pypm-manager 1.0.0

Efficient Python package manager with centralized storage and zero duplication

PyPM - Python Package Manager

A revolutionary Python package manager that eliminates package duplication across environments by using centralized storage with environment-specific manifests.

🎯 Problem Solved

Traditional Python package managers create separate copies of packages for each virtual environment, leading to:

• Massive storage waste from duplicated packages
• Slower environment setup due to redundant downloads
• Difficult package management across multiple projects

💡 Solution

PyPM stores each package version only once in a central location and uses lightweight environment dictionary files to specify which versions each environment should use.

Key Features

• ✅ Zero Duplication: Each package version stored only once
• ✅ Environment Manifests: JSON-based dictionary files specify package versions
• ✅ Efficient Loading: Only specified packages are loaded per environment
• ✅ Storage Savings: Dramatically reduces disk usage
• ✅ Fast Setup: No redundant package downloads
• ✅ No Dependencies: Pure Python standard library

📦 Installation

Install from PyPI (Recommended)

pip install pypm-manager

Install from Source

git clone https://github.com/yourusername/pypm.git
cd pypm
pip install -e .

Verify Installation

pypm --help

📁 Architecture

PyPM System
├── Central Store (~/.pypm_store/)
│   ├── packages/
│   │   ├── <hash1>/  (e.g., numpy 1.24.0)
│   │   ├── <hash2>/  (e.g., pandas 2.0.0)
│   │   └── <hash3>/  (e.g., numpy 1.23.0)
│   └── metadata.json
│
└── Environments (~/.pypm_envs/)
    ├── project1.json  (manifest with package versions)
    ├── project2.json
    └── ml_project.json

🚀 Quick Start

After installation, the pypm command is available globally:

Basic Usage

1. Add Packages to Central Store

# Add a package version to the central store
pypm add numpy 1.24.0 /path/to/numpy/files

# Add another version
pypm add numpy 1.23.0 /path/to/numpy-1.23.0/files

# Add different packages
pypm add pandas 2.0.0 /path/to/pandas/files
pypm add requests 2.31.0 /path/to/requests/files

2. Create Environments

# Create a new environment
#### 2. Create Environments

```bash
# Create a new environment
pypm create-env project1 -d "Data analysis project"

# Create another environment
pypm create-env ml_project -d "Machine learning project"

3. Install Packages to Environments

# Install specific package versions to project1
pypm install project1 numpy 1.24.0
pypm install project1 pandas 2.0.0

# Install different versions to ml_project
pypm install ml_project numpy 1.23.0
pypm install ml_project requests 2.31.0

4. Verify & Activate

# Verify all packages are available
pypm verify project1

# Generate activation script
pypm activate project1 -o activate_project1.py

📋 CLI Commands

Package Management

# Add package to central store
pypm add <name> <version> <path>

# Remove package from store
pypm remove <name> <version>

# List all packages in store
pypm list

# Show store information
pypm info

Environment Management

# Create environment
pypm create-env <name> [-d description]

# Delete environment
pypm delete-env <name>

# List all environments
pypm list-envs

# Show environment details
pypm show-env <name>

# Install package to environment
pypm install <env> <package> <version>

# Uninstall package from environment
pypm uninstall <env> <package>

# Verify environment
pypm verify <name>

# Create activation script
pypm activate <name> [-o output.py]

💻 Programmatic Usage

from pypm import CentralPackageStore, EnvironmentManager, PackageLoader

# Initialize components
store = CentralPackageStore()
env_manager = EnvironmentManager()
loader = PackageLoader(store, env_manager)

# Add packages to central store
store.add_package("numpy", "1.24.0", "/path/to/numpy")
store.add_package("pandas", "2.0.0", "/path/to/pandas")

# Create environment
env_manager.create_environment("my_project", "Data analysis")
env_manager.add_package_to_env("my_project", "numpy", "1.24.0")
env_manager.add_package_to_env("my_project", "pandas", "2.0.0")

# Load and activate environment
loader.activate_environment("my_project")

# Now you can import the packages
import numpy as np
import pandas as pd

📊 Example Workflow

# 1. Setup central store with packages
pypm add numpy 1.24.0 C:/packages/numpy-1.24.0
pypm add pandas 2.0.0 C:/packages/pandas-2.0.0
pypm add scikit-learn 1.3.0 C:/packages/sklearn-1.3.0

# 2. Create two projects with different requirements
pypm create-env data_analysis -d "Data analysis project"
pypm create-env ml_model -d "ML model training"

# 3. Configure each environment
pypm install data_analysis numpy 1.24.0
pypm install data_analysis pandas 2.0.0

pypm install ml_model numpy 1.24.0
pypm install ml_model scikit-learn 1.3.0

# 4. Verify environments
pypm verify data_analysis
pypm verify ml_model

# 5. View what's in store
pypm list
pypm info

# 6. Generate activation scripts
pypm activate data_analysis
pypm activate ml_model

🔍 Environment Manifest Example

Each environment is stored as a JSON file:

{
  "name": "project1",
  "description": "Data analysis project",
  "packages": {
    "numpy": "1.24.0",
    "pandas": "2.0.0",
    "requests": "2.31.0"
  },
  "metadata": {
    "created": true
  }
}

💾 Storage Savings Example

Traditional approach:

• Environment 1: numpy (50MB) + pandas (100MB) + requests (5MB) = 155MB
• Environment 2: numpy (50MB) + scikit-learn (200MB) = 250MB
• Total: 405MB (numpy duplicated)

PyPM approach:

• Central Store: numpy (50MB) + pandas (100MB) + requests (5MB) + scikit-learn (200MB) = 355MB
• Environment files: 2 × ~1KB = 2KB
• Total: 355MB + 2KB
• Savings: 50MB (12% reduction with just 2 environments)

With more environments, savings multiply significantly!

🏗️ Project Structure

Packagemanager/
├── central_store.py         # Central package storage management
├── environment_manager.py   # Environment manifest handling
├── package_loader.py        # Package loading and activation
├── pypm.py                  # Command-line interface
├── __init__.py              # Package initialization
├── README.md                # This file
└── examples/                # Example usage scripts
    ├── example_basic.py
    └── example_advanced.py

🔧 Requirements

• Python 3.7+
• No external dependencies (uses only standard library)

🎓 Use Cases

1. Multi-project development: Share packages across multiple projects
2. Testing multiple versions: Test code against different package versions
3. CI/CD pipelines: Faster environment setup in automated builds
4. Educational environments: Create multiple student environments efficiently
5. Production deployments: Reduce container image sizes

📝 Notes

• Package paths are stored as absolute paths in the central store
• Environment manifests are lightweight JSON files (~1KB each)
• The system uses content-addressable storage (hash-based naming)
• Compatible with existing Python packages

🚀 Future Enhancements

• Automatic package download from PyPI
• Dependency resolution
• Virtual environment integration
• Package verification and checksums
• Export/import environment configurations
• Cloud storage backend support

📄 License

This project is open source and available for educational and commercial use.

🤝 Contributing

Contributions are welcome! This is a proof-of-concept that can be extended with additional features.

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PyPM - Making Python package management efficient, one environment at a time! 🚀