example_multiprocess.py

def wrapper_logistic_regression_with_L2(l2_penalty,
                                         feature_matrix=feature_matrix_train,
                                         sentiment=sentiment_train, 
                                         initial_coefficients=np.zeros(194), 
                                         step_size=5e-6, max_iter=501):
                                           
    coef=logistic_regression_with_L2(feature_matrix, sentiment, initial_coefficients, step_size, l2_penalty, max_iter)
    return (l2_penalty,coef)
    
from multiprocessing import Pool

# Run 
n_cores=4
pool = Pool(processes=n_cores) 
resultados=[pool.map(wrapper_logistic_regression_with_L2, [0,4,10,1e2,1e3,1e5])]

# create dataframe with relation l2 - coefficients
for l2,coef in resultados[0]:    
    add_coefficients_to_table(coef, 'coefficients [L2={:0.0f}]'.format(l2))
    
# show result    
for l2,coef in resultados[0]:
    train_accurancy=get_classification_accuracy(feature_matrix_train, sentiment_train, coef)
    validation_accurancy=get_classification_accuracy(feature_matrix_valid, sentiment_valid, coef)
    print ("L2 penalty = {}".format(l2) )
    print ("train accuracy = {}, validation_accuracy = {}".format(train_accurancy, validation_accurancy))
    print ("--------------------------------------------------------------------------------")