Celery: Daemonic Processes Are Not Allowed To Have Children

Answer :

billiard and multiprocessing are different libraries - billiard is the Celery project's own fork of multiprocessing. You will need to import billiard and use it instead of multiprocessing

However the better answer is probably that you should refactor your code so that you spawn more Celery tasks instead of using two different ways of distributing your work.

You can do this using Celery canvas

from celery import group  @app.task def sleepawhile(t):     print("Sleeping %i seconds..." % t)     time.sleep(t)     return t      def work(num_procs):     return group(sleepawhile.s(randint(1, 5)) for x in range(num_procs)])  def test(self):     my_group = group(work(randint(1, 5)) for x in range(5))     result = my_group.apply_async()     result.get() 

I've attempted to make a working version of your code that uses canvas primitives instead of multiprocessing. However since your example was quite artificial it's not easy to come up with something that makes sense.

Update:

Here is a translation of your real code that uses Celery canvas:

tasks.py:

@shared_task run_training_method(saveindex, embedder_id):     embedder = Embedder.objects.get(pk=embedder_id)     embedder.training_method(saveindex) 

models.py:

from tasks import run_training_method from celery import group  class Embedder(Model):      def embedder_update_task(self):         my_group = []          for saveindex in range(self.start_index, self.start_index + self.nsaves):             self.create_storage(saveindex)             # Add to list             my_group.extend([run_training_method.subtask((saveindex, self.id))                           for i in range(self.nproc)])          result = group(my_group).apply_async() 

I got a similar Error trying to call a multiprocessing method from a Celery task in django. I solved using billiard instead of multiprocessing

import billiard as multiprocessing 

Hope it helps.

If you are using a submodule/library with multiprocessing already baked in, it may make more sense to set the -P threads argument of the worker:

celery worker -P threads 

https://github.com/celery/celery/issues/4525#issuecomment-566503932