The Problem: Python Dependency Management is a Dumpster Fire

If you’ve spent any amount of time working with Python, especially in AI/ML, you already know the pain. Python’s dependency management is in a perpetual state of chaos, teetering on the edge of total collapse at any given moment.

You install something today, it works. Tomorrow? Your environment is bricked because some package upstream decided to push a breaking change. Maybe it’s a minor version bump that should be safe. Maybe pip decided to upgrade just one dependency but not the others. Maybe you’re debugging a segfault because some wheel got built with a broken C extension.

Sound familiar? Yeah, thought so.

The Current “Solutions” Suck

Python gives us tools to manage this mess, but they’re about as effective as a bandaid on a gunshot wound.

1. Virtual Environments (venv, virtualenv)
   • “Just use a virtual environment!” they say.
   • Yeah, sure. Until you need different Python versions, system dependencies, or GPU acceleration, and suddenly you’re managing a dozen environments like some kind of package janitor.
2. Conda
   • Better than pip, but still a mess.
   • You ever try to install something that’s only available via pip, but conda doesn’t play nice? Now you’ve got a hybrid environment that’s one bad install away from total meltdown.
   • Also, enjoy the massive environment sizes and inexplicably slow package resolution.
3. Poetry / Pipenv
   • “Modern” package managers that add some structure but don’t solve the core problem.
   • Still susceptible to upstream breakage.
   • Still doesn’t solve system-level dependencies.

The Real Solution: Containerize Everything

Forget venv. Forget pipenv. If you care about reliability and portability, use Docker (or some other containerization solution). Here’s why.

1. Full Isolation
   • Your Python environment is completely independent from the host system.
   • No more “works on my machine” issues.
   • No accidental dependency version mismatches.
2. Reproducibility
   • A Dockerfile is a self-contained, versioned description of your environment.
   • Someone else (or future you) can spin up the exact same setup months later without worrying about broken dependencies.
3. Better Dependency Management
   • Need different versions of Python? Just pick the right base image.
   • Need system dependencies (libc, cuda, ffmpeg)? Install them in the container without messing up your local machine.
4. Easy Deployment
   • Your environment works the same in dev, testing, and production.
   • No more janky server setups where Python versions and dependencies don’t match.

How to Containerize Your Python Projects

Here’s a dead-simple example of how to build a Docker container for a Python project.

Step 1: Create a Dockerfile

# Use an official Python base image
FROM python:3.11-slim

# Set the working directory
WORKDIR /app

# Copy your requirements file and install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the rest of your code.
# Pro tip: Always do this after lengthy dependency install steps so you don't have to repeat those every time you change application code
COPY . .

# Set the entrypoint
CMD ["python", "main.py"]

Step 2: Build and Run the Container

docker build -t my-python-app .
docker run --rm my-python-app

Step 3: Use Docker Compose for Complex Projects

If you need multiple services (e.g., a database, Redis, etc.), use docker-compose.yml:

version: '3'

services:
    app:
        build: .
        volumes:
            - .:/app
        depends_on:
            - db
    db:
        image: postgres:15
        environment:
            POSTGRES_USER: user
            POSTGRES_PASSWORD: password
            POSTGRES_DB: mydb

Then run:

docker-compose up

But What About Performance?

Yeah, running a full container can add some overhead, but in most cases, it’s negligible. If you need GPU acceleration for ML, use NVIDIA’s Docker runtime (nvidia-docker).

For general development, mount your local code inside the container so you don’t have to rebuild constantly:

volumes:
  - .:/app

TL;DR: Just Use Containers

Python’s dependency management is fundamentally broken, and existing solutions aren’t enough. The only reliable way to ensure a stable, reproducible, and scalable Python environment is containerization.

Stop wasting time debugging package version conflicts. Just put everything in a container and move on with your life.