Of course. This is a very common point of confusion for developers moving from Linux/macOS to Windows. Let's break it down clearly.

The Short Answer

Python's os.fork() does not work on Windows.

Attempting to use os.fork() on a Windows system will raise a NotImplementedError.

The Detailed Explanation: Why It Doesn't Work

The fundamental reason is that Windows and Unix-like systems (Linux, macOS) have different underlying architectures for process creation.

How fork() Works on Linux/macOS (Copy-on-Write)

When you call os.fork() on a Unix-like system:

1. The kernel creates a new process that is an almost exact copy of the parent process.
2. This copy includes the same memory space, the same file descriptors, and the same program counter.
3. Crucially, the copying of the parent's memory pages is initially lazy. This mechanism is called Copy-on-Write (CoW). Both the parent and child processes point to the same physical memory pages.
4. If either process tries to modify a memory page, the kernel makes a real, separate copy of that page just for the modifying process. This makes the initial fork() call extremely fast, as it doesn't require duplicating gigabytes of RAM.

How Windows Creates Processes (The CreateProcess API)

Windows does not have a fork()-like system call. Instead, it uses the Win32 API function CreateProcess. This function works very differently:

1. It requires the fully qualified path to a new program (an .exe file) to run.
2. It takes a command line string to be passed to that new program.
3. The new process is a brand new instance of the specified executable. It does not inherit the memory space of the parent process.

Because of this architectural difference, implementing a true fork() on top of CreateProcess would be incredibly inefficient. It would require manually copying the entire parent's memory space, which is a complex and slow operation that defeats the purpose of fork()'s performance benefits.

The Pythonic Solution: multiprocessing Module

Since os.fork() is not available, the standard and correct way to achieve parallelism on Windows (and it works perfectly on Unix-like systems too) is to use Python's built-in multiprocessing module.

The multiprocessing module is designed to provide a consistent interface for creating processes across different operating systems. It handles all the platform-specific details for you.

Here are the most common ways to use it:

The Process Class (Direct Equivalent)

This is the most direct way to create a new process that runs a specific function, similar in spirit to what you'd do with fork() and exec().

import multiprocessing
import os
import time
def worker_function(message):
    """A function that will be run in a child process."""
    pid = os.getpid()
    print(f"[Child Process {pid}] Started. Message: '{message}'")
    time.sleep(2) # Simulate some work
    print(f"[Child Process {pid}] Finished.")
if __name__ == "__main__":
    print(f"[Parent Process {os.getpid()}] Starting...")
    # Create a Process object
    # The 'target' is the function to run in the new process
    # The 'args' are the arguments to pass to that function (as a tuple)
    p = multiprocessing.Process(target=worker_function, args=("Hello from child!",))
    # Start the process
    p.start()
    print(f"[Parent Process {os.getpid()}] Child process has been started.")
    print(f"[Parent Process {os.getpid()}] Main program continues to run.")
    # Wait for the child process to complete
    p.join()
    print(f"[Parent Process {os.getpid()}] Child process has finished. Exiting.")

How to Run: Save this as a file (e.g., my_script.py) and run it from your terminal:

python my_script.py

You will see the output from both the parent and child process, demonstrating that they are running concurrently.

The Pool Class (For Parallelizing Tasks)

If you have a list of tasks and want to distribute them across a fixed number of worker processes, multiprocessing.Pool is the ideal tool.

import multiprocessing
import time
def square(n):
    """Function to square a number."""
    time.sleep(0.5) # Simulate some work
    result = n * n
    print(f"Process {multiprocessing.current_process().name}: Squared {n} to get {result}")
    return result
if __name__ == "__main__":
    numbers = [1, 2, 3, 4, 5, 6, 7, 8]
    # Create a pool of 4 worker processes
    with multiprocessing.Pool(processes=4) as pool:
        # 'map' applies the square function to every item in the list
        results = pool.map(square, numbers)
    print("\nFinal results:", results)

How to Run: Save and run as before. You'll see that the numbers are processed by the available worker processes in the pool, demonstrating efficient parallel execution.

Important: The if __name__ == "__main__": Guard

When using multiprocessing on Windows, you must wrap your main process logic in an if __name__ == "__main__": block.

Why? On Windows, every new process is created by re-importing the main script. Without this guard, the child process would re-execute the code that creates its own child processes, leading to an exponential explosion of processes and a crash. The if __name__ == "__main__": block ensures that the process-creation code only runs in the original parent process.

Summary Table