0Pricing
FastAPI Backend Development Bootcamp · Aula

Conteinerização de aplicações FastAPI

Aprenda a conteinerizar seu serviço FastAPI usando o Docker e a criar imagens de implantação eficientes e portáteis.

Conteinerização de aplicações FastAPI é uma aula grátis de FastAPI Backend Development Bootcamp no CoddyKit. Esta é a aula 1 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de FastAPI Backend Development Bootcamp, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de FastAPI Backend Development Bootcamp inclui 4 aulas no total.

Partes desta aula ainda não foram traduzidas e aparecem em inglês.

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!

Perguntas Frequentes

A aula “Conteinerização de aplicações FastAPI” é grátis?

Sim — o texto completo de “Conteinerização de aplicações FastAPI” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de FastAPI Backend Development Bootcamp, atualize para CoddyKit PRO. O curso de FastAPI Backend Development Bootcamp inclui 4 aulas no total.

O que vou aprender em “Conteinerização de aplicações FastAPI”?

Aprenda a conteinerizar seu serviço FastAPI usando o Docker e a criar imagens de implantação eficientes e portáteis. Você pratica FastAPI Backend Development Bootcamp com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.

Preciso ter experiência prévia para começar FastAPI Backend Development Bootcamp?

Nenhuma experiência prévia é necessária. FastAPI Backend Development Bootcamp no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 1 de 4.

Quanto tempo leva a aula “Conteinerização de aplicações FastAPI”?

A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.

Posso escrever e executar código nesta aula de FastAPI Backend Development Bootcamp?

Sim. Cada aula de FastAPI Backend Development Bootcamp inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.

Todas as aulas deste curso

  1. Conteinerização de aplicações FastAPI
  2. Implantação com Gunicorn e Uvicorn
  3. Estratégias de implantação na nuvem
  4. Gerenciamento de variáveis de ambiente e segredos
← Voltar para FastAPI Backend Development Bootcamp