digy 0.2.7

Do Interactive Git deploY - Deploy Python applications from Git repositories in isolated environments with interactive menu

DIGY - Dynamic Interactive Git deploY

Note: DIGY is in active development. Some features may be experimental.

DIGY is a powerful tool for executing Python code in various environments with minimal setup. It provides a consistent interface for running code locally, in Docker containers, in-memory, or on remote machines.

🚀 Quick Links

• Features
• Installation
• Quick Start
• Examples
• Documentation
• Contributing
• License
• Support

🌟 Features

🛠️ Multi-Environment Execution

• Local: Run code in isolated virtual environments
• Docker: Containerized execution for reproducibility
• RAM: In-memory execution for maximum performance
• Remote: Execute on remote machines via SSH
• JVM: Run Java/Scala code with Python interop

💻 Interactive & Scriptable

• Rich terminal interface with auto-completion
• Command-line automation support
• Scriptable execution flows
• Jupyter notebook integration

🔄 Flexible Code Loading

• Direct Git repository cloning
• Automatic zip download fallback
• Support for private repositories
• Branch/tag/commit selection

📁 File & Data Management

• Interactive file selection
• File attachment support
• Volume mounting for persistent data
• Built-in data processing utilities

🔒 Security & Authentication

• Multiple authentication methods
• Secure credential management
• Environment-based configuration
• Custom authentication providers

📊 Monitoring & Debugging

• Real-time resource usage
• Detailed logging
• Debug mode for troubleshooting
• Performance metrics

🔄 Integration & Extensibility

• Plugin system for custom environments
• Webhook support
• REST API
• CLI and programmatic interfaces

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Git (recommended for direct Git operations)
• Docker (optional, for containerized execution)
• Poetry (for development)

Installation

Using pip (recommended)

pip install digy

Development Installation

# Clone the repository
git clone https://github.com/pyfunc/digy.git
cd digy

# Install with development dependencies
pip install -e .[dev]

# Or use Poetry
poetry install

Basic Usage

For Local Scripts (Recommended)

For running local scripts directly, use Python directly:

# Run a local script directly with Python
python -m examples.basic.hello_world

# For machine learning example
python -m examples.machine_learning.iris_classifier

Using DIGY (Repository-based Execution)

DIGY is designed to work with git repositories. To use DIGY:

1. Initialize a git repository if you haven't already:

   git init
   git add .
   git commit -m "Initial commit"
   

2. Run with DIGY:

   # Start interactive mode
   digy local .
   
   # Or specify the script directly (requires git repository)
   digy run . examples/basic/hello_world.py
   

Common Issues

If you see errors about missing manifest files, create a basic digy.yaml:

echo "python: 3.10" > digy.yaml

For "Failed to clone repository" errors, ensure:

1. The directory is a git repository
2. Remote repository is properly set up if using digy run
3. You have proper git permissions for the repository

📚 Documentation

Command Reference

digy local [REPO_URL]

Start an interactive session for a git repository.

Options:

• --python VERSION: Python version to use (e.g., 3.10)
• --cwd PATH: Working directory
• --debug: Enable debug output

Examples:

# Start interactive mode in current directory
digy local .

# Specify Python version
digy local --python 3.10 .

digy run <REPO_URL> <SCRIPT_PATH> [args...]

Run a specific script from a git repository.

Options:

• --python VERSION: Python version to use
• --debug: Enable debug output

Examples:

# Run a script from the current repository
digy run . examples/basic/hello_world.py

# With arguments
digy run . examples/basic/script.py arg1 arg2

digy docker [options] <script> [args...]

Run a script in a Docker container.

Options:

• --image IMAGE: Docker image to use (default: python:3.9-slim)
• --build: Build Docker image from Dockerfile
• --dockerfile PATH: Path to Dockerfile
• --no-cache: Disable Docker cache
• --volume SRC:DST: Mount a volume

Examples:

# Basic usage
digy docker script.py

# Specify custom image
digy docker --image tensorflow/tensorflow script.py

# Mount volumes
digy docker -v $(pwd)/data:/data script.py

digy ram <script> [args...]

Run a script in memory for maximum performance.

Examples:

# Basic usage
digy ram script.py

# With dependencies
pip install -r requirements.txt
digy ram script.py

digy remote <user@host> <repo> <script> [args...]

Run a script on a remote machine.

Options:

• --key PATH: SSH private key
• --port PORT: SSH port (default: 22)
• --ssh-args ARGS: Additional SSH arguments

Examples:

# Basic usage
digy remote [e-mail] github.com/owner/repo script.py

# With custom SSH key
digy remote --key ~/.ssh/id_rsa [e-mail] github.com/owner/repo script.py

📦 Configuration

DIGY can be configured using environment variables or a configuration file.

