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Choose the right multiprocessing start method in Python to optimize performance and ensure cross-platform compatibility.

Understanding multiprocessing.get_start_method in Python

The multiprocessing module in Python is a powerful tool that allows you to run multiple processes simultaneously, leveraging multiple CPU cores for parallel processing. One of the functions provided by this module is multiprocessing.get_start_method(). This function is used to determine the method used to start new processes within the multiprocessing framework.

What is multiprocessing.get_start_method?

The multiprocessing.get_start_method function returns the method that is currently used for starting new processes. This is important because Python supports different start methods that can affect the behavior and performance of the multiprocessing tasks. The start method can influence the way resources are shared between processes and how they are initiated.




  • allow_none (optional): A boolean argument. If set to True, the function can return None if no start method has been set. The default value is False, meaning it will raise an exception if no start method has been set.

Return Value

The function returns a string that represents the current start method. Possible values include:

  • 'fork': This method is available on Unix platforms. The parent process is forked, and the child process inherits the resources of the parent.
  • 'spawn': This method is available on Unix and Windows. A fresh Python interpreter process is started. The child process does not inherit resources from the parent.
  • 'forkserver': This method is available on Unix platforms. A server process is started, which forks new processes on request.
  • None: If no start method is set and allow_none is True.

Importance of Start Methods

Different start methods can have significant implications for your multiprocessing applications:


  • Advantages:
    • Faster process creation as it avoids the overhead of starting a new interpreter.
    • Useful for applications that need to share a large amount of data between processes.
  • Disadvantages:
    • Not available on Windows.
    • Can lead to issues with thread safety and resource sharing.


  • Advantages:
    • Safer and more consistent across platforms, including Windows.
    • Each process is independent, reducing issues related to resource sharing.
  • Disadvantages:
    • Slower process creation compared to fork.
    • Requires re-importing modules in the child process.


  • Advantages:
    • Combines advantages of both fork and spawn.
    • Suitable for multi-threaded programs where fork can be unsafe.
  • Disadvantages:
    • Slightly more complex to set up.
    • Limited to Unix platforms.

Example Usage

Here is a simple example to illustrate how you can use multiprocessing.get_start_method to check the current start method:

import multiprocessing

def worker():
    print("Worker process started")

if __name__ == "__main__":
    current_method = multiprocessing.get_start_method()
    print(f"Current start method: {current_method}")

    # Set the start method to 'spawn'
    multiprocessing.set_start_method('spawn', force=True)
    print(f"Start method after setting: {multiprocessing.get_start_method()}")

    p = multiprocessing.Process(target=worker)

In this example, the current start method is retrieved using multiprocessing.get_start_method(). Then, the start method is set to 'spawn' using multiprocessing.set_start_method('spawn', force=True). Finally, a new process is created and started using the specified start method.

When to Use Which Method

  • Use fork:
    • When you are working on a Unix-based system.
    • When you need faster process creation and are not concerned about thread safety.
    • When your application benefits from sharing a large amount of data between processes.
  • Use spawn:
    • When you need cross-platform compatibility, especially on Windows.
    • When you want to avoid issues related to resource sharing and thread safety.
    • When you are fine with the overhead of starting a new interpreter for each process.
  • Use forkserver:
    • When you need a balance between fork and spawn.
    • When your application is multi-threaded and you need a safer start method.
    • When you are working on a Unix-based system.


Understanding and choosing the correct start method in the multiprocessing module is crucial for optimizing the performance and reliability of your parallel processing tasks. The multiprocessing.get_start_method function provides a straightforward way to check the current start method, allowing you to make informed decisions based on your application’s requirements. By leveraging the different start methods effectively, you can ensure that your multiprocessing applications run efficiently and safely across different platforms.