Speeding up grid search in sklearn

Question

I am performing a grid search to identify the best SVM parameters. I am using ipython and sklearn. The code is slow and runs on only one core. How can this be seeded up and utilize multiple cores? Thanks

random_state = np.random.RandomState(10)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=.2,random_state=random_state)

model_to_set = OneVsRestClassifier(svm.SVC(kernel="linear"))

parameters = {
    "estimator__C": [1, 2, 4, 8, 16, 32],
    "estimator__kernel": ["linear", "rbf"],
    "estimator__gamma":[1, 0.1, 1e-2, 1e-3, 1e-4],
}

model_tuning = GridSearchCV(model_to_set, param_grid=parameters)
model_tuning.fit(X_train, y_train)

print model_tuning.best_score_
print model_tuning.best_params_
print "Time passed: ", "{0:.1f}".format(time.time()-t), "sec"

Answer 1

There is an n_jobs parameter in GridSearchCV

n_jobs : int, default=1

Number of jobs to run in parallel. Changed in version 0.17: Upgraded to joblib 0.9.3.