Instantiates a multiprocessing.Process to run a target function in a separate process for parallel execution.

Expresses the intention to run a worker function concurrently in a separate process with a given argument.

When you need to execute a function in parallel to utilize multiple CPU cores or isolate side‑effects.

Used in multiprocessing programs after importing multiprocessing.Process and defining a callable worker function.

p = Process(target=callable, args=(arg,))

Process(target=..., args=(...,))

target: any callable (function, method, lambda); args: tuple of arguments to pass to target; optional kwargs: keyword arguments dictionary

• Process
• target
• args
• worker
• arg
• multiprocessing
• start
• join

• target can be any callable
• args can be any tuple (including empty)
• can also use kwargs keyword argument
• can add daemon=True

Failing to wrap a single argument in a tuple (args=arg instead of args=(arg,)); forgetting to call .start() on the Process object; passing non‑picklable objects as arguments; confusing threading.Thread with multiprocessing.Process

threading.Thread(target=worker, args=(arg,)) uses threads instead of separate processes; multiprocessing.Pool provides a pool of workers for task distribution

Confusing threading with multiprocessing; assuming arguments are shared memory (they are pickled/copied); neglecting to call .start() so the process never runs

• multiprocessing-process-creation

The args tuple must contain picklable objects; a single argument must be expressed as a length‑one tuple, e.g., (arg,)

Signals the developer’s intent to offload work to a separate process for parallel execution or isolation

Think of a Process as a worker bee: you tell it the job (target) and the data (args) it should carry.

Remember to call .start() to begin execution and .join() to wait for completion; otherwise the process may never run or become a zombie.

Consider using multiprocessing.Pool or concurrent.futures.ProcessPoolExecutor for higher‑level task distribution.