我正在分析模拟的时间序列。基本上,它在每个时间步上都执行相同的任务。由于有非常多的时间步,而且每个时间步的分析都是独立的,所以我想创建一个可以对另一个函数进行多处理的函数。后者将有参数,并返回结果。
使用共享措辞和lib concurrent.futures,我成功地编写了以下代码:
import concurrent.futures as Cfut
def multiprocess_loop_grouped(function, param_list, group_size, Nworkers, *args):
# function : function that is running in parallel
# param_list : list of items
# group_size : size of the groups
# Nworkers : number of group/items running in the same time
# **param_fixed : passing parameters
manager = mlp.Manager()
dic = manager.dict()
executor = Cfut.ProcessPoolExecutor(Nworkers)
futures = [executor.submit(function, param, dic, *args)
for param in grouper(param_list, group_size)]
Cfut.wait(futures)
return [dic[i] for i in sorted(dic.keys())]
通常,我可以这样使用它:
def read_file(files, dictionnary):
for file in files:
i = int(file[4:9])
#print(str(i))
if 'bz2' in file:
os.system('bunzip2 ' + file)
file = file[:-4]
dictionnary[i] = np.loadtxt(file)
os.system('bzip2 ' + file)
Map = np.array(multiprocess_loop_grouped(read_file, list_alti, Group_size, N_thread))
或者像这样:
def autocorr(x):
result = np.correlate(x, x, mode='full')
return result[result.size//2:]
def find_lambda_finger(indexes, dic, Deviation):
for i in indexes :
#print(str(i))
# Beach = Deviation[i,:] - np.mean(Deviation[i,:])
dic[i] = Anls.find_first_max(autocorr(Deviation[i,:]), valmax = True)
args = [Deviation]
Temp = Rescal.multiprocess_loop_grouped(find_lambda_finger, range(Nalti), Group_size, N_thread, *args)
基本上,它是有效的但效果不太好有时会崩溃。有时它实际上启动了许多与Nworkers相等的python进程,有时当我指定
Nworkers = 15
时,一次只有2或3个进程在运行。例如,在我提出的以下主题中描述了我获得的典型错误:Calling matplotlib AFTER multiprocessing sometimes results in error : main thread not in main loop
还有什么比蟒蛇更能达到我的目的呢如何改进此功能的控件?如何控制运行python进程的数量?
最佳答案
python多处理的基本概念之一是使用队列。当您有一个输入列表可以迭代并且不需要由子进程更改时,它可以很好地工作。它还使您能够很好地控制所有进程,因为您生成了所需的数字,您可以将它们闲置或停止。
它也很容易调试。显式共享数据通常是一种更难正确设置的方法。
根据定义,队列可以容纳任何内容。因此,您可以用文件路径字符串来填充它们,用不可数来进行计算,甚至用图像来绘制。
在您的情况下,布局可能是这样的:
import multiprocessing as mp
import numpy as np
import itertools as it
def worker1(in_queue, out_queue):
#holds when nothing is available, stops when 'STOP' is seen
for a in iter(in_queue.get, 'STOP'):
#do something
out_queue.put({a: result}) #return your result linked to the input
def worker2(in_queue, out_queue):
for a in iter(in_queue.get, 'STOP'):
#do something differently
out_queue.put({a: result}) //return your result linked to the input
def multiprocess_loop_grouped(function, param_list, group_size, Nworkers, *args):
# your final result
result = {}
in_queue = mp.Queue()
out_queue = mp.Queue()
# fill your input
for a in param_list:
in_queue.put(a)
# stop command at end of input
for n in range(Nworkers):
in_queue.put('STOP')
# setup your worker process doing task as specified
process = [mp.Process(target=function,
args=(in_queue, out_queue), daemon=True) for x in range(Nworkers)]
# run processes
for p in process:
p.start()
# wait for processes to finish
for p in process:
p.join()
# collect your results from the calculations
for a in param_list:
result.update(out_queue.get())
return result
temp = multiprocess_loop_grouped(worker1, param_list, group_size, Nworkers, *args)
map = multiprocess_loop_grouped(worker2, param_list, group_size, Nworkers, *args)
当您担心队列内存不足时,可以使它更具动态性。而不是在进程运行时填充和清空队列。参见本例here。
最后一句话:并不像你要求的那样更像蟒蛇但对于新手来说更容易理解;-)