run_all.py

"""
MLflow Complete Tutorial - Master Script

This script runs all four MLflow components in sequence:
1. MLflow Tracking
2. MLflow Projects
3. MLflow Models
4. MLflow Model Registry

Run this script to see the complete MLflow workflow in action.
"""

import sys
import time
import subprocess
from datetime import datetime


def print_banner(text, char="=", width=80):
    """Print a formatted banner."""
    print("\n" + char * width)
    print(f"  {text}")
    print(char * width + "\n")


def print_component_header(number, name):
    """Print component header."""
    print_banner(f"Component {number}: {name}", char="=")
    print(f"Starting at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")


def print_status(message, status="info"):
    """Print status message."""
    symbols = {
        "info": "ℹ",
        "success": "✓",
        "error": "✗",
        "warning": "⚠"
    }
    symbol = symbols.get(status, "•")
    print(f"{symbol} {message}")


def run_component(script_name, component_name, component_number):
    """
    Run a tutorial component.
    
    Args:
        script_name: Name of the Python script
        component_name: Display name of the component
        component_number: Component number (1-4)
    
    Returns:
        True if successful, False otherwise
    """
    print_component_header(component_number, component_name)
    
    try:
        print_status(f"Executing {script_name}...", "info")
        
        # Run the component
        result = subprocess.run(
            [sys.executable, script_name],
            capture_output=False,
            text=True
        )
        
        if result.returncode == 0:
            print_status(f"{component_name} completed successfully!", "success")
            return True
        else:
            print_status(f"{component_name} failed with return code {result.returncode}", "error")
            return False
    
    except Exception as e:
        print_status(f"Error running {component_name}: {str(e)}", "error")
        return False


def check_mlflow_ui():
    """Check if MLflow UI is running."""
    print_status("Checking MLflow UI availability...", "info")
    
    try:
        import urllib.request
        import urllib.error
        
        try:
            urllib.request.urlopen("http://localhost:5000", timeout=1)
            print_status("MLflow UI is running at http://localhost:5000", "success")
            return True
        except urllib.error.URLError:
            print_status("MLflow UI is not running", "warning")
            print("  To start MLflow UI, run in a separate terminal:")
            print("  $ mlflow ui")
            print("  Then visit: http://localhost:5000")
            return False
    except Exception as e:
        print_status(f"Could not check MLflow UI: {str(e)}", "warning")
        return False


def print_summary(results):
    """Print execution summary."""
    print_banner("Execution Summary", char="=")
    
    total = len(results)
    successful = sum(1 for r in results.values() if r)
    failed = total - successful
    
    print(f"Total Components: {total}")
    print(f"Successful: {successful}")
    print(f"Failed: {failed}")
    print()
    
    print("Component Results:")
    print("-" * 80)
    
    for component, success in results.items():
        status = "✓ Success" if success else "✗ Failed"
        print(f"  {component:<40} {status}")
    
    print("-" * 80)
    
    if all(results.values()):
        print_status("All components completed successfully!", "success")
        return True
    else:
        print_status("Some components failed. Check the output above for details.", "error")
        return False


def print_next_steps():
    """Print next steps and recommendations."""
    print_banner("Next Steps", char="=")
    
    print("1. Explore MLflow UI:")
    print("   - Start the UI: mlflow ui")
    print("   - Visit: http://localhost:5000")
    print("   - Browse experiments, runs, and models")
    print()
    
    print("2. Review Individual Components:")
    print("   - Component 1 (Tracking): python 1_tracking.py")
    print("   - Component 2 (Projects): python 2_projects.py")
    print("   - Component 3 (Models): python 3_models.py")
    print("   - Component 4 (Registry): python 4_model_registry.py")
    print()
    
    print("3. Experiment Further:")
    print("   - Modify hyperparameters in the scripts")
    print("   - Try different ML algorithms")
    print("   - Add your own datasets")
    print("   - Explore MLflow's advanced features")
    print()
    
    print("4. Key Files to Review:")
    print("   - README.md: Complete documentation")
    print("   - MLproject: Project configuration")
    print("   - requirements.txt: Python dependencies")
    print("   - utils.py: Shared utility functions")
    print()
    
    print("5. Production Deployment:")
    print("   - Set up remote tracking server")
    print("   - Configure artifact storage (S3, Azure, GCS)")
    print("   - Deploy models to production endpoints")
    print("   - Implement CI/CD pipelines")
    print()


def main():
    """Main execution function."""
    # Print welcome message
    print_banner("MLflow Complete Tutorial", char="=")
    print("This script will run all four MLflow components in sequence.")
    print("Each component demonstrates key MLflow features.")
    print()
    print("The tutorial covers:")
    print("  1. MLflow Tracking - Experiment tracking and logging")
    print("  2. MLflow Projects - Reproducible runs")
    print("  3. MLflow Models - Model packaging and deployment")
    print("  4. MLflow Model Registry - Model versioning and lifecycle")
    print()
    
    # Check MLflow UI
    check_mlflow_ui()
    print()
    
    # Confirm execution
    print("-" * 80)
    response = input("Press Enter to continue or Ctrl+C to cancel... ")
    print()
    
    start_time = time.time()
    
    # Define components
    components = [
        ("1_tracking.py", "MLflow Tracking", 1),
        ("2_projects.py", "MLflow Projects", 2),
        ("3_models.py", "MLflow Models", 3),
        ("4_model_registry.py", "MLflow Model Registry", 4)
    ]
    
    # Track results
    results = {}
    
    # Run each component
    for script, name, number in components:
        success = run_component(script, name, number)
        results[f"Component {number}: {name}"] = success
        
        if not success:
            print_status(f"Component {number} failed. Continuing to next component...", "warning")
        
        # Brief pause between components
        if number < 4:
            print("\n" + "." * 80)
            time.sleep(2)
    
    # Calculate execution time
    end_time = time.time()
    duration = end_time - start_time
    minutes = int(duration // 60)
    seconds = int(duration % 60)
    
    # Print summary
    print()
    print_summary(results)
    print()
    print(f"Total execution time: {minutes} minutes, {seconds} seconds")
    print()
    
    # Print next steps
    print_next_steps()
    
    # Final message
    print_banner("Tutorial Complete!", char="=")
    
    if all(results.values()):
        print("🎉 Congratulations! You've successfully completed the MLflow tutorial!")
        print()
        print("You now understand:")
        print("  ✓ How to track experiments with MLflow Tracking")
        print("  ✓ How to create reproducible projects with MLflow Projects")
        print("  ✓ How to package models with MLflow Models")
        print("  ✓ How to manage model lifecycle with MLflow Model Registry")
        print()
        print("Happy experimenting with MLflow! 🚀")
        return 0
    else:
        print("⚠ The tutorial completed with some errors.")
        print("Please review the output above for details.")
        return 1


if __name__ == "__main__":
    try:
        exit_code = main()
        sys.exit(exit_code)
    except KeyboardInterrupt:
        print("\n\n" + "=" * 80)
        print("  Tutorial interrupted by user")
        print("=" * 80)
        print("\nYou can run individual components separately:")
        print("  $ python 1_tracking.py")
        print("  $ python 2_projects.py")
        print("  $ python 3_models.py")
        print("  $ python 4_model_registry.py")
        sys.exit(1)
    except Exception as e:
        print(f"\n❌ Unexpected error: {str(e)}")
        import traceback
        traceback.print_exc()
        sys.exit(1)