Multiprocessing : use tqdm to display a progress bar
To make my code more “pythonic” and faster, I use “multiprocessing” and a map function to send it a) the function and b) the range of iterations.
multiprocessing
How to share numpy random state of a parent process with child processes?
I set numpy random seed at the beginning of my program. During the program execution I run a function multiple times using multiprocessing.Process. The function uses numpy random functions to draw random numbers. The problem is that Process gets a copy of the current environment. Therefore, each process is running independently and they all start with the same random seed as the parent environment.
Multiprocessing – Pipe vs Queue
What are the fundamental differences between queues and pipes in Python’s multiprocessing package?
Python command line input in a process
I’ve got a system that needs to receive input from a few different processes. The simplest is just a command line where the user enters data manually. This data will be added to a multiprocessing.Queue and handled later by the main process, but I’m not even getting that far; calling raw_input inside a process doesn’t seem to work. I pulled out the meat of the code and here’s an example:
Appending to the same list from different processes using multiprocessing
I need to append objects to one list L from different processes using multiprocessing , but it returns empty list.
How can I let many processes append to list L using multiprocessing?
Can I use a multiprocessing Queue in a function called by Pool.imap?
I’m using python 2.7, and trying to run some CPU heavy tasks in their own processes. I would like to be able to send messages back to the parent process to keep it informed of the current status of the process. The multiprocessing Queue seems perfect for this but I can’t figure out how to get it work.
Using the multiprocessing module for cluster computing
I’m interested in running a Python program using a computer cluster. I have in the past been using Python MPI interfaces, but due to difficulties in compiling/installing these, I would prefer solutions which use built-in modules, such as Python’s multiprocessing module.
multiprocessing.Pool – PicklingError: Can’t pickle : attribute lookup thread.lock failed
multiprocessing.Pool is driving me crazy…
I want to upgrade many packages, and for every one of them I have to check whether there is a greater version or not. This is done by the check_one function.
The main code is in the Updater.update method: there I create the Pool object and call the map() method.
Concurrent.futures vs Multiprocessing in Python 3
Python 3.2 introduced Concurrent Futures, which appear to be some advanced combination of the older threading and multiprocessing modules.
Parallelizing a Numpy vector operation
Let’s use, for example, numpy.sin()