Python multiprocessing doesn't seem to use more than one core

Question

I want to use Python multiprocessing to run grid search for a predictive model. When I look at core usage, it always seem to be using only one core. Any idea what I'm doing wrong?

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

Your problem is that you join each job immediately after you started it:

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

join blocks until the respective process has finished working. This means that your code starts only one process at once, waits until it is finished and then starts the next one.

In order for all processes to run in parallel, you need to first start them all and then join them all:

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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