いデプロイ用イメージを作成する方法を学びます。

「FastAPIアプリケーションのDocker化」はCoddyKit上の無料FastAPI Backend Development Bootcampレッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはFastAPI Backend Development Bootcamp学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 FastAPI Backend Development Bootcampコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Docker & FastAPI: Why Containerize?

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.

Docker's Core: Images & Containers

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.

A Basic FastAPI Service

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.

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
async def read_root():
    return {"message": "Hello from FastAPI!"}

# To run locally (without Docker):
# uvicorn main:app --host 0.0.0.0 --port 8000

The Dockerfile: Starting Strong

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.
# Dockerfile
FROM python:3.9-slim-buster
WORKDIR /app

FastAPI Dependencies: requirements.txt

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:

# requirements.txt
fastapi==0.104.1
uvicorn[standard]==0.24.0.post1

Dockerfile: Installing Dependencies

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.
# Dockerfile (continued)
FROM python:3.9-slim-buster
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

Dockerfile: App Code & Entrypoint

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.
# Dockerfile (final)
FROM python:3.9-slim-buster
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Building Your Docker Image

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.
# Terminal Command
docker build -t myfastapi-app .

Running Your FastAPI Container

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.

# Terminal Command
docker run -p 8000:8000 myfastapi-app

Introducing Docker Compose

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.

# docker-compose.yml (simplified)
version: '3.8'
services:
  web:
    build: .
    ports:
      - "8000:8000"

Dockerfile Command Check

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

Recap: FastAPI in a Box!

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!

よくある質問

「FastAPIアプリケーションのDocker化」レッスンは無料ですか？

はい。「FastAPIアプリケーションのDocker化」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、FastAPI Backend Development Bootcampコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 FastAPI Backend Development Bootcampコースには全4レッスンが含まれています。

「FastAPIアプリケーションのDocker化」で何を学びますか？

Dockerを使ってFastAPIサービスをコンテナ化し、効率的で移植性の高いデプロイ用イメージを作成する方法を学びます。 ブラウザで直接実行するハンズオンコードでFastAPI Backend Development Bootcampを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

FastAPI Backend Development Bootcampを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのFastAPI Backend Development Bootcampは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。

「FastAPIアプリケーションのDocker化」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このFastAPI Backend Development Bootcampレッスンでコードを書いて実行できますか？

はい。すべてのFastAPI Backend Development Bootcampレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. FastAPIアプリケーションのDocker化
  2. GunicornとUvicornによるデプロイ
  3. クラウドデプロイ戦略
  4. 環境変数とシークレットの管理
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