我正在努力并行运行几种机器学习算法（来自scikit-learn），并且我将Process类与进程之间共享的变量一起使用，以保存结果。

不幸的是，我的代码永无止境。因为我正在运行10种相当繁重的算法，这可能是内存问题吗？还是只是速度慢？

我试图将整个代码分为两部分（我当时认为这样做会更快），但是，它并没有改变任何东西...

请注意，train_bow和test_bow只是浮点向量。

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB,ComplementNB,bernoulliNB
from sklearn.ensemble import GradientBoostingClassifier,AdaBoostClassifier,VotingClassifier,ExtraTreesClassifier
from sklearn.svm import SVC,LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier as Knn
from sklearn.feature_extraction.text import TfidfVectorizer

#Custom class
from utilities.db_handler import *
from utilities.utils import *
from multiprocessing import Process,Manager
import json
import pickle as pkl
import os
import numpy as np
import pandas as pd

manager = Manager()
return_dict = manager.dict()

# Use a shared variable in order to get the results
proc = []
fncs1 = [random_forest_classification,SVC_classification,LinearSVC_classification,MultinomialNB_classification,LogisticRegression_classification]
fncs2 = [bernoulliNB_classification,GradientBoosting_classification,AdaBoost_classification,VotingClassifier_classification,ComplementNB_classification,ExtrExtraTrees_classification]

# Instantiating 2 set of processes with relative arguments. Each function
# writes the result on result_dict
for fn in fncs1:
    p = Process(target=fn,args=(train_bow,test_bow,label_train,label_test,return_dict))
    proc.append(p)
    p.start()
for p in proc:
    p.join()

for fn in fncs2:
    p = Process(target=fn,return_dict))
    proc.append(p)
    p.start()
for p in proc:
    p.join()

# then pick te best of the results from return_dict and save them

这段代码给了我一些属于算法的警告，但是没有显示与多处理有关的任何错误或警告。