Open Source Libraries
A behind-the-scenes look at the libraries that power the platform—and the chaos they caused.
Overview
Open-source libraries are both a dream and a nightmare. At first, I was living the “pip install” life, blindly adding whatever I needed without a second thought.
- Did I track my dependencies? Nope. - Did I know about requirements.in? Not a clue. - Did I laugh in the face of venv? Oh yes.
Then, the unraveling began—when I bought a new laptop and naively thought I could just copy my setup over. Spoiler: It didn’t work.
That was my introduction to: - Python dependency chaos** - Version incompatibilities from hell** - Why freezing environments without a plan is a terrible idea**
Key Goals:
Create a reproducible environment (Docker & virtual environments) for easy setup across devices.
Reduce dependency bloat**—only install what’s truly necessary.
Keep track of core vs. secondary libraries** (requirements.in vs. requirements.txt).
Ensure compatibility** and avoid dependency landmines when updating libraries.
Complexity: Medium
Components
Virtual Environments, Dependency Management & Open Source Libraries
The hard-earned realization that open-source is amazing—until it isn’t.
SOARL Summary
I had accumulated 80-90 core packages (numpy, sqlalchemy, redis, django, pandas, etc.) across various services.
Everything was running smoothly—until I tried to recreate my environment on a new machine.
My approach of “pip install whatever I need at the moment” had turned into an unmanageable mess.
Freezing the environment** didn’t work well because new library versions broke compatibility.
Python environment management isn’t obvious**—until it’s too late.
Dependency issues are sneaky—they only reveal themselves when you **need a clean install.
Took a step back** and cleaned up everything using pip-tools, Homebrew, and a virtual environment strategy.
Migrated to requirements.in, ensuring a **clean and structured dependency tree.
Isolated each service into separate environments** to avoid library conflicts.
A clean, well-structured environment where I actually know what’s installed.
Easier multi-device setup**—switching between laptop and desktop now works seamlessly.
No more mystery errors from outdated or conflicting libraries.
ChatGPT won’t warn you about dependency hell**—until you’re in it.
Python will happily let you dig a hole, jump in, and only then tell you to use virtual environments.**
You don’t realize you need good dependency management—until you do.**
Situation:
Obstacle:
Action:
Result:
Learning:
Key Learnings
- Dependency management is a time bomb if ignored.** - Virtual environments aren’t optional—they’re your **only way to stay sane when managing open-source libraries. - “Just pip install it” is fine… until you need to recreate the environment.**
Demos
Final Thoughts
If I could go back in time, I’d slap my past self and set up virtual environments on day one.
Open-source is amazing—but without proper management, it will consume you. Now, with better tracking, cleaner installs, and environment isolation, I can confidently say: I have tamed the beast. 🔥