diff --git a/splearn/pipeline.py b/splearn/pipeline.py
index 0d1df9f..1f55157 100644
--- a/splearn/pipeline.py
+++ b/splearn/pipeline.py
@@ -5,9 +5,8 @@
 import numpy as np
 import scipy.sparse as sp
 from sklearn.externals import six
-from sklearn.externals.joblib import Parallel, delayed
 from sklearn.pipeline import FeatureUnion, Pipeline, _name_estimators
-from splearn.rdd import ArrayRDD, DictRDD
+from splearn.rdd import DictRDD
 
 
 class SparkPipeline(Pipeline):
@@ -218,6 +217,7 @@ class SparkFeatureUnion(FeatureUnion):
         List of transformer objects to be applied to the data. The first
         half of each tuple is the name of the transformer.
     n_jobs: int, optional
+        Ignored on spark, useful after conversion to scikit object
         Number of jobs to run in parallel (default 1).
     transformer_weights: dict, optional
         Multiplicative weights for features per transformer.
@@ -239,9 +239,11 @@ def fit(self, Z, **fit_params):
             step, param = pname.split('__', 1)
             fit_params_steps[step][param] = pval
 
-        transformers = Parallel(n_jobs=self.n_jobs, backend="threading")(
-            delayed(_fit_one_transformer)(trans, Z, **fit_params_steps[name])
-            for name, trans in self.transformer_list)
+        transformers = [_fit_one_transformer(trans,
+                                             Z,
+                                             **fit_params_steps[name])
+                        for name, trans in self.transformer_list]
+
         self._update_transformer_list(transformers)
         return self