multiprocessing

问题 I’ve a function: def func(something): for j in tqdm(something): ... which is called by: joblib.Parallel(n_jobs=4)((joblib.delayed)(s) for s in something_else) Now, this creates 4 overlapping tqdm progress bars. Is it possible to get 4 separate ones that update independently? 回答1: EDIT: I was sent this discussion by a friend in which a much cleaner solution is provided. I wrote a quick performance test to make sure that the lock does not cause the threads to block each other. There was no

问题 I have the following code, where I try to parallelize: import numpy as np from joblib import Parallel, delayed lst = [[0.0, 1, 2], [3, 4, 5], [6, 7, 8]] arr = np.array(lst) w, v = np.linalg.eigh(arr) def proj_func(i): return np.dot(v[:,i].reshape(-1, 1), v[:,i].reshape(1, -1)) proj = Parallel(n_jobs=-1)(delayed(proj_func)(i) for i in range(len(w))) proj returns a really large list and it's causing memory issues. Is there a way I could work around this? I had thought about returning a

问题 I have the following code, where I try to parallelize: import numpy as np from joblib import Parallel, delayed lst = [[0.0, 1, 2], [3, 4, 5], [6, 7, 8]] arr = np.array(lst) w, v = np.linalg.eigh(arr) def proj_func(i): return np.dot(v[:,i].reshape(-1, 1), v[:,i].reshape(1, -1)) proj = Parallel(n_jobs=-1)(delayed(proj_func)(i) for i in range(len(w))) proj returns a really large list and it's causing memory issues. Is there a way I could work around this? I had thought about returning a

问题 Note : I "forayed" into the land of multiprocessing 2 days ago. So my understanding is very basic. I am writing and application for uploads to amazon s3 buckets. In case the file size is larger( 100mb ), Ive implemented parallel uploads using pool from the multiprocessing module. I am using a machine with core i7 , i had a cpu_count of 8 . I was under the impression that if i do pool = Pool(process = 6) I use 6 cores and the file begins to upload in parts and the uploads for the first 6 parts

问题 I want to use multiprocessing.Pool, but multiprocessing.Pool can't abort a task after a timeout. I found solution and some modify it. from multiprocessing import util, Pool, TimeoutError from multiprocessing.dummy import Pool as ThreadPool import threading import sys from functools import partial import time def worker(y): print("worker sleep {} sec, thread: {}".format(y, threading.current_thread())) start = time.time() while True: if time.time() - start >= y: break time.sleep(0.5) # show

问题 I want to do run a process in parallel on several cores. Therefore, I use the multiprocessing library in Python. However, there is a subprocess which sometimes breaks so that the whole script does not work anymore. I'd like to test whether the subprocess breaks and if this is the case, restart it. These are the packages and data I use: from frog import Frog, FrogOptions import multiprocessing textparts = ['these', 'are', 'eight', 'words', 'matching', 'the', 'eight', 'cores'] The function in

问题 Below is my code , for which i am facing a multiprocessing issue. I see this question has been asked before and i have tried those solutions but it does not seem to work. Can someone help me out here ? from multiprocessing import Pool, Manager Class X: def _init_(): def method1(number1,var_a, var_b, var_c, var_d): return values if __name__ == 'main': for value in ["X", "Y"]: dict_values = Manager().dict() with Pool(1) as p: p.starmap(method1, [ (1, dict_values, var_a, var_b, var_c, var_d), (2

问题 My Python application creates an array shared between processes using multiprocessing.RawArray . Now to speed up computation I want to modify this array from within a C++ function. What is a safe way to pass a pointer to the underlying memory to a C++ function that accepts a void * argument? The function is defined in a pxd file as: cdef extern from 'lib/lib.hpp': void fun(void *buffer) My naive attempt so far: buffer = multiprocessing.RawArray(ctypes.c_ubyte, 10000) clib.fun(ctypes.cast(self

问题 I want to create a shared array with a dynamic size. I want to assign an array with an unknown size to it in another process. from multiprocessing import Process, Value, Array def f(a): b=[3,5,7] #resize(a,len(b)) # How to resize "a" ??? a[:]=b # Only works when "a" is initialized with the same size like the "b" arr = Array('d', 0) #Array with a size of 0 p = Process(target=f, args=(arr)) p.start() p.join() print arr[:] 回答1: The size of mp.Arrays can only be set once upon instantiation. You

问题 I want to create a shared array with a dynamic size. I want to assign an array with an unknown size to it in another process. from multiprocessing import Process, Value, Array def f(a): b=[3,5,7] #resize(a,len(b)) # How to resize "a" ??? a[:]=b # Only works when "a" is initialized with the same size like the "b" arr = Array('d', 0) #Array with a size of 0 p = Process(target=f, args=(arr)) p.start() p.join() print arr[:] 回答1: The size of mp.Arrays can only be set once upon instantiation. You