Why I Replaced pip with uv for My AI/ML Projects

Managing Python environments has always been a quiet background task in machine learning workflows — important, but rarely exciting. For a long time, I was content using pip and virtualenv or conda, accepting that setting up environments just took time.

But as my AI projects got more experimental — jumping between GenAI prototypes, NLP pipelines, and deep learning models — I started to feel the friction more acutely.

Waiting minutes for heavy libraries like torch, transformers, or opencv-python to install. Dealing with version mismatches across environments. Rerunning installs just to switch between experiments.

It wasn’t unworkable. But it was slowing me down in subtle, constant ways.

So, I tried something new: [uv](https://github.com/astral-sh/uv), a Rust-based Python package manager that promised to be significantly faster than pip.

I didn’t expect it to make a big difference.

But it did.

What Changed After Switching to uv

🚀 Speed That Actually Matters

The first thing I noticed was how fast it is. Installing torch no longer meant stepping away for coffee. Complex environments were ready in seconds, not minutes.

This small gain had a compounding effect: it made prototyping feel lighter. I could reset environments without hesitation. Try different combinations. Burn down and rebuild faster than I used to troubleshoot.

When you’re working with fast-evolving libraries, that agility really starts to matter.

🔄 Reproducible and Lightweight by Default

uv creates ephemeral virtual environments automatically — and they’re smart about how they’re built. Large binary files don’t get copied around. Things are cached aggressively. Disk usage stays minimal, even across multiple experiments.

It also locks dependencies deterministically, so I stopped seeing the classic “it works on my machine” behavior when sharing projects or pulling them across devices.

🛠 Minimal Setup, Maximum Compatibility

The transition was smooth. I didn’t have to rethink anything:

• requirements.txt and pyproject.toml worked out of the box
• It played nicely with CUDA toolkits and system dependencies
• Layered setups using pipx or conda still worked when I needed them.

All I did was swap pip install with uv pip install. That was it.

Why It Stuck

I didn’t switch to uv because everyone was talking about it. In fact, I hadn’t seen many people using it when I first tried it.

But once I did, I realized how much mental overhead I was carrying around just to keep environments working smoothly.

Since then, I’ve used uv on every new ML project. Not because I had to—but because it made building feel smoother, lighter, and faster. And that kind of momentum makes a difference when you’re trying to stay creative in a space that moves as fast as AI does.

If you’re working on local AI projects and environment setup is silently draining your time and focus, give uv a try. It’s not just faster—it’s the kind of quiet upgrade that you only notice once it’s gone.