Environment Variables

Variable	Default	Description
DIGY_DEBUG	false	Enable debug output
DIGY_CACHE_DIR	~/.cache/digy	Cache directory
DIGY_CONFIG	~/.config/digy/config.toml	Config file path
DIGY_DOCKER_IMAGE	python:3.9-slim	Default Docker image
DIGY_PYTHON_BIN	python3	Python interpreter

Configuration File

Create ~/.config/digy/config.toml:

[core]
debug = false
cache_dir = "~/.cache/digy"

[docker]
image = "python:3.9-slim"
build = false
no_cache = false

[remote]
port = 22
key = "~/.ssh/id_rsa"

[local]
python = "python3"

pip install -e .

Or using Poetry

poetry install

### Basic Usage

#### Interactive Mode
```bash
# Clone and interact with a repository
digy local https://github.com/octocat/Hello-World.git

# Run with a specific branch
digy local https://github.com/octocat/Hello-World.git --branch main

# Attach local files (available in interactive menu)
digy local https://github.com/pyfunc/repo.git --file ./local_script.py

Non-Interactive Mode

# Run a specific script from a repository
digy local https://github.com/user/repo.git --script path/to/script.py

# With command-line arguments
digy local https://github.com/user/repo.git --script main.py -- --arg1 value1

# Using environment variables
DIGY_RAM_SIZE=4 digy local https://github.com/user/repo.git

Docker Execution

# Run in a Docker container
digy docker https://github.com/user/repo.git

# Specify custom Docker image
digy docker --image python:3.12 https://github.com/user/repo.git

RAM-Based Execution

# Run with RAM disk for temporary files
digy ram https://github.com/user/repo.git --ram-size 2  # 2GB RAM

Getting Help

# Show help
digy --help

# Show version
digy --version

# Command-specific help
digy local --help
digy docker --help
digy ram --help

🔧 Known Limitations

• Docker support requires the docker Python package and Docker daemon running
• Private repository access requires proper SSH/Git credentials setup
• Large repositories may require additional memory allocation
• Windows support has limited testing
• Some edge cases in error handling may need improvement

🔄 Fallback Behavior

DIGY implements a robust fallback mechanism for repository loading:

1. Primary Method: Direct Git clone (requires Git)
2. Fallback 1: HTTPS Git clone (if SSH fails)
3. Fallback 2: Download repository as zip archive
4. Fallback 3: Use local cache if available

This ensures maximum compatibility across different environments and network conditions.

🐳 Docker Support (Optional)

DIGY's Docker integration provides isolated execution environments with these benefits:

• Isolation: Projects run in complete isolation
• Reproducibility: Consistent environments across different systems
• Security: No host system modifications
• Cleanup: Automatic resource cleanup
• Performance: RAM-based storage for temporary files

When to Use Docker

• When you need complete environment isolation
• For consistent testing across different systems
• When working with system dependencies
• For security-sensitive operations

Docker Prerequisites

• Docker Engine installed and running
• Python docker package (pip install docker)
• Sufficient permissions to run Docker commands
• Minimum 2GB of available RAM (4GB recommended)
• At least 1GB of free disk space

⚙️ Configuration

DIGY can be configured through multiple methods (in order of precedence):

1. Command-line arguments (highest priority)
2. Environment variables
3. Configuration file (~/.config/digy/config.toml)
4. Default values (lowest priority)

Environment Variables

Variable	Default	Description
DIGY_RAM_SIZE	1	RAM disk size in GB
DIGY_DOCKER_IMAGE	python:3.12-slim	Default Docker image
DIGY_LOG_LEVEL	INFO	Logging level (DEBUG, INFO, WARNING, ERROR)
DIGY_CACHE_DIR	~/.cache/digy	Cache directory
DIGY_TIMEOUT	300	Operation timeout in seconds
DIGY_AUTO_CLEANUP	true	Automatically clean up temporary files
DIGY_GIT_BIN	git	Path to Git executable
DIGY_PYTHON_BIN	python3	Path to Python interpreter

Configuration File Example

Create ~/.config/digy/config.toml:

[core]
ram_size = 2
timeout = 600
auto_cleanup = true
log_level = "INFO"

[docker]
image = "python:3.12-slim"
use_sudo = false

[git]
bin = "/usr/bin/git"
timeout = 300

[cache]
enabled = true
max_size = "1GB"
path = "~/.cache/digy"

🚀 Advanced Usage

Authentication

GitHub Personal Access Token

