diff --git a/paddlehub/autofinetune/autoft.py b/paddlehub/autofinetune/autoft.py
index 6ec3a5642497451d78f56a21cbf9eccde427138b..e7aba48d5be0b768587a6e69a15e72c89976e0c3 100644
--- a/paddlehub/autofinetune/autoft.py
+++ b/paddlehub/autofinetune/autoft.py
@@ -79,7 +79,8 @@ class BaseTuningStrategy(object):
         # for parallel on mpi
         self.mpi = MPIHelper()
         if self.mpi.multi_machine:
-            print("Autofinetune multimachine mode: running on {}".format(self.mpi.gather(self.mpi.name)))
+            print("Autofinetune multimachine mode: running on {}".format(
+                self.mpi.gather(self.mpi.name)))
 
     @property
     def thread(self):
diff --git a/paddlehub/autofinetune/mpi_helper.py b/paddlehub/autofinetune/mpi_helper.py
index 7d7ee1e6109e99cceb17ca001a29b515ed972857..9608363bfa888f208e20faaf7c9ac9d2278b33d3 100755
--- a/paddlehub/autofinetune/mpi_helper.py
+++ b/paddlehub/autofinetune/mpi_helper.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+
 class MPIHelper(object):
     def __init__(self):
         try:
@@ -43,7 +44,7 @@ class MPIHelper(object):
     def bcast(self, data):
         if self._multi_machine:
             # call real bcast
-            return self._comm.bcast(data, root = 0)
+            return self._comm.bcast(data, root=0)
         else:
             # do nothing
             return data
@@ -51,7 +52,7 @@ class MPIHelper(object):
     def gather(self, data):
         if self._multi_machine:
             # call real gather
-            return self._comm.gather(data, root = 0)
+            return self._comm.gather(data, root=0)
         else:
             # do nothing
             return [data]
@@ -73,7 +74,8 @@ class MPIHelper(object):
             return average_count * self._rank, average_count * (self._rank + 1)
         else:
             if self._rank < array_length % self._size:
-                return (average_count + 1) * self._rank, (average_count + 1) * (self._rank + 1)
+                return (average_count + 1) * self._rank, (average_count + 1) * (
+                    self._rank + 1)
             else:
                 start = (average_count + 1) * (array_length % self._size) \
                       + average_count * (self._rank - array_length % self._size)
@@ -83,7 +85,8 @@ class MPIHelper(object):
 if __name__ == "__main__":
 
     mpi = MPIHelper()
-    print("Hello world from process {} of {} at {}.".format(mpi.rank, mpi.size, mpi.name))
+    print("Hello world from process {} of {} at {}.".format(
+        mpi.rank, mpi.size, mpi.name))
 
     all_node_names = mpi.gather(mpi.name)
     print("all node names using gather: {}".format(all_node_names))
@@ -106,8 +109,7 @@ if __name__ == "__main__":
 
     # test for split
     for i in range(12):
-        length = i + mpi.size # length should >= mpi.size
+        length = i + mpi.size  # length should >= mpi.size
         [start, end] = mpi.split_range(length)
         split_result = mpi.gather([start, end])
         print("length {}, split_result {}".format(length, split_result))
-
diff --git a/paddlehub/commands/autofinetune.py b/paddlehub/commands/autofinetune.py
index 15035d7968616d170cb9bd116e01eeb39d1a4dd7..8efb56f53a47be7b6378ecfe174a24d3803a8025 100644
--- a/paddlehub/commands/autofinetune.py
+++ b/paddlehub/commands/autofinetune.py
@@ -199,14 +199,16 @@ class AutoFineTuneCommand(BaseCommand):
                 print("%s=%s" % (hparam_name, best_hparams[index]))
                 f.write(hparam_name + "\t:\t" + str(best_hparams[index]) + "\n")
 
-            best_hparams_dir, best_hparams_rank = solutions_modeldirs[tuple(best_hparams_origin)]
+            best_hparams_dir, best_hparams_rank = solutions_modeldirs[tuple(
+                best_hparams_origin)]
 
             print("The final best eval score is %s." %
                   autoft.get_best_eval_value())
 
             if autoft.mpi.multi_machine:
                 print("The final best model parameters are saved as " +
-                      autoft._output_dir + "/best_model on rank " + str(best_hparams_rank) + " .")
+                      autoft._output_dir + "/best_model on rank " +
+                      str(best_hparams_rank) + " .")
             else:
                 print("The final best model parameters are saved as " +
                       autoft._output_dir + "/best_model .")
@@ -226,7 +228,8 @@ class AutoFineTuneCommand(BaseCommand):
                         "\tsaved_params_dir\trank\n")
             else:
                 f.write(
-                    "The final best model parameters are saved as ./best_model .")
+                    "The final best model parameters are saved as ./best_model ."
+                )
                 f.write("\t".join(autoft.hparams_name_list) +
                         "\tsaved_params_dir\n")
 
@@ -237,7 +240,8 @@ class AutoFineTuneCommand(BaseCommand):
                 param = evaluator.convert_params(solution)
                 param = [str(p) for p in param]
                 if autoft.mpi.multi_machine:
-                    f.write("\t".join(param) + "\t" + modeldir[0] + "\t" + str(modeldir[1]) + "\n")
+                    f.write("\t".join(param) + "\t" + modeldir[0] + "\t" +
+                            str(modeldir[1]) + "\n")
                 else:
                     f.write("\t".join(param) + "\t" + modeldir[0] + "\n")