Complete Process Isolation

A full blueprint for running memory-intensive Python tasks without affecting your primary server.

1. The Master (main.py)

import subprocess
import time

def start_orchestrator():
    print("Master: Server is idle at 50MB.")
    
    # Trigger the worker as a separate entity
    subprocess.run(["python", "worker.py"])
    
    print("Master: Worker is gone. RAM is clean.")

if __name__ == "__main__":
    start_orchestrator()

The Master script is your primary application. It uses the subprocess module to bridge the gap to a new memory space.

Role: Orchestration & Long-term persistence.

2. The Worker (worker.py)

# HEAVY imports happen here
import heavy_library 

def execute_task():
    print("Worker: Loaded heavy library (150MB spike).")
    heavy_library.run()

if __name__ == "__main__":
    execute_task()

The Worker script is completely independent. It is designed to be "born" and "die" for a single execution.

The OS Cleanup: When this script reaches the end of its execution, the OS terminates the process and releases its RAM immediately.

Live RAM Lifecycle

System Status: Idle

Click to see how Master and Worker interact.