export GITHUB_TOKEN="your_github_token"
digy local https://github.com/username/private-repo.git

SSH Authentication

1. Ensure your SSH key is added to the SSH agent:

   eval "$(ssh-agent -s)"
   ssh-add ~/.ssh/your_private_key
   

2. Use SSH URL:

   digy local [e-mail]:username/private-repo.git
   

Advanced Docker Usage

Custom Docker Network

digy docker --network host https://github.com/user/repo.git

Volume Mounts

# Read-only mount
digy docker --mount ./config:/app/config:ro https://github.com/user/repo.git

# Read-write mount
digy docker --mount ./data:/app/data:rw https://github.com/user/repo.git

Environment Variables

# Set environment variables
digy docker -e DEBUG=1 -e API_KEY=secret https://github.com/user/repo.git

# Load from .env file
digy docker --env-file .env https://github.com/user/repo.git

Resource Management

Memory Limits

# Set memory limit (Docker only)
digy docker --memory 4g https://github.com/user/repo.git

# CPU limits
digy docker --cpus 2 https://github.com/user/repo.git

Cleanup

# Clean all temporary files
digy clean --all

# Remove cached repositories
digy clean --cache

# Remove Docker resources
digy clean --docker

🔍 Troubleshooting

Common Issues

Git Authentication Failures

Error: Failed to clone repository: Authentication failed

Solution:

1. Verify your SSH key is added to the SSH agent
2. For HTTPS, ensure you have a valid GitHub token
3. Check repository access permissions

Docker Permission Denied

Got permission denied while trying to connect to the Docker daemon

Solution:

1. Add your user to the docker group:

   sudo usermod -aG docker $USER
   newgrp docker
   

2. Or use sudo (not recommended for security reasons)

Out of Memory

Error: Container ran out of memory

Solution:

1. Increase memory allocation:

   digy docker --memory 8g https://github.com/user/repo.git
   

2. Or reduce memory usage in your application

Debugging

Enable debug logging:

digy --log-level DEBUG local https://github.com/user/repo.git

View logs:

# System logs (Linux)
journalctl -u docker.service

# Application logs
cat ~/.cache/digy/logs/digy.log

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

1. Fork the repository

2. Clone your fork:

   git clone https://github.com/your-username/digy.git
   cd digy
   

3. Install development dependencies:

   poetry install --with dev
   

4. Run tests:

   pytest
   

5. Run linters:

   black .
   flake8
   mypy .
   

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📬 Contact

• GitHub: @pyfunc
• Twitter: @yourhandle
• Email: [e-mail]

🙏 Acknowledgments

• Thanks to all contributors who have helped make DIGY better
• Inspired by tools like Docker, Git, and Jupyter
• Built with ❤️ and Python

📄 Examples

Basic Examples

Hello World

# hello_world.py
print("Hello, DIGY!")

digy local hello_world.py

Environment Information

# env_info.py
import platform
import sys

print("Python Version:", sys.version)
print("Platform:", platform.platform())
print("Current Directory:", os.getcwd())

digy local env_info.py

Data Processing

# data_analysis.py
import pandas as pd

# Load data
df = pd.read_csv('data.csv')

# Process data
summary = df.describe()
print(summary)

# Save results
summary.to_csv('results/summary.csv')

digy local data_analysis.py

Web Scraping

# scraper.py
import requests
from bs4 import BeautifulSoup

def scrape(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    return {
        'title': soup.title.string,
        'links': [a['href'] for a in soup.find_all('a', href=True)]
    }

if __name__ == '__main__':
    result = scrape('https://example.com')
    print(result)

pip install requests beautifulsoup4
digy local scraper.py

Machine Learning

# train.py
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import joblib

# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Evaluate
score = model.score(X_test, y_test)
print(f"Accuracy: {score:.2f}")

# Save model
joblib.dump(model, 'model.joblib')

pip install scikit-learn joblib
digy local train.py

For more examples, see the examples directory.

📦 Installation

# Install from PyPI
pip install digy

# Or install from source
git clone https://github.com/pyfunc/digy
cd digy
pip install -e .

Dependencies

DIGY requires:

• Python 3.8+
• Git
• Docker (for container execution)
