Fixing the Dependency Jungle First

Fixing the Dependency Jungle First | Generated by AI

Home 2026.03

🦞 Here’s the journey distilled:

The Hard Problem: Getting Inference Working Without a Clean Environment

Core challenge: Python environment was borked—no torch, mismatched Python versions (3.13 vs 3.14 in paths), Homebrew’s PEP 668 blocking installs, and previous failed install attempts littering the logs.

Critical Breakthrough Points:

1. Diagnosis Over Assumption

Didn’t assume “just pip install torch” would work
Checked existing checkpoints first (find . -name "*.pt")
Discovered 3 trained models already existed (saved days of training time)

2. Environment Detective Work

Found user had tried CUDA torch installs before (1.5GB downloads in logs)
Homebrew Python 3.14 with system protection (PEP 668) blocking installs
Traced conda environments that no longer existed
Identified .local/lib/python3.13 vs current Python 3.14 mismatch

3. The Workaround: `--system-site-packages`

Key insight: User already had numpy and tiktoken in .local/

python3 -m venv venv --system-site-packages

This inherited existing packages instead of forcing fresh installs.

4. Torch Was Already Installed!

Biggest “aha”: Torch 2.10.0+cpu was already in .local/lib/python3.14/ from a previous attempt. The venv with system-site-packages could see it.

Why This Was Non-Trivial:

Silent success: Previous installs partially succeeded but weren’t discoverable
Path hell: Multiple Python versions, brew vs system, .local vs site-packages
No clear errors: Just “ModuleNotFoundError: No module named ‘torch’” despite torch existing elsewhere
Resource constraints: 1.5GB model checkpoints + 900MB torch installs = memory/time challenges

The Solution Pattern:

Check what exists → Understand environment constraints →
Leverage existing artifacts → Minimal new installs → Test incrementally

Bottom line: The hard part wasn’t running inference—it was navigating the dependency jungle to reach a state where inference could run. The actual model execution was straightforward once the environment puzzle was solved.

The critical skill was forensic environment analysis rather than ML expertise.

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