diff --git a/auto_ml/__init__.py b/auto_ml/__init__.py
index f22b51a..d4b6cf0 100644
--- a/auto_ml/__init__.py
+++ b/auto_ml/__init__.py
@@ -1,8 +1,3 @@
-"""
-For examples, please reference https://github.com/ClimbsRocks/auto_ml
-
-"""
-
 from auto_ml.predictor import Predictor
 from auto_ml._version import __version__
 from auto_ml.utils_models import load_ml_model
diff --git a/auto_ml/predictor.py b/auto_ml/predictor.py
index bf6924f..b2d8ed6 100644
--- a/auto_ml/predictor.py
+++ b/auto_ml/predictor.py
@@ -1429,18 +1429,20 @@ def train_one_categorical_model(category, relevant_X, relevant_y):
             }
             return result
 
-        pool = pathos.multiprocessing.ProcessPool()
-
-        # Since we may have already closed the pool, try to restart it
-        try:
-            pool.restart()
-        except AssertionError as e:
-            pass
 
         if os.environ.get('is_test_suite', False) == 'True':
             # If this is the test_suite, do not run things in parallel
             results = list(map(lambda x: train_one_categorical_model(x[0], x[1], x[2]), categories_and_data))
         else:
+
+            pool = pathos.multiprocessing.ProcessPool()
+
+            # Since we may have already closed the pool, try to restart it
+            try:
+                pool.restart()
+            except AssertionError as e:
+                pass
+
             try:
                 results = list(pool.map(lambda x: train_one_categorical_model(x[0], x[1], x[2]), categories_and_data))
             except RuntimeError:
@@ -1450,12 +1452,12 @@ def train_one_categorical_model(category, relevant_X, relevant_y):
                 results = list(pool.map(lambda x: train_one_categorical_model(x[0], x[1], x[2]), categories_and_data))
                 sys.setrecursionlimit(original_recursion_limit)
 
-        # Once we have gotten all we need from the pool, close it so it's not taking up unnecessary memory
-        pool.close()
-        try:
-            pool.join()
-        except AssertionError:
-            pass
+            # Once we have gotten all we need from the pool, close it so it's not taking up unnecessary memory
+            pool.close()
+            try:
+                pool.join()
+            except AssertionError:
+                pass
 
         for result in results:
             if result['trained_category_model'] is not None:
diff --git a/auto_ml/utils_ensembling.py b/auto_ml/utils_ensembling.py
index 35ce359..94a894c 100644
--- a/auto_ml/utils_ensembling.py
+++ b/auto_ml/utils_ensembling.py
@@ -42,14 +42,6 @@ def get_predictions_for_one_estimator(estimator, X):
             predictions_from_all_estimators = map(lambda predictor: get_predictions_for_one_estimator(predictor, X), self.ensemble_predictors)
 
         else:
-            # Open a new multiprocessing pool
-            pool = pathos.multiprocessing.ProcessPool()
-
-            # Since we may have already closed the pool, try to restart it
-            try:
-                pool.restart()
-            except AssertionError as e:
-                pass
 
             # Pathos doesn't like datasets beyond a certain size. So fall back on single, non-parallel predictions instead.
             # try:
@@ -57,19 +49,22 @@ def get_predictions_for_one_estimator(estimator, X):
                 predictions_from_all_estimators = map(lambda predictor: get_predictions_for_one_estimator(predictor, X), self.ensemble_predictors)
 
             else:
+                # Open a new multiprocessing pool
+                pool = pathos.multiprocessing.ProcessPool()
+
+                # Since we may have already closed the pool, try to restart it
+                try:
+                    pool.restart()
+                except AssertionError as e:
+                    pass
                 predictions_from_all_estimators = pool.map(lambda predictor: get_predictions_for_one_estimator(predictor, X), self.ensemble_predictors)
 
-            # except:
-            #     predictions_from_all_estimators = map(lambda predictor: get_predictions_for_one_estimator(predictor, X), self.ensemble_predictors)
-            #     predictions_from_all_estimators = list(predictions_from_all_estimators)
-
-
-            # Once we have gotten all we need from the pool, close it so it's not taking up unnecessary memory
-            pool.close()
-            try:
-                pool.join()
-            except AssertionError:
-                pass
+                # Once we have gotten all we need from the pool, close it so it's not taking up unnecessary memory
+                pool.close()
+                try:
+                    pool.join()
+                except AssertionError:
+                    pass
 
         predictions_from_all_estimators = list(predictions_from_all_estimators)