When using the multiprocessing.Pool in Python with a function that returns a custom object, you need to ensure that the custom object can be pickled, which is necessary for transferring data between processes. Here is an example of how to do it:
First, create the custom object and define the necessary functions for pickling and unpickling:
import pickle
class CustomObject:
def __init__(self, data):
self.data = data
def __getstate__(self):
return {"data": self.data}
def __setstate__(self, state):
self.data = state["data"]
def custom_object_pickle(obj):
return pickle.dumps(obj)
def custom_object_unpickle(data):
return CustomObject.__setstate__(CustomObject(), pickle.loads(data))
Next, define the function that will be executed in parallel using the multiprocessing.Pool:
def parallel_function(arg):
# do some calculations
custom_obj = CustomObject(data)
return custom_obj
Then, create the multiprocessing.Pool object and pass the functions for pickling and unpickling the custom object as arguments to the initializer:
if __name__ == "__main__":
pool = multiprocessing.Pool(
processes=multiprocessing.cpu_count(),
initializer=multiprocessing.get_logger().info,
initargs=("test",),
pickle_protocol=pickle.HIGHEST_PROTOCOL,
pickle_custom_objects=[CustomObject],
)
results = pool.map(parallel_function, args_list)
pool.close()
pool.join()
Note that the pickle_protocol
argument specifies the highest protocol version to use for pickling, and the pickle_custom_objects
argument specifies a list of custom objects that need to be pickled and unpickled.
By following these steps, you should be able to use the multiprocessing.Pool in Python to parallelize a function that returns a custom object.
Asked: 2023-06-26 07:02:38 +0000
Seen: 8 times
Last updated: Jun 26 '23