How to use multiprocessing pool.map with multiple arguments
In the Python multiprocessing library, is there a variant of pool.map which supports multiple arguments?
python-multiprocessing
Python multiprocessing PicklingError: Can’t pickle
I am sorry that I can’t reproduce the error with a simpler example, and my code is too complicated to post. If I run the program in IPython shell instead of the regular Python, things work out well.
How can I recover the return value of a function passed to multiprocessing.Process?
In the example code below, I’d like to recover the return value of the function worker. How can I go about doing this? Where is this value stored?
Sharing a result queue among several processes
The documentation for the multiprocessing module shows how to pass a queue to a process started with multiprocessing.Process. But how can I share a queue with asynchronous worker processes started with apply_async? I don’t need dynamic joining or anything else, just a way for the workers to (repeatedly) report their results back to base.
Shared variable in python’s multiprocessing
First question is what is the difference between Value and Manager().Value?
multiprocessing.Pool: What’s the difference between map_async and imap?
I’m trying to learn how to use Python’s multiprocessing package, but I don’t understand the difference between map_async and imap.
I noticed that both map_async and imap are executed asynchronously. So when should I use one over the other? And how should I retrieve the result returned by map_async?
Keras + Tensorflow and Multiprocessing in Python
I’m using Keras with Tensorflow as backend.
Is it possible to run function in a subprocess without threading or writing a separate file/script.
import subprocess def my_function(x): return x + 100 output = subprocess.Popen(my_function, 1) #I would like to pass the function object and its arguments print output #desired output: 101 I have only found documentation on opening subprocesses using separate scripts. Does anyone know how to pass function objects or even an easy way to pass function … Read more
Preserve custom attributes when pickling subclass of numpy array
I’ve created a subclass of numpy ndarray following the numpy documentation. In particular, I have added a custom attribute by modifying the code provided.