By default, multiprocessing uses fork() without exec() on POSIX. For a variety of reasons this can lead to inconsistent state in subprocesses: module-level globals are copied, which can mess up logging, threads don't survive fork(), etc..

The end results vary, but quite often are silent lockups.

In real world usage, this results in users getting mysterious hangs they do not have the knowledge to debug.

The fix for these people is to use "spawn" by default, which is the default on Windows.

Just a small sample:

1. Today I talked to a scientist who spent two weeks stuck, until she found my article on the subject (https://codewithoutrules.com/2018/09/04/python-multiprocessing/). Basically multiprocessing locked up, doing nothing forever. Switching to "spawn" fixed it.
2. Default multiprocessing context is broken and should never be used dask/dask#3759 (comment) is someone who had issues fixed by "spawn".
3. matmul operator @ can freeze / hang when used with default python multiprocessing using fork context instead of spawn numpy/numpy#15973 is a NumPy issue which apparently impacted scikit-learn.

I suggest changing the default on POSIX to match Windows.

Looks like as of 3.8 this only impacts Linux/non-macOS-POSIX, so I'll amend the above to say this will also make it consistent with macOS.

Just got an email from someone for whom switching to "spawn" fixed a problem. Earlier this week someone tweeted about this fixing things. This keeps hitting people in the real world.

Another person with the same issue: https://twitter.com/volcan01010/status/1324764531139248128

I just ran into and fixed (thanks to itamarst's blog post) a problem likely related to this.

Multiprocessing workers performing work and sending a logging message back with success/fail info. I had a few intermittent deadlocks that became a recurring problem when I sped up the process that skipped tasks which had previously completed (I think this shortened the time between forking and attempting to send messages causing the third process to deadlock). After changing that it deadlocked *every time*.

Switching to "spawn" at the top of the main function has fixed it.

The problem with changing the default is that this will break any application that depends on passing non-picklable data to the child process (in addition to the potentially unexpected performance impact).

The docs already contain a significant elaboration on the matter, but feel free to submit a PR that would make the various caveats more explicit:
https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods

This change was made on macOS at some point, so why not Linux? "spawn" is already the default on macOS and Windows.

The macOS change was required before "fork" simply ceased to work.
Windows has always used "spawn", because no other method can be implemented on Windows.

Given people's general experience, I would not say that "fork" works on Linux either. More like "99% of the time it works, 1% it randomly breaks in mysterious way".

Agreed, but again, changing will break some applications.

We could switch to forkserver, but we should have a transition period where a FutureWarning will be displayed if people didn't explicitly set a start method.

After updating PyPy3 to use Python 3.9's stdlib, we hit very bad hangs because of this — literally compiling a single file with "parallel" compileall could hang. In the end, we had to revert the change in how Python 3.9 starts workers because otherwise multiprocessing would be impossible to use:

https://foss.heptapod.net/pypy/pypy/-/commit/c594b6c48a48386e8ac1f3f52d4b82f9c3e34784

This is a very bad default and what's even worse is that it often causes deadlocks that are hard to reproduce or debug. Furthermore, since "fork" is the default, people are unintentionally relying on its support for passing non-pickleable projects and are creating non-portable code. The code often becomes complex and hard to change before they discover the problem.

Before we managed to figure out how to workaround the deadlocks in PyPy3, we were experimenting with switching the default to "spawn". Unfortunately, we've hit multiple projects that didn't work with this method, precisely because of pickling problems. Furthermore, they were surprised to learn that their code wouldn't work on macOS (in the end, many people perceive Python as a language for writing portable software).

Finally, back in 2018 I've made one of my projects do parallel work using multiprocessing. It gave its users great speedup but for some it caused deadlocks that I couldn't reproduce nor debug. In the end, I had to revert it. Now that I've learned about this problem, I'm wondering if this wasn't precisely because of "fork" method.

Another example: Nelson Elhage reports that "as of recently(?) pytorch silently deadlocks (even without GPUs involved at all) using method=fork so that's been fun to debug".

Examples he provided:

• Tensor operation hangs when used with multiprocessing pytorch/pytorch#82843
• Dataloader hangs. Potential deadlock with set_num_threads in worker processes? pytorch/pytorch#75147
• Deadlock with multiprocessing (using fork) and OpenMP / PyTorch should warn after OMP and fork that multithreading may be broken pytorch/pytorch#17199

After updating a couple of libraries in a project we are working on, the code would hang without much explanation. After much debugging, I think one of the reasons for our issues is the forking default (this issue). Our business logic does not use multiprocessing, but the underlying execution engine does (in our case Luigi). Turns out that gRPC client (which was buried deep into one of our dependencies) can hang in some cases when forked grpc/grpc#18075. This was the case for us, and was very tricky to debug.

general plan:

• A DeprecationWarning in 3.12 and 3.13 when the default not-explicitly-specified start method of fork is used on platforms where that is the default.
• 3.14: flip the default for all platforms to spawn.

per https://discuss.python.org/t/switching-default-multiprocessing-context-to-spawn-on-posix-as-well/21868