Python 多处理似乎不使用多个内核

Question

我想使用 Python 多处理来运行网格搜索以查找预测模型。 当我查看核心使用情况时，它似乎总是只使用一个核心。知道我做错了什么吗？

import multiprocessing
from sklearn import svm
import itertools

#first read some data
#X will be my feature Numpy 2D array
#y will be my 1D Numpy array of labels

#define the grid        
C = [0.1, 1]
gamma = [0.0]
params = [C, gamma]
grid = list(itertools.product(*params))
GRID_hx = []

def worker(par, grid_list):
    #define a sklearn model
    clf = svm.SVC(C=g[0], gamma=g[1],probability=True,random_state=SEED)
    #run a cross validation function: returns error
    ll = my_cross_validation_function(X, y, model=clf, n=1, test_size=0.2)
    print(par, ll)
    grid_list.append((par, ll))


if __name__ == '__main__':
   manager = multiprocessing.Manager()
   GRID_hx = manager.list()
   jobs = []
   for g in grid:
      p = multiprocessing.Process(target=worker, args=(g,GRID_hx))
      jobs.append(p)
      p.start()
      p.join()

   print("\n-------------------")
   print("SORTED LIST")
   print("-------------------")
   L = sorted(GRID_hx, key=itemgetter(1))
   for l in L[:5]:
      print l

Answer 1

您的问题是您加入每项工作在您开始后立即:

for g in grid:
    p = multiprocessing.Process(target=worker, args=(g,GRID_hx))
    jobs.append(p)
    p.start()
    p.join()

join 阻塞，直到相应的进程完成工作。这意味着您的代码一次只启动一个进程，等待它完成，然后再启动下一个进程。

为了让所有进程并行运行，您需要首先将它们全部启动，然后将它们全部加入:

jobs = []
for g in grid:
    p = multiprocessing.Process(target=worker, args=(g,GRID_hx))
    jobs.append(p)
    p.start()

for j in jobs:
    j.join()

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