Module 5: Dockerization of FastAPI Applications
Containerize python endpoints using Dockerfile specifications. Configure ports redirection, run detached instances, and upload images to Docker Hub registry.
Day 9
What is Docker Containerization?
Why this matters
Docker Intro: Containers package dependencies so 'works on my machine' becomes 'works in prod'.
Docker packages your app + dependencies into an image that runs identically on laptop, CI, and cloud.
| VM | Container |
|---|---|
| Full OS per instance | Shares host kernel; lighter |
| Slow boot | Seconds to start |
Workflow: docker build → docker run -p 8000:8000 → test /health.
Common mistakes
- Blocking the event loop with heavy sync code in async routes.
- Returning wrong HTTP status codes (200 on validation failure).
- Shipping without request/response models for ML endpoints.
Interview checkpoints
- Q: Explain docker intro in one minute. A: Definition + ML deployment angle.
- Q: One FastAPI pitfall? A: Validation, async blocking, or wrong status code.
Practice
- Basic: Define Docker Intro and give an example.
- Intermediate: Implement a minimal snippet for Docker Intro.
- Advanced: Break it and read the OpenAPI / error response.
Recap
- You can explain docker intro clearly.
- You know one mistake to avoid.
- You see how this connects to the next lesson.
Next: Dockerfile
Day 10
Writing a Custom Dockerfile for FastAPI
Why this matters
Dockerfile: A Dockerfile encodes image layers: base Python, install deps, copy app, CMD uvicorn.
A Dockerfile lists layers: base image, install requirements, copy source, define entrypoint.
FROM python:3.11-slim
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"]- Pin base image tags (
python:3.11-slim) - Use
.dockerignoreto exclude.venvand notebooks - Map host port to container
EXPOSEport
Common mistakes
- Blocking the event loop with heavy sync code in async routes.
- Returning wrong HTTP status codes (200 on validation failure).
- Shipping without request/response models for ML endpoints.
Interview checkpoints
- Q: Explain dockerfile in one minute. A: Definition + ML deployment angle.
- Q: One FastAPI pitfall? A: Validation, async blocking, or wrong status code.
Practice
- Basic: Define Dockerfile and give an example.
- Intermediate: Implement a minimal snippet for Dockerfile.
- Advanced: Break it and read the OpenAPI / error response.
Recap
- You can explain dockerfile clearly.
- You know one mistake to avoid.
- You see how this connects to the next lesson.
Next: Pydantic Models