python / python/cpython

`multiprocessing.managers.BaseProxy` can not be unpickled when using a custom `authkey`

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stdlib topic-multiprocessing type-bug
Lenguaje dominante
Python
Estrellas
77.2k
Forks
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Descripción

Bug report

Bug description:

The problem, simplified

When using a Manager with a custom authkey, a pickled proxy object can not be unpickled:

from multiprocessing.managers import BaseManager, BaseProxy


class MyManager(BaseManager):
    pass


class MyObject:
    def __init__(self):
        self.value = 42

    def get_value(self):
        return self.value

    def set_value(self, new_value):
        self.value = new_value


class MyProxy(BaseProxy):
    _exposed_ = ("get_value", "set_value")

    def get_value(self):
        return self._callmethod("get_value")

    def set_value(self, new_value):
        return self._callmethod("set_value", (new_value,))


_my_object = MyObject()


def _get_my_object():
    return _my_object


def test_proxy_unpickle():
    MyManager.register("get_my_object", callable=_get_my_object, proxytype=MyProxy)

    manager = MyManager(
        address=("", 0),
        authkey=b"customkey", # <--- CUSTOM AUTHKEY
    )
    manager.start()

    proxy_object = manager.get_my_object()
    assert isinstance(proxy_object, MyProxy)
    assert proxy_object.get_value() == 42

    # We simulate pickling...
    rebuild_proxy, args = proxy_object.__reduce__()

    # ... and unpickling in a child process
    # Fails with: multiprocessing.context.AuthenticationError: digest sent was rejected
    rebuild_proxy(*args)

(It works when no custom authkey is passed to MyManager.)

This is because When a proxy is pickled the authkey is deliberately dropped. Accordingly, BaseProxy.__init__ uses process.current_process().authkey and fails to connect to the manager.

Actually, there is one condition where an authkey is passed:

class BaseProxy:
    def __reduce__(self):
        kwds = {}
        if get_spawning_popen() is not None:
            kwds['authkey'] = self._authkey

(So only while spawning a new process, but not at any other time later.)

This makes me wonder why the authkey parameter of BaseManager was introduced at all if it does not work in all cases but the default process.current_process().authkey does...

The documentation says:

An important feature of proxy objects is that they are picklable so they can be passed between processes.

As shown above, this is not unconditionally true. I have the feeling that the implementation can not be changed, so I would suggest to make the documentation more precise.

The context

I'm using dask_jobqueue.SLURMCluster to start worker processes on a distributed system. I want to use a multiprocessing Manager to enable communication between the main process and it's workers for progress reporting. (I already have it running for "regular" multiprocessing and I'm hesitant to switch to another means of IPC just because of this problem...)

I see two ways how I could make this work:

  • Do multiprocessing.process.current_process().authkey = b"customkey" during the initialization of the Dask worker.
  • Override BaseProxy.__reduce__ to unconditionally set kwds['authkey'] = bytes(self._authkey).

In both cases, I potentially expose authkey to the outside world... Any advice?

CPython versions tested on:

3.13

Operating systems tested on:

Linux

Linked PRs
  • gh-144311

Guía de contribución

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  1. Lee el issue completo y luego la guía de contribución del proyecto.
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  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Línea de trabajo

Comienza con multiprocessing.managers.BaseProxy.reduce y BaseProxy.init, usando la reproducción del issue para rastrear cómo se gestiona un authkey personalizado durante el unpickling del proxy. Revisa la documentación de los objetos proxy y el PR enlazado gh-144311; se considera terminado cuando el comportamiento documentado y la reproducción con el authkey personalizado coinciden.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
python
Área
distributed-systems
Tipo de issue
Error
Dificultad
4/5
Tiempo estimado
3-5 días
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
35/100

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