python-multiprocessing

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 years ago . How to parallelize the below code, number of elements in the attributes column is nearly 15 and so combination is taking more time. combs = set() for L in range(0,len(attributes)+1): combs.add(itertools.combinations(attributes,L)) Any way to parallelize it using multiprocessing? I tried this, but i

问题 I'm doing some multiprocessing, and I'd like to understand why the following piece of code sometimes (but never at the first iteration of the loop) generates the following error : Exception ignored in: <bound method Image.__del__ of <tkinter.PhotoImage object at 0x7f6c40d25ba8>> Traceback (most recent call last): File "/usr/lib/python3.5/tkinter/__init__.py", line 3359, in __del__ self.tk.call('image', 'delete', self.name) RuntimeError: main thread is not in main loop Tcl_AsyncDelete: async

问题 Looking to print everything in order, for a Python parallelized script. Note the c3 is printed prior to the b2 -- out of order. Any way to make the below function with a wait feature? If you rerun, sometimes the print order is correct for shorter batches. However, looking for a reproducible solution to this issue. from joblib import Parallel, delayed, parallel_backend import multiprocessing testFrame = [['a',1], ['b', 2], ['c', 3]] def testPrint(letr, numbr): print(letr + str(numbr)) return

问题 What am I missing or doing wrong in the following python file using multiprocessing? When I run it, nothing happens and I have to restart the shell! def f(x): lo=0 for i in range(x): lo+=i return(lo) from multiprocessing import Pool def f_parallel(x1,x2,x3,x4): with Pool(processes=4) as pool: resulto_parallel=pool.map(f,[x1,x2,x3,x4]) return(resulto_parallel) f_parallel(1,2,3,4) Here is screenshot of what happens when I run it. And then after waiting a while I just restart the shell. 回答1:

问题 I have the following code in python: import multiprocessing import time print "I want this to show once at the beggining" def leaveout(): print "I dont Want This to Show" def hang(input1): print "I want this to show 3 times" print "Number = %s" % input1 def main(): p = multiprocessing.Process(target=hang, args=("0")) p.start() p1 = multiprocessing.Process(target=hang, args=("1")) p1.start() p2 = multiprocessing.Process(target=hang, args=("2")) p2.start() p.join() p1.join() p2.join() if __name

问题 I have a node.js application that runs a client interface which exposes action that triggers machine-learn tasks. Since python is a better choice when implementing machine-learn related stuff, I've implemented a python application that runs on demand machine learning tasks. Now, I need to integrate both applications. It has been decided that we need to use a single (AWS) instance to integrate both applications. One way found to do such integration was using python-shell node module. There,

问题 RELATED: Python multiprocessing: Permission denied I want to use Python's multiprocessing.Pool import multiprocessing as mp pool = mp.Pool(3) for i in range(num_to_run): pool.apply_async(popen_wrapper, args=(i,), callback=log_result) I get OSError File "/usr/local/lib/python2.6/multiprocessing/__init__.py", line 178, in RLock return RLock() File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 142, in __init__ SemLock.__init__(self, RECURSIVE_MUTEX, 1, 1) File "/usr/local/lib

问题 This is related to how to parallelize many (fuzzy) string comparisons using apply in Pandas? Consider this simple (but funny) example again: import dask.dataframe as dd import dask.multiprocessing import dask.threaded from fuzzywuzzy import fuzz import pandas as pd master= pd.DataFrame({'original':['this is a nice sentence', 'this is another one', 'stackoverflow is nice']}) slave= pd.DataFrame({'name':['hello world', 'congratulations', 'this is a nice sentence ', 'this is another one',

问题 I am trying to initialise a function in a class using multiprocessing, by calling it from a function, which is inside the same same class def Streaminit(self,_track): self.twitterStream = tweepy.Stream(self.auth, Twitterapi.Listener()) self.twitterStream.filter(track=_track) def Stream(self,track=""): self.streamobj = multiprocessing.Process(target = self.Streaminit(),args=(track,)) but when I call stream it raises an error TypeError: Streaminit() takes exactly 2 arguments (1 given) What am I

问题 I have four queues that each have multiple processes/threads that are interdependent in the following way: Queue 1 is reading a file from disk and copying to RAM Queue 2 takes the file in RAM and performs an operation on it Queue 3 takes the result of Queue 2 and performs a separate operation on it Queue 4 writes the end result back to disk I would like these 4 queues to operate in parallel as much as possible with the caveat that Queue 2 has to wait for Queue 1 to place at least one process