🚀 Tip: Supercharge Your Python Package Installs with uv

If you're tired of waiting on pip to resolve and install dependencies, you're going to love uv. It's a blazing fast, drop-in replacement for pip that makes traditional installs feel sluggish by comparison.

⚡ The Problem

Python dependency resolution with pip especially in larger projects can be painfully slow. Dependency backtracking and wheel building often eat up precious minutes in CI pipelines or local dev environments.

🔥 The Solution

Use uv, a Rust-powered Python package manager by Astral, designed to dramatically accelerate installs and dependency resolution while being compatible with requirements.txt, pyproject.toml, and pip workflows.

🛠️ Quick Setup + Virtual Environment

1. Install uv

curl -Ls https://astral.sh/uv/install.sh | bash

Make sure uv is in your $PATH. You can test it with:

uv --version

2. Create a Virtual Environment (with uv)

Instead of using python -m venv, you can create a virtual environment directly with uv:

uv venv .venv
source .venv/bin/activate

✅ uv venv is a faster, more deterministic alternative that supports multiple Python versions if configured.

3. Install Packages

uv pip install -r requirements.txt

#or

uv pip install <package_name>

🧪 Benchmark: uv vs pip

Let’s say we’re installing common ML/development packages from a requirements.txt:

numpy
pandas
scikit-learn
matplotlib
requests

Installing with pip:

time pip install -r requirements.txt

Result:

real    0m27.123s
user    0m22.854s
sys     0m4.149s

Installing with uv:

time uv pip install -r requirements.txt

Result:

real    0m3.902s
user    0m2.786s
sys     0m1.022s

💡 That’s a 7x speedup — without changing a single line of your dependencies.

✅ Why Use uv?

• ⚡ Fast: Written in Rust, optimized for speed.
• 📦 Compatible: Works with requirements.txt, pyproject.toml, and virtual environments.
• 🔒 Deterministic: Supports lockfiles and reproducible installs.
• 🐍 Drop-in Replacement: No learning curve for pip users.

🔚 Final Thoughts

Whether you're spinning up dev environments, deploying ML models, or managing large Python projects, switching to uv can cut your install times dramatically. Try it once—you’ll be hooked.