multiprocessing

问题 Here's a little bench-marking code to illustrate my question: import numpy as np import multiprocessing as mp # allocate memory %time temp = mp.RawArray(np.ctypeslib.ctypes.c_uint16, int(1e8)) Wall time: 46.8 ms # assign memory, very slow %time temp[:] = np.arange(1e8, dtype = np.uint16) Wall time: 10.3 s # equivalent numpy assignment, 100X faster %time a = np.arange(1e8, dtype = np.uint16) Wall time: 111 ms Basically I want a numpy array to be shared between multiple processes because it's

问题 In the subprocess Python 2 module, Popen can be given an env . Seems that the equivalent way to do it with Process in multiprocessing module is to pass the env dictionnary in args or kwargs , and then use os.environ['FOO'] = value in the target . Is it the right way? Is it safe? I mean, no risk that the environment in the parent process or other child processes can be modified? Here is an example (that works). import multiprocessing import time import os def target(someid): os.environ['FOO']

问题 Recently I'm studying parallel programming tools in Python. And here are two major differences between os.pipe and multiprocessing.Pipe.(despite the occasion they are used) os.pipe is unidirectional , multiprocessing.Pipe is bidirectional ; When putting things into pipe/receive things from pipe, os.pipe uses encode/decode , while multiprocessing.Pipe uses pickle/unpickle I want to know if my understanding is correct, and is there other difference? Thank you. 回答1: I believe everything you've

问题 I'm using concurrent.futures to implement multiprocessing. I am getting a queue.Full error, which is odd because I am only assigning 10 jobs. A_list = [np.random.rand(2000, 2000) for i in range(10)] with ProcessPoolExecutor() as pool: pool.map(np.linalg.svd, A_list) error: Exception in thread Thread-9: Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/threading.py", line 921, in _bootstrap_inner self.run() File "/Library/Frameworks/Python

问题 I use pygame for running experiments in cognitive science, and often I have heavy I/O demands so I like to fork off these tasks to separate processes (when using a multi-core machine) to improve performance of my code. However, I encountered a scenario where some code works on my colleague's linux machine (Ubuntu LTS), but not on my mac. Below is code representing a minimal reproducible example. My mac is a 2011 Macbook Air running 10.7.2 and using the default python 2.7.1. I tried both

问题 I use pygame for running experiments in cognitive science, and often I have heavy I/O demands so I like to fork off these tasks to separate processes (when using a multi-core machine) to improve performance of my code. However, I encountered a scenario where some code works on my colleague's linux machine (Ubuntu LTS), but not on my mac. Below is code representing a minimal reproducible example. My mac is a 2011 Macbook Air running 10.7.2 and using the default python 2.7.1. I tried both

问题 I have been tackling this problem for a week now and it's been getting pretty frustrating because every time I implement a simpler but similar scale example of what I need to do, it turns out multiprocessing will fudge it up. The way it handles shared memory baffles me because it is so limited, it can become useless quite rapidly. So the basic description of my problem is that I need to create a process that gets passed in some parameters to open an image and create about 20K patches of size

问题 I am studing the multiprocessing module of Python. I have two cases: Ex. 1 def Foo(nbr_iter): for step in xrange(int(nbr_iter)) : print random.uniform(0,1) ... from multiprocessing import Pool if __name__ == "__main__": ... pool = Pool(processes=nmr_parallel_block) pool.map(Foo, nbr_trial_per_process) Ex 2. (using numpy) def Foo_np(nbr_iter): np.random.seed() print np.random.uniform(0,1,nbr_iter) In both cases the random number generators are seeded in their forked processes. Why do I have to

问题 I'm using Python's Watchdog to monitor a given directory for new files being created. When a file is created, some code runs that spawns a subprocess shell command to run different code to process this file. This should run for every new file that is created. I've tested this out when one file is created, and things work great, but am having trouble getting it working when multiple files are created, either at the same time, or one after another. My current problem is this... the processing

问题 I'm using Python's Watchdog to monitor a given directory for new files being created. When a file is created, some code runs that spawns a subprocess shell command to run different code to process this file. This should run for every new file that is created. I've tested this out when one file is created, and things work great, but am having trouble getting it working when multiple files are created, either at the same time, or one after another. My current problem is this... the processing