pemexe 1.1.0

🚀 Python Execution Manager - A powerful, modern tool for managing, scheduling, and executing Python scripts and projects with comprehensive logging, flexible scheduling, and dependency management. Perfect for DevOps, data science, and automation workflows.

PEM - Python Execution Manager 🚀

A powerful, modern CLI tool for managing, scheduling, and executing Python scripts and projects with ease.

✨ What is PEM?

PEM (Python Execution Manager) is your comprehensive solution for managing Python script and project execution. Whether you need to run scripts on-demand, schedule automated tasks, or manage complex Python workflows, PEM provides an intuitive CLI interface with powerful scheduling capabilities.

🎯 Perfect for:

• Data Scientists scheduling ETL pipelines and analysis scripts
• DevOps Engineers automating deployment and maintenance tasks
• Python Developers managing multiple projects and scripts
• System Administrators running scheduled monitoring and backup tasks
• Anyone who needs reliable Python execution with proper logging and scheduling

🌟 Key Features

📋 Unified Job Management

• Two Job Types: Handle both standalone Python scripts and full projects
• Smart Auto-execution: Automatically run jobs after creation (configurable)
• Dependency Management: Specify Python dependencies for script-type jobs
• Enable/Disable Control: Easy job activation and deactivation

⏰ Flexible Scheduling System

• Multiple Schedule Types:
  • once - Execute at a specific date/time
  • interval - Run every X seconds/minutes/hours/days
  • cron - Use cron-style expressions for complex schedules
  • until_done - Retry until successful execution
• Unified Execution: Single run command for immediate + optional scheduling
• Background Processing: All scheduled jobs run automatically in the background

📊 Comprehensive Monitoring & Logging

• Detailed Execution Logs: Every run logged with timestamps, status, and output
• Real-time Status: View system overview with job counts and schedules
• SQLite Database: Persistent local storage for all jobs and execution history
• Rich CLI Output: Color-coded feedback with emojis for better UX

🛠 Developer-Friendly Interface

• Intuitive Commands: Simple, memorable command structure
• Type-safe CLI: Built with modern Typer framework
• Clean Help Output: No clutter - only essential information shown
• Actionable Error Messages: Clear guidance when things go wrong

🚀 Quick Start

Installation

# If using in your project
uv add pemexe
# OR
pip install pemexe

# If using as tool
uvx --from pemexe pem

# Clone and install locally
git clone https://github.com/arian24b/pem.git
cd pem
uv sync

# Or install from source
pip install -e .

Basic Usage

# Add and run a Python script immediately
pem add --path ./my_script.py --script --name "data-processor"

# Add a project without auto-execution
pem add --path ./my_project --name "web-app" --no-auto-run

# Execute an existing job
pem run --name "data-processor"

# Execute and schedule for hourly runs
pem run --name "data-processor" --schedule --type interval --minutes 60

# View all jobs
pem show

# Check system status
pem status

📖 Command Reference

Job Management Commands

pem add - Create New Jobs

Create a new job to execute Python scripts or projects.

# Add a simple script with dependencies
pem add --path ./script.py --script --name "analyzer" --with pandas requests

# Add a project (uses existing environment)
pem add --path ./my_project --name "web-server"

# Add disabled job (no auto-execution)
pem add --path ./script.py --script --disabled --no-auto-run

# Add with specific Python version
pem add --path ./script.py --script --python 3.11 --with numpy

Key Options:

• --path, -p: Path to Python script or project directory (required)
• --name, -n: Unique job name (auto-generated if not provided)
• --script, -s: Treat as single Python script (vs. project)
• --with, -w: Python dependencies to install (scripts only)
• --python, -v: Required Python version (scripts only)
• --enabled/--disabled, -e: Enable/disable job execution
• --auto-run/--no-auto-run: Execute immediately after creation

pem show - Display Job Information

Display details of jobs (all jobs if no filter specified).

# Show all jobs
pem show

# Show specific job by name
pem show --name "data-processor"

# Show specific job by ID
pem show --id 1

pem update - Modify Existing Jobs

Update properties of an existing job.

# Enable a disabled job
pem update --name "data-processor" --enabled

# Update job path
pem update --id 1 --path ./new_script.py

# Change job type and dependencies
pem update --name "analyzer" --script --with pandas numpy matplotlib

pem delete - Remove Jobs

Remove a job permanently from the system.

# Delete by name
pem delete --name "old-job"

# Delete by ID
pem delete --id 1

Execution Commands

pem run - Execute Jobs (Immediate + Optional Scheduling)

Execute a job immediately, with optional recurring schedule setup.

# Execute job immediately
pem run --name "data-processor"

# Execute and set up hourly schedule
pem run --name "monitor" --schedule --type interval --minutes 60

# Execute by ID with daily schedule
pem run --id 1 --schedule --type interval --minutes 1440

Key Options:

• --id, -i: ID of job to execute
• --name, -n: Name of job to execute
• --schedule/--no-schedule, -s: Set up recurring schedule
• --type, -t: Schedule type (interval, once, cron, until_done)
• --minutes, -m: Interval in minutes (for interval scheduling)

Scheduling Commands

pem cron - Advanced Job Scheduling

Schedule a job for automatic execution using various timing options.