• SSH (for remote execution)

Install development dependencies:

pip install -e ".[dev]"

🔄 Execution Environments

DIGY supports multiple execution environments:

1. Local Execution

digy local github.com/pyfunc/digy

• Uses local Python environment
• Creates virtual environment if needed
• Supports file attachments

2. Remote Execution

digy remote user@host github.com/pyfunc/digy script.py

• Executes code on remote host via SSH
• Supports authentication
• Transfers necessary files automatically

3. Docker Execution

digy docker --image python:3.12 github.com/pyfunc/digy script.py

• Runs in isolated container
• Customizable Docker images
• Volume mounting support

4. RAM Execution

digy ram github.com/pyfunc/digy script.py

• Runs code directly in RAM for maximum performance
• No disk I/O overhead
• Ideal for high-performance computing

5. JVM Execution

digy jvm github.com/pyfunc/digy script.py

• Executes Python code on JVM using Jython
• Java integration
• Cross-platform compatibility

🎯 Akronim DIGY

DIGY = Dynamic Interactive Git deploY

• Dynamic - Dynamiczne ładowanie repozytoriów
• Interactive - Interaktywne menu z nawigacją strzałkami
• Git - Integracja z repozytoriami Git
• deploY - Deployment w izolowanych środowiskach

🚀 Funkcjonalności

• ⚡ Szybkie ładowanie - Pobieranie repozytoriów bezpośrednio do pamięci RAM (100MB bazowo)
• 🔒 Izolowane środowiska - Automatyczne tworzenie virtual environment
• 🎮 Interaktywne menu - Nawigacja strzałkami z pomocą
• 📊 Zarządzanie pamięcią - Monitoring i kontrola użycia RAM
• 🔍 Inspekcja kodu - Przeglądanie plików z podświetlaniem składni

📝 API Reference

digy(repo_url, branch='main')

Główna funkcja ładująca repozytorium i uruchamiająca interaktywne menu.

Parametry:

• repo_url (str): URL repozytorium (github.com/user/repo lub pełny URL)
• branch (str): Gałąź do pobrania (domyślnie 'main')

Zwraca:

• str | None: Ścieżka do lokalnego repozytorium lub None przy błędzie

Klasa Deployer

Zarządza deploymentem aplikacji w izolowanych środowiskach.

Klasa InteractiveMenu

Zapewnia interaktywne menu z nawigacją strzałkami.

Klasa MemoryManager

Zarządza alokacją pamięci dla załadowanych repozytoriów.

🔍 Przykłady zaawansowane

Niestandardowa ścieżka

from digy.loader import GitLoader
from digy.deployer import Deployer

loader = GitLoader("/custom/path")
local_path = loader.download_repo("github.com/user/repo")
deployer = Deployer(local_path)

Programowe uruchamianie

from digy import digy

# Uruchomienie z kodu Pythona
# Lokalnie
result = digy.local('github.com/user/repo', 'script.py', ['arg1', 'arg2'])

# W pamięci RAM
result = digy.ram('github.com/user/repo', 'script.py', ['arg1', 'arg2'])

# W Dockerze
result = digy.docker('github.com/user/repo', 'script.py', ['arg1', 'arg2'])

# Wynik zawiera (success, stdout, stderr)
print(f"Sukces: {result[0]}")
print(f"Wyjście: {result[1]}")
if result[2]:
    print(f"Błędy: {result[2]}")

🛠️ Rozwój

Wymagania deweloperskie

• Python 3.8+
• Poetry
• Git

Instalacja deweloperska

git clone https://github.com/pyfunc/digy
cd digy
poetry install
poetry run pytest

Struktura projektu

digy/
├── digy/
│   ├── __init__.py      # Główny moduł
│   ├── loader.py        # Ładowanie repozytoriów
│   ├── deployer.py      # Deployment and execution
│   ├── interactive.py   # Interactive menu
│   ├── cli.py          # Command line interface
│   ├── environment.py   # Environment management
│   ├── auth.py         # Authentication providers
│   └── version.py      # Version information
├── tests/              # Tests
├── examples/           # Usage examples
│   ├── basic/          # Basic examples
│   ├── env/            # Environment examples
│   └── attachments/    # File attachment examples
├── pyproject.toml      # Konfiguracja Poetry
└── README.md          # Dokumentacja

📄 Licencja

Apache Software License - Zobacz plik LICENSE dla szczegółów.

🤝 Wkład

Zapraszamy do współpracy! Prosimy o:

1. Forkowanie repozytorium
2. Tworzenie feature branch
3. Commit zmian
4. Push do branch
5. Tworzenie Pull Request

📞 Wsparcie

• Issues: https://github.com/pyfunc/digy/issues
• Email: [e-mail]
• Dokumentacja: https://github.com/pyfunc/digy

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DIGY - Twój interaktywny asystent do deploymentu aplikacji Python! 🚀