Unexpected need to protect lines inside script for multiprocessing

[vigji]

Not urgent matter as I figured a way around the issue, reporting here to make sure I am not misunderstanding something and in case someone might need it. Also, this behavior appeared in the last month or so, so maybe it is still unreported?

I am working on a Windows machine (anaconda, python 3.10); had some troubles using multiprocessing in any possible scenario since something like this:

from pathlib import Path
from datetime import datetime
from spikeinterface.core import load_extractor

data_path = Path(r"...\test-data")
test_data = load_extractor(data_path)

temp_path = data_path.parent / f"test_dataset_resaved_{datetime.now().strftime('%Y%m%d-%H%M%S')}"
test_data.save(folder=temp_path, n_jobs=-1)

would result in (expand to show full error traceback):

RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ...

```shell write_binary_recording with n_jobs = 20 and chunk_size = 30000 Traceback (most recent call last): File "", line 1, in File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\spawn.py", line 125, in _main prepare(preparation_data) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\runpy.py", line 86, in _run_code exec(code, run_globals) File "c:\Users\SNeurobiology\code\ephys-preprocessing\benchmarking\debug_compression.py", line 10, in test_data.save(folder=temp_path, n_jobs=-1) File "C:\Users\SNeurobiology\code\spikeinterface\src\spikeinterface\core\base.py", line 760, in save loaded_extractor = self.save_to_folder(**kwargs) File "C:\Users\SNeurobiology\code\spikeinterface\src\spikeinterface\core\base.py", line 838, in save_to_folder cached = self._save(folder=folder, verbose=verbose, **save_kwargs) File "C:\Users\SNeurobiology\code\spikeinterface\src\spikeinterface\core\baserecording.py", line 462, in _save write_binary_recording(self, file_paths=file_paths, dtype=dtype, **job_kwargs) File "C:\Users\SNeurobiology\code\spikeinterface\src\spikeinterface\core\core_tools.py", line 314, in write_binary_recording executor.run() File "C:\Users\SNeurobiology\code\spikeinterface\src\spikeinterface\core\job_tools.py", line 391, in run results = executor.map(function_wrapper, all_chunks) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\concurrent\futures\process.py", line 766, in map results = super().map(partial(_process_chunk, fn), File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\concurrent\futures\_base.py", line 610, in map fs = [self.submit(fn, *args) for args in zip(*iterables)] File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\concurrent\futures\_base.py", line 610, in fs = [self.submit(fn, *args) for args in zip(*iterables)] File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\concurrent\futures\process.py", line 737, in submit self._adjust_process_count() File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\concurrent\futures\process.py", line 697, in _adjust_process_count self._spawn_process() File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\concurrent\futures\process.py", line 714, in _spawn_process p.start() File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\process.py", line 121, in start self._popen = self._Popen(self) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\context.py", line 336, in _Popen return Popen(process_obj) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__ prep_data = spawn.get_preparation_data(process_obj._name) File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "C:\Users\SNeurobiology\miniconda3\envs\ephys-env\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ```

The workaround was to refactor my script to be the following:

from pathlib import Path
from datetime import datetime
from spikeinterface.core import load_extractor

data_path = Path(r"...\test-data")
test_data = load_extractor(data_path)

temp_path = data_path.parent / f"test_dataset_resaved_{datetime.now().strftime('%Y%m%d-%H%M%S')}"

if __name__ == "__main__":
    test_data.save(folder=temp_path, n_jobs=-1)

And now it works. Still, I can't understand why I would need to protect my script from import - although the secrets of multiprocessing are not super clear to me and maybe I am missing something obvious.

This started with pulling the latest version of the package, when I was playing with it approx. one month ago I do not remember encountering the same issue even though I was for sure using multiprocessing already.

Thank you so very much for the amazing package!

[alejoe91]

@vigji Thanks for reporting this. Nothing obvious comes to mind but we'll investigate!

[samuelgarcia]

My guess here is that on window, which does a spawn by default (quite slow) the save() finish before all jobs are spawn. Do you have same behavior with 1) long recording 2) small n_jobs ?

[vigji]

I tried with a 100 GB rec and 2 two jobs and got the same issue. Let me know if there's more tests that I can run!

Also, it works in a Jupyter notebook without protection - maybe this is trivial?. However, there I have an issue with the progress bar: it gets displayed but it does not update (dave not checked issues in detail to see if it was reported already).

[zm711]

I just want to confirm that I was running progress_bar=True in an IPython Console and also the progress bar renders, but then does not advance (I'm on the dev version). I wonder if there is an issue with tqdm vs tqdm.notebook or if this is a Windows issue only. If I find a tqdm issue linked to this I'll post here.

[h-mayorquin]

Thanks a lot for sharing your example with a full error trace.

This is expected behavior for windows. You should always run multiprocessing inside a protected way as it is stated in the docs:

https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods

See the section Safe importing of main module.

In fact, know that I know this. I wonder why it does not fail all the time in other places. The confusing thing is that it works just fine in most cases though when it should not ! Like, why this did use to work?

Se pytorch recommendation on the same issue: https://pytorch.org/docs/stable/notes/windows.html#multiprocessing-error-without-if-clause-protection