python - Train two models concurrently -

all need is, train 2 regression models (using scikit-learn) on same data @ same time, using different cores. i've tried figured out myself using process without success.

gb1 = gradientboostingregressor(n_estimators=10) gb2 = gradientboostingregressor(n_estimators=100)  def train_model(model, data, target):     model.fit(data, target)  live_data # pandas dataframe object target # numpy array object p1 = process(target=train_model, args=(gb1, live_data, target)) # same data p2 = process(target=train_model, args=(gb2, live_data, target)) # same data p1.start() p2.start() 

if run code above following error while trying start p1 process.

traceback (most recent call last):   file "<pyshell#28>", line 1, in <module>     p1.start()   file "c:\python27\lib\multiprocessing\process.py", line 130, in start     self._popen = popen(self)   file "c:\python27\lib\multiprocessing\forking.py", line 274, in __init__     to_child.close() ioerror: [errno 22] invalid argument 

i'm running script (in idle) on windows. suggestions on how should proceed?

ok.. after hours spent in try working, i'll post solution. first thing. if you're on windows , you're using interactive intepreter need encapsualte code under 'main' condition, @ exeption of function definition , imports. because when new process spawned go on loop.

my solution below:

from sklearn.ensemble import gradientboostingregressor multiprocessing import pool itertools import repeat  def train_model(params):     model, data, target = params     # since pool args accept once argument, need pass 1     # , unroll above     model.fit(data, target)     return model  if __name__ == '__main__':     gb1 = gradientboostingregressor(n_estimators=10)     gb2 = gradientboostingregressor(n_estimators=100)      live_data # pandas dataframe object     target    # numpy array object      po = pool(2) # 2 numbers of process want spawn     gb, gb2 = po.map_async(train_model,                   zip([gb1,gb2], repeat(data), repeat(target))                  # zip in 1 iterable object               ).get()     # start processes , execute them     po.terminate()     # kill spawned processes