pepip 0.0.2

Shared global environment package installer using symlinks — like pnpm for Python

🐍 pepip

uv and pip, but shared. Install packages once. Use them everywhere.

pepip is the pnpm of Python — a drop-in alternative to pip / uv that stores each resolved package version once in a shared store and wires your project .venv to it via symlinks. No more downloading torch five times. It internatly uses uv for package resolution and venv management, so you get all the same features and compatibility, but with a fraction of the disk usage and faster installs for subsequent projects.

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🤔 The Problem

Every Python project gets its own virtual environment. That means every project downloads and stores its own copy of every dependency — including the big ones.

project-a/.venv/   →  numpy (35 MB)  pandas (15 MB)  torch (2.4 GB)
project-b/.venv/   →  numpy (35 MB)  pandas (15 MB)  torch (2.4 GB)
project-c/.venv/   →  numpy (35 MB)  pandas (15 MB)  torch (2.4 GB)
                                                  ↑ stored 3× for no reason

✅ The Solution

pepip keeps an immutable shared package-version store and symlinks each project's .venv back to the exact versions it resolved. Same Python import behaviour. A fraction of the disk usage.

~/.pepip/packages/      →  numpy 2.0 (35 MB)  pandas 2.2 (15 MB)  torch 2.5 (2.4 GB)  ← stored once per version

project-a/.venv/        →  numpy (symlink)  pandas (symlink)  torch (symlink)
project-b/.venv/        →  numpy (symlink)  pandas (symlink)  torch (symlink)
project-c/.venv/        →  numpy (symlink)  pandas (symlink)  torch (symlink)

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🚀 Installation

pip install pepip

Requirements: Python 3.8+ · uv (auto-installed)

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📦 Usage

Install packages using pepip

# Install one or more packages
pepip install numpy pandas

# Install from a requirements file
pepip install -r requirements.txt

# Use a custom local venv path (default: .venv)
pepip install numpy --venv /path/to/my-env

Then activate and use your .venv exactly as you normally would:

source .venv/bin/activate
python -c "import numpy; print(numpy.__version__)"

Override the global store location

PEPIP_HOME=/shared/team-env pepip install torch

This is handy for sharing a global store across a whole team on a shared machine.

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🗂 How it works

~/.pepip/
├── global-venv/                         ← build interpreter for uv installs
└── packages/
    └── cpython-312-linux-x86_64/
        ├── numpy-2.0/
        │   ├── numpy/                   ← real files, stored once
        │   └── numpy-2.0.dist-info/
        ├── requests-2.25.1/
        │   ├── requests/
        │   └── requests-2.25.1.dist-info/
        └── requests-2.26.0/
            ├── requests/
            └── requests-2.26.0.dist-info/

my-project/
└── .venv/
    └── lib/
        └── python3.12/
            └── site-packages/
                ├── numpy   ──────────→  ~/.pepip/packages/.../numpy-2.0/numpy   (symlink, ~bytes)
                ├── pandas  ──────────→  ~/.pepip/packages/.../pandas-2.2/pandas  (symlink, ~bytes)
                └── ...

• First install of a package version — downloads once into the shared store, then symlinks.
• Every subsequent project using the same package version — symlinks only. Near-instant, zero extra disk.
• Different projects can use different versions — for example, one project can link to requests==2.25.1 while another links to requests==2.26.0.

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📊 Benchmarks

The eval/benchmark.py script measures installation latency and disk usage across N projects compared to a plain uv workflow.

# 5 projects, mixed real-world packages
python eval/benchmark.py --projects 5 --packages tomli packaging requests numpy pandas

# Keep temp directories for manual inspection
python eval/benchmark.py --no-cleanup

Latest results — 5 projects · tomli packaging requests numpy pandas

Metric	uv (baseline)	pepip	Improvement
⏱ Latency	0.56 s	0.33 s ★	−41.3 %
💾 Disk usage	475.19 MB	95.22 MB ★	−80.0 %

⏱ pepip saved 0.23 s of install time across 5 projects. 💾 pepip saved 379.97 MB of disk space across 5 projects.

Why the savings get better over time

• Storage savings are consistent from project one: each package version lives exactly once in the global store, so local .venv directories contain only tiny symlinks (dozens of bytes each) instead of full copies.
• Latency savings grow with project count: the first project pays the same download cost as plain uv. Every additional project only needs venv creation + symlink creation, which is nearly instant. For large packages like torch or transformers (GBs in size), these savings per extra project are proportionally enormous.

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🛠 Development

# Clone and install in editable mode
git clone https://github.com/perf-pip/pepip
cd pepip
pip install -e .

# Run tests
pip install pytest
pytest

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💡 Inspired by

pnpm — the Node.js package manager that pioneered content-addressable, symlink-based shared stores. pepip brings the same idea to the Python ecosystem.

Acknowledgements

• uv — used for venv management, package resolution, and shared download caching.

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Made with ❤️ for developers tired of downloading torch over and over again.