diff --git a/examples/ensemble/plot_forest_importances_faces.py b/examples/ensemble/plot_forest_importances_faces.py
index 3ece3754a07cfa49fd6a04da821ae2bf49abc61a..3e71d67c4f9e8b4955a2282b91b04c34c7a6a345 100644
--- a/examples/ensemble/plot_forest_importances_faces.py
+++ b/examples/ensemble/plot_forest_importances_faces.py
@@ -19,7 +19,7 @@ from sklearn.datasets import fetch_olivetti_faces
 from sklearn.ensemble import ExtraTreesClassifier
 
 # Number of cores to use to perform parallel fitting of the forest model
-n_jobs = 2
+n_jobs = 1
 
 # Loading the digits dataset
 data = fetch_olivetti_faces()
diff --git a/examples/exercises/plot_cv_diabetes.py b/examples/exercises/plot_cv_diabetes.py
index 65efd6fd99878a0ebeebd52946aeeffd236afabe..e4ccee0fa074761bd72081cdeef9998e1a06200c 100644
--- a/examples/exercises/plot_cv_diabetes.py
+++ b/examples/exercises/plot_cv_diabetes.py
@@ -26,7 +26,7 @@ scores_std = list()
 
 for alpha in alphas:
     lasso.alpha = alpha
-    this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=-1)
+    this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=1)
     scores.append(np.mean(this_scores))
     scores_std.append(np.std(this_scores))
 
diff --git a/examples/exercises/plot_cv_digits.py b/examples/exercises/plot_cv_digits.py
index 1eeb997c5003b4080cf51331c0ac9134c43d3f4d..b9706d3264336faf4b476b57f3f47eb3bcf23f2d 100644
--- a/examples/exercises/plot_cv_digits.py
+++ b/examples/exercises/plot_cv_digits.py
@@ -23,7 +23,7 @@ scores = list()
 scores_std = list()
 for C in C_s:
     svc.C = C
-    this_scores = cross_validation.cross_val_score(svc, X, y, n_jobs=-1)
+    this_scores = cross_validation.cross_val_score(svc, X, y, n_jobs=1)
     scores.append(np.mean(this_scores))
     scores_std.append(np.std(this_scores))
 
diff --git a/examples/plot_digits_pipe.py b/examples/plot_digits_pipe.py
index 6ca85e22f9b842f798b01161c203a934314a2ecb..caf5c34254bfacb2330b65143e092fb5e90267b2 100644
--- a/examples/plot_digits_pipe.py
+++ b/examples/plot_digits_pipe.py
@@ -49,7 +49,6 @@ pl.ylabel('explained_variance_')
 
 ###############################################################################
 # Prediction
-scores = cross_validation.cross_val_score(pipe, X_digits, y_digits, n_jobs=-1)
 
 from sklearn.grid_search import GridSearchCV