update job

benchmark/cluster/v2/trainer.yaml

@@ -21,6 +21,8 @@ spec:
         env:
         - name: PADDLE_JOB_NAME
           value: vgg16job
+        - name: OMP_NUM_THREADS
+          value: "1"
         - name: TRAINERS
           value: "20"
         - name: PSERVERS
@@ -36,7 +38,7 @@ spec:
         - name: PADDLE_INIT_NICS
           value: "xgbe0"
         - name: PADDLE_INIT_TRAINER_COUNT
-          value: "1"
+          value: "2"
         - name: PADDLE_INIT_PORTS_NUM
           value: "1"
         - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
@@ -44,7 +46,7 @@ spec:
         - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
           value: "20"
         - name: PADDLE_INIT_NUM_PASSES
-          value: "1"
+          value: "2"
         - name: PADDLE_INIT_USE_GPU
           value: "0"
         - name: LD_LIBRARY_PATH

benchmark/cluster/v2/vgg16.py

@@ -74,14 +74,14 @@ def vgg19(input, class_dim):
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=False)
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(DATA_DIM))
     lbl = paddle.layer.data(
         name="label", type=paddle.data_type.integer_value(CLASS_DIM))
 
     extra_layers = None
-    learning_rate = 0.01
+    learning_rate = 1e-3
     out = vgg16(image, class_dim=CLASS_DIM)
     cost = paddle.layer.classification_cost(input=out, label=lbl)