diff --git a/official_examples/Using_TensorFlow_to_Create_a_MNIST_Dataset_Recognition_Application/codes/train_mnist_tf.py b/official_examples/Using_TensorFlow_to_Create_a_MNIST_Dataset_Recognition_Application/codes/train_mnist_tf.py
index 68db8fea13ce2e9ba973da56ac2567d65800a6a6..bf2415de1db9a0aebdb820f657ad1ba5e33218ad 100644
--- a/official_examples/Using_TensorFlow_to_Create_a_MNIST_Dataset_Recognition_Application/codes/train_mnist_tf.py
+++ b/official_examples/Using_TensorFlow_to_Create_a_MNIST_Dataset_Recognition_Application/codes/train_mnist_tf.py
@@ -7,6 +7,8 @@ import os
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
+import moxing as mox
+
 tf.flags.DEFINE_integer('max_steps', 1000, 'number of training iterations.')
 tf.flags.DEFINE_string('data_url', '/home/jnn/nfs/mnist', 'dataset directory.')
 tf.flags.DEFINE_string('train_url', '/home/jnn/temp/delete', 'saved model directory.')
@@ -15,64 +17,68 @@ FLAGS = tf.flags.FLAGS
 
 
 def main(*args):
-  # Train model
-  print('Training model...')
-  mnist = input_data.read_data_sets(FLAGS.data_url, one_hot=True)
-  sess = tf.InteractiveSession()
-  serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
-  feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
-  tf_example = tf.parse_example(serialized_tf_example, feature_configs)
-  x = tf.identity(tf_example['x'], name='x')
-  y_ = tf.placeholder('float', shape=[None, 10])
-  w = tf.Variable(tf.zeros([784, 10]))
-  b = tf.Variable(tf.zeros([10]))
-  sess.run(tf.global_variables_initializer())
-  y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
-  cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
-
-  tf.summary.scalar('cross_entropy', cross_entropy)
-
-  train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
-
-  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
-  accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
-  tf.summary.scalar('accuracy', accuracy)
-  merged = tf.summary.merge_all()
-  test_writer = tf.summary.FileWriter(FLAGS.train_url, flush_secs=1)
-
-  for step in range(FLAGS.max_steps):
-    batch = mnist.train.next_batch(50)
-    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
-    if step % 10 == 0:
-      summary, acc = sess.run([merged, accuracy], feed_dict={x: mnist.test.images, y_: mnist.test.labels})
-      test_writer.add_summary(summary, step)
-      print('training accuracy is:', acc)
-  print('Done training!')
-
-  builder = tf.saved_model.builder.SavedModelBuilder(os.path.join(FLAGS.train_url, 'model'))
-
-  tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
-  tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
-
-  prediction_signature = (
-      tf.saved_model.signature_def_utils.build_signature_def(
-          inputs={'images': tensor_info_x},
-          outputs={'scores': tensor_info_y},
-          method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
-
-  builder.add_meta_graph_and_variables(
-      sess, [tf.saved_model.tag_constants.SERVING],
-      signature_def_map={
-          'predict_images':
-              prediction_signature,
-      },
-      main_op=tf.tables_initializer(),
-      strip_default_attrs=True)
-
-  builder.save()
-
-  print('Done exporting!')
+    mox.file.copy_parallel(FLAGS.data_url, '/cache/data_url')
+
+    # Train model
+    print('Training model...')
+    mnist = input_data.read_data_sets('/cache/data_url', one_hot=True)
+    sess = tf.InteractiveSession()
+    serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
+    feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
+    tf_example = tf.parse_example(serialized_tf_example, feature_configs)
+    x = tf.identity(tf_example['x'], name='x')
+    y_ = tf.placeholder('float', shape=[None, 10])
+    w = tf.Variable(tf.zeros([784, 10]))
+    b = tf.Variable(tf.zeros([10]))
+    sess.run(tf.global_variables_initializer())
+    y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
+    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
+
+    tf.summary.scalar('cross_entropy', cross_entropy)
+
+    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
+
+    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
+    tf.summary.scalar('accuracy', accuracy)
+    merged = tf.summary.merge_all()
+    test_writer = tf.summary.FileWriter('/cache/train_url', flush_secs=1)
+
+    for step in range(FLAGS.max_steps):
+        batch = mnist.train.next_batch(50)
+        train_step.run(feed_dict={x: batch[0], y_: batch[1]})
+        if step % 10 == 0:
+            summary, acc = sess.run([merged, accuracy], feed_dict={x: mnist.test.images, y_: mnist.test.labels})
+            test_writer.add_summary(summary, step)
+            print('training accuracy is:', acc)
+    print('Done training!')
+
+    builder = tf.saved_model.builder.SavedModelBuilder(os.path.join('/cache/train_url', 'model'))
+
+    tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
+    tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
+
+    prediction_signature = (
+        tf.saved_model.signature_def_utils.build_signature_def(
+            inputs={'images': tensor_info_x},
+            outputs={'scores': tensor_info_y},
+            method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
+
+    builder.add_meta_graph_and_variables(
+        sess, [tf.saved_model.tag_constants.SERVING],
+        signature_def_map={
+            'predict_images':
+                prediction_signature,
+        },
+        main_op=tf.tables_initializer(),
+        strip_default_attrs=True)
+
+    builder.save()
+
+    print('Done exporting!')
+
+    mox.file.copy_parallel('/cache/train_url', FLAGS.train_url)
 
 
 if __name__ == '__main__':
-  tf.app.run(main=main)
\ No newline at end of file
+    tf.app.run(main=main)
\ No newline at end of file