# Schedule every 30 minutes
pem cron --name "monitor" --type interval --minutes 30

# Schedule daily at 9 AM using cron
pem cron --name "report" --type cron --cron-hour 9 --cron-minute 0

# Schedule one-time execution
pem cron --name "backup" --type once --date "2024-12-31T23:59:59"

# Retry until success
pem cron --name "flaky-job" --type until_done --max-retries 5 --retry-interval 300

Schedule Types:

• interval: --seconds, --minutes, --hours, --days
• once: --date (ISO format: 2024-01-01T10:00:00)
• cron: --cron-minute, --cron-hour, --cron-day, --cron-month, --cron-dow
• until_done: --max-retries, --retry-interval

pem crons - List Scheduled Jobs

List all jobs currently scheduled for automatic execution.

pem crons

pem cancel - Cancel Scheduled Jobs

Cancel a scheduled job by its scheduler ID.

# Cancel specific scheduled job (use ID from 'pem crons')
pem cancel --scheduler-id "pem_job_interval_1_20241201_120000"

System Commands

pem status - System Overview

Display system overview with job counts and scheduling statistics.

pem status

Shows:

• Total jobs (enabled/disabled, scripts/projects)
• Active scheduled jobs with next run times
• System health at a glance

🏗 Architecture & Design

PEM is built with modern Python technologies:

• AsyncTyper: Custom async CLI framework extending Typer
• SQLAlchemy: Robust async database ORM for data persistence
• APScheduler: Reliable background job scheduling
• Faker: Automatic job name generation
• AsyncIO: Efficient asynchronous execution throughout

Database Schema

• Jobs Table: Stores job definitions, paths, dependencies, and settings
• Execution History: Tracks all job runs with status, logs, and timing
• Schedule Tracking: Manages active schedules and their configurations

🔧 Configuration & Setup

PEM works out of the box with minimal configuration:

• Database: SQLite (pem.db) in working directory
• Logs: Stored in ./logs/ with timestamped filenames
• Auto-initialization: Database and tables created automatically
• Environment Isolation: Uses proper Python environment handling

📁 Project Structure

pem/
├── pem/
│   ├── cli.py              # Main CLI interface with all commands
│   ├── settings.py         # Configuration and settings
│   ├── core/
│   │   ├── executor.py     # Job execution engine
│   │   └── scheduler.py    # Background scheduling manager
│   └── db/
│       ├── database.py     # Database configuration & sessions
│       └── models.py       # SQLAlchemy data models
├── logs/                   # Execution logs directory
├── pem.db                 # SQLite database file
└── pyproject.toml         # Project configuration with uv

� Common Workflows

1. Quick Script Development & Testing

# Add and test a script immediately
pem add --path ./analysis.py --script --name "analysis" --with pandas

# Iterate and re-run during development
pem run --name "analysis"

2. Production Data Pipeline Setup

# Add ETL script
pem add --path ./etl_pipeline.py --script --name "daily-etl" --no-auto-run

# Schedule for daily execution at 2 AM
pem cron --name "daily-etl" --type cron --cron-hour 2 --cron-minute 0

# Monitor scheduled jobs
pem crons
pem status

3. System Monitoring Setup

# Add monitoring script
pem add --path ./health_check.py --script --name "health-monitor" --no-auto-run

# Set up 5-minute monitoring
pem cron --name "health-monitor" --type interval --minutes 5

# Check system status
pem status

4. Project Management

# Add a Python project
pem add --path ./my_fastapi_app --name "api-server" --no-auto-run

# Run when needed
pem run --name "api-server"

# Update project path as it evolves
pem update --name "api-server" --path ./updated_fastapi_app

🆚 Why Choose PEM?

vs. Cron/Task Scheduler

• ✅ Cross-platform: Works identically on Windows, macOS, and Linux
• ✅ Python-native: No shell scripting required
• ✅ Dependency management: Built-in package handling
• ✅ Rich logging: Detailed execution history and status tracking
• ✅ User-friendly: Modern CLI with helpful error messages

vs. Other Python Task Runners

• ✅ Zero configuration: Works immediately without complex setup files
• ✅ Local storage: No external databases or services required
• ✅ Unified interface: Single tool for all execution needs
• ✅ Flexible scheduling: Multiple timing patterns in one tool
• ✅ Developer UX: Intuitive commands with excellent error handling

vs. Manual Script Management

• ✅ Automated scheduling: Set-and-forget execution
• ✅ Centralized management: All scripts in one place
• ✅ Execution tracking: History, logs, and status monitoring
• ✅ Error handling: Retry mechanisms and failure notifications
• ✅ Environment isolation: Proper dependency and version management

🤝 Contributing

We welcome contributions! To get started:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes with tests
4. Run the test suite: pytest
5. Submit a pull request

Development Setup

git clone https://github.com/arian24b/pem.git
cd pem
uv sync

📝 License

MIT License - see LICENSE file for details.

🙋‍♀️ Support & Community

• Issues: GitHub Issues
• Feature Requests: GitHub Discussions
• Documentation: See the comprehensive Tool Overview

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Made with ❤️ by Arian Omrani

PEM - Schedule and execute Python scripts and projects with ease 🐍✨