diff --git a/core/general-client/src/general_model.cpp b/core/general-client/src/general_model.cpp
index a778ee6356515dfe74c3ab61fdaae4d0634fbc19..c2db765a082bf2e18aa7fe88c614a6bc8bb457c8 100644
--- a/core/general-client/src/general_model.cpp
+++ b/core/general-client/src/general_model.cpp
@@ -199,13 +199,9 @@ int PredictorClient::numpy_predict(
               << float_shape[vec_idx].size();
       for (uint32_t j = 0; j < float_shape[vec_idx].size(); ++j) {
         tensor->add_shape(float_shape[vec_idx][j]);
-        std::cout << "float shape " << j << " : " << float_shape[vec_idx][j]
-                  << std::endl;
       }
       for (uint32_t j = 0; j < float_lod_slot_batch[vec_idx].size(); ++j) {
         tensor->add_lod(float_lod_slot_batch[vec_idx][j]);
-        std::cout << "float lod: " << vec_idx << " " << j
-                  << " value:" << float_lod_slot_batch[vec_idx][j] << std::endl;
       }
       tensor->set_elem_type(1);
       const int float_shape_size = float_shape[vec_idx].size();
@@ -264,13 +260,9 @@ int PredictorClient::numpy_predict(
 
       for (uint32_t j = 0; j < int_shape[vec_idx].size(); ++j) {
         tensor->add_shape(int_shape[vec_idx][j]);
-        std::cout << "int shape " << j << " : " << int_shape[vec_idx][j]
-                  << std::endl;
       }
       for (uint32_t j = 0; j < int_lod_slot_batch[vec_idx].size(); ++j) {
         tensor->add_lod(int_lod_slot_batch[vec_idx][j]);
-        std::cout << "int lod: " << vec_idx << " " << j
-                  << " value:" << int_lod_slot_batch[vec_idx][j] << std::endl;
       }
       tensor->set_elem_type(_type[idx]);
 
diff --git a/core/general-server/op/general_reader_op.cpp b/core/general-server/op/general_reader_op.cpp
index 34243da71f73703bc571abf24de2323b77141fca..03ceb90c075f37ff1f7ec2bf1381ba38210393e8 100644
--- a/core/general-server/op/general_reader_op.cpp
+++ b/core/general-server/op/general_reader_op.cpp
@@ -135,8 +135,6 @@ int GeneralReaderOp::inference() {
       lod_tensor.dtype = paddle::PaddleDType::INT32;
     }
     // implement lod tensor here
-    std::cout << "lod size: " << req->insts(0).tensor_array(i).lod_size()
-              << std::endl;
     if (req->insts(0).tensor_array(i).lod_size() > 0) {
       VLOG(2) << "(logid=" << log_id << ") var[" << i << "] is lod_tensor";
       lod_tensor.lod.resize(1);
@@ -224,7 +222,6 @@ int GeneralReaderOp::inference() {
       int offset = 0;
       for (int j = 0; j < batch_size; ++j) {
         int elem_num = req->insts(j).tensor_array(i).int64_data_size();
-        std::cout << "int elem num: " << elem_num << std::endl;
         for (int k = 0; k < elem_num; ++k) {
           dst_ptr[offset + k] = req->insts(j).tensor_array(i).int64_data(k);
         }
@@ -236,7 +233,6 @@ int GeneralReaderOp::inference() {
       int offset = 0;
       for (int j = 0; j < batch_size; ++j) {
         int elem_num = req->insts(j).tensor_array(i).float_data_size();
-        std::cout << "float elem num: " << elem_num << std::endl;
         for (int k = 0; k < elem_num; ++k) {
           dst_ptr[offset + k] = req->insts(j).tensor_array(i).float_data(k);
         }