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Dockerizing an ML Application

Why Docker helps

Docker packages:

  • code
  • dependencies
  • runtime environment

So it runs the same everywhere.

false

  flowchart LR
  C[Code] --> I[Docker image]
  D[Dependencies] --> I
  R[Runtime config] --> I
  I --> S[Run anywhere]

false

A simple Dockerfile (FastAPI)

Dockerfile
FROM python:3.11-slim
 
WORKDIR /app
 
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
 
COPY . .
 
EXPOSE 8000
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
Dockerfile
FROM python:3.11-slim
 
WORKDIR /app
 
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
 
COPY . .
 
EXPOSE 8000
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

tips

  • pin versions in requirements.txtrequirements.txt
  • keep images small (slimslim base)
  • don’t bake secrets into images

Mini-checkpoint

Why is Docker useful for ML specifically?

  • ML dependencies are often heavy and version-sensitive.

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