Question: I am trying and failing to reproduce and understand a problem I saw where multiprocessing failed when using a python module written in C++. My understanding was that the problem is that multiprocessing needs to pickle the function it is using. So I made my_module.cpp as follows: #include <pybind11/pybind11.h> int add(int input_number) { return input_number + 10; } PYBIND11_MODULE(my_module, m) { m.doc() = "A simple module implemented in C++ to add 10 to a number."; m.def("add", &add, "Add 10 to a number"); }  After pip install pybind11  I compiled with: c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) my_module.cpp -o my_module$(python3-config --extension-suffix)  I can import my_module and it works as expected. I can test if it can be pickled with: import my_module import pickle # Use the add function print(my_module.add(5)) # Outputs: 15 # Attempt to pickle the module try: pickle.dumps(my_module) except TypeError as e: print(f"Pickling error: {e}") # Expected error  which outputs Pickling error: cannot pickle 'module' object as expected. Now I tested multiprocessing and was surprising that it worked. I was expecting it to give a pickling error. import my_module from multiprocessing import Pool # A wrapper function to call the C++ add function def parallel_add(number): return my_module.add(number) if __name__ == "__main__": numbers = [1, 2, 3, 4, 5] try: # Create a pool of worker processes with Pool(processes=2) as pool: results = pool.map(parallel_add, numbers) print(results) # If successful, prints the results except Exception as e: print(f"Multiprocessing error: {e}")  How can I make a Python module in C++ with pybind11 which fails with multiprocessing because of a pickling error? I am using Linux

Answer: I don't think your code tries to pickle a module as-is? If you redefine parallel_add to take a module as an argument, then use a partial to pass my_module into it, you can force Python to do that. import my_module from functools import partial from multiprocessing import Pool # Same wrapper, but now takes a module as an argument def parallel_add(module, number): return module.add(number) if __name__ == "__main__": numbers = [1, 2, 3, 4, 5] with Pool(processes=2) as pool: results = pool.map(partial(parallel_add, my_module), numbers) print(results)  This throws the error you were expecting: Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/home/anerdw/stackoverflow/unpickling.py", line 13, in <module> results = pool.map(partial(parallel_add, my_module), numbers) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/multiprocessing/pool.py", line 367, in map return self._map_async(func, iterable, mapstar, chunksize).get() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/multiprocessing/pool.py", line 774, in get raise self._value File "/usr/lib/python3.11/multiprocessing/pool.py", line 540, in _handle_tasks put(task) File "/usr/lib/python3.11/multiprocessing/connection.py", line 205, in send self._send_bytes(_ForkingPickler.dumps(obj)) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) TypeError: cannot pickle 'module' object  You can also get a pickling-related multiprocessing error much more quickly by cutting out the wrapper and trying to pickle the function directly. import my_module from multiprocessing import Pool if __name__ == "__main__": numbers = [1, 2, 3, 4, 5] with Pool(processes=2) as pool: results = pool.map(my_module.add, numbers) print(results)  Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/home/anerdw/stackoverflow/unpickling.py", line 8, in <module> results = pool.map(my_module.add, numbers) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/multiprocessing/pool.py", line 367, in map return self._map_async(func, iterable, mapstar, chunksize).get() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/multiprocessing/pool.py", line 774, in get raise self._value File "/usr/lib/python3.11/multiprocessing/pool.py", line 540, in _handle_tasks put(task) File "/usr/lib/python3.11/multiprocessing/connection.py", line 205, in send self._send_bytes(_ForkingPickler.dumps(obj)) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) TypeError: cannot pickle 'PyCapsule' object