change trainer_id

go/pserver/cclient/test/mnist_test.py

@@ -56,7 +56,7 @@ def convolutional_neural_network(img):
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1, trainer_id=1)
+    paddle.init(use_gpu=False, trainer_count=1)
 
     # define network topology
     images = paddle.layer.data(
@@ -92,9 +92,6 @@ def main():
                 print "Pass %d, Batch %d, Cost %f, %s" % (
                     event.pass_id, event.batch_id, event.cost, event.metrics)
 
-                with gzip.open('params.tar.gz', 'w') as f:
-                    parameters.to_tar(f)
-
         elif isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=paddle.batch(
                 paddle.dataset.mnist.test(), batch_size=128))

go/pserver/cclient/test/test_train.py

@@ -4,7 +4,7 @@ import paddle.v2.dataset.uci_housing as uci_housing
 
 def main():
     # init
-    paddle.init(use_gpu=False, trainer_count=1, trainer_id=1)
+    paddle.init(use_gpu=False, trainer_count=1)
 
     # network config
     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))

paddle/trainer/NewRemoteParameterUpdater.cpp

@@ -39,8 +39,8 @@ void NewRemoteParameterUpdater::init(
   }
 
   // create parameter server client.
-  parameterClient_ =
-      paddle_new_pserver_client((char *)pserverSpec_.c_str(), FLAGS_trainer_id);
+  parameterClient_ = paddle_new_pserver_client((char *)pserverSpec_.c_str(),
+                                               FLAGS_trainer_id == 0);
 
   // init names_ for get parameter through paddle_cclient
   names_ = (char **)malloc(parameterSize() * sizeof(char *));