diff --git a/deep_speech_2/cloud/pcloud_submit.sh b/deep_speech_2/cloud/pcloud_submit.sh
index 2fb80d6677..3a64f32e24 100644
--- a/deep_speech_2/cloud/pcloud_submit.sh
+++ b/deep_speech_2/cloud/pcloud_submit.sh
@@ -7,7 +7,7 @@ MEAN_STD_FILE="../mean_std.npz"
 CLOUD_DATA_DIR="/pfs/dlnel/home/sunxinghai@baidu.com/deepspeech2/data"
 CLOUD_MODEL_DIR="/pfs/dlnel/home/sunxinghai@baidu.com/deepspeech2/model"
 # Configure cloud resources
-NUM_CPU=12
+NUM_CPU=8
 NUM_GPU=8
 NUM_NODE=1
 MEMORY="10Gi"
diff --git a/deep_speech_2/model.py b/deep_speech_2/model.py
index e2f2903b6e..99412e595a 100644
--- a/deep_speech_2/model.py
+++ b/deep_speech_2/model.py
@@ -46,6 +46,7 @@ def train(self,
               gradient_clipping,
               num_passes,
               output_model_dir,
+              is_local=True,
               num_iterations_print=100):
         """Train the model.
 
@@ -65,6 +66,8 @@ def train(self,
         :param num_iterations_print: Number of training iterations for printing
                                      a training loss.
         :type rnn_iteratons_print: int
+        :param is_local: Set to False if running with pserver with multi-nodes.
+        :type is_local: bool
         :param output_model_dir: Directory for saving the model (every pass).
         :type output_model_dir: basestring
         """
@@ -79,7 +82,8 @@ def train(self,
         trainer = paddle.trainer.SGD(
             cost=self._loss,
             parameters=self._parameters,
-            update_equation=optimizer)
+            update_equation=optimizer,
+            is_local=is_local)
 
         # create event handler
         def event_handler(event):
diff --git a/deep_speech_2/train.py b/deep_speech_2/train.py
index 379e364c92..262d8bf012 100644
--- a/deep_speech_2/train.py
+++ b/deep_speech_2/train.py
@@ -179,15 +179,13 @@ def train():
         gradient_clipping=400,
         num_passes=args.num_passes,
         num_iterations_print=args.num_iterations_print,
-        output_model_dir=args.output_model_dir)
+        output_model_dir=args.output_model_dir,
+        is_local=args.is_local)
 
 
 def main():
     utils.print_arguments(args)
-    paddle.init(
-        use_gpu=args.use_gpu,
-        trainer_count=args.trainer_count,
-        is_local=args.is_local)
+    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
     train()