rpc local unifed

core/configure/proto/server_configure.proto

@@ -58,6 +58,8 @@ message ResourceConf {
   optional string cube_config_path = 5;
   optional string cube_config_file = 6;
   optional int32 cube_quant_bits = 7; // set 0 if no quant.
+  optional string auth_product_name = 8;
+  optional string auth_container_id = 9;
 };
 
 // DAG node depency info

core/cube/cube-api/include/meta.h

@@ -22,7 +22,8 @@
 #ifdef BCLOUD
 #include "baidu/rpc/channel.h"
 #include "baidu/rpc/parallel_channel.h"
-#include "rapidjson/document.h"
+#include "rapidjson_1.0/document.h"
+#include "rapidjson_1.0/rapidjson.h"
 #else
 #include "brpc/channel.h"
 #include "brpc/parallel_channel.h"

core/general-client/include/general_model.h

@@ -217,7 +217,7 @@ class PredictorClient {
   int create_predictor();
 
   int destroy_predictor();
-  
+
   int numpy_predict(
       const std::vector<std::vector<py::array_t<float>>>& float_feed_batch,
       const std::vector<std::string>& float_feed_name,

core/general-client/src/general_model.cpp

@@ -39,7 +39,9 @@ using configure::GeneralModelConfig;
 
 void PredictorClient::init_gflags(std::vector<std::string> argv) {
   std::call_once(gflags_init_flag, [&]() {
+#ifndef BCLOUD
     FLAGS_logtostderr = true;
+#endif
     argv.insert(argv.begin(), "dummy");
     int argc = argv.size();
     char **arr = new char *[argv.size()];
@@ -135,7 +137,6 @@ int PredictorClient::create_predictor() {
   return 0;
 }
 
-
 int PredictorClient::numpy_predict(
     const std::vector<std::vector<py::array_t<float>>> &float_feed_batch,
     const std::vector<std::string> &float_feed_name,
@@ -143,7 +144,7 @@ int PredictorClient::numpy_predict(
     const std::vector<std::vector<py::array_t<int64_t>>> &int_feed_batch,
     const std::vector<std::string> &int_feed_name,
     const std::vector<std::vector<int>> &int_shape,
-    const std::vector<std::vector<int>>& lod_slot_batch,
+    const std::vector<std::vector<int>> &lod_slot_batch,
     const std::vector<std::string> &fetch_name,
     PredictorRes &predict_res_batch,
     const int &pid,
@@ -197,7 +198,8 @@ 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 << "shape " <<  j << " : " << float_shape[vec_idx][j] << std::endl;
+        std::cout << "shape " << j << " : " << float_shape[vec_idx][j]
+                  << std::endl;
       }
       for (uint32_t j = 0; j < lod_slot_batch[vec_idx].size(); ++j) {
         tensor->add_lod(lod_slot_batch[vec_idx][j]);

core/general-client/src/pybind_general_model.cpp

@@ -105,7 +105,7 @@ PYBIND11_MODULE(serving_client, m) {
                   &int_feed_batch,
               const std::vector<std::string> &int_feed_name,
               const std::vector<std::vector<int>> &int_shape,
-              const std::vector<std::vector<int>>& lod_slot_batch,
+              const std::vector<std::vector<int>> &lod_slot_batch,
               const std::vector<std::string> &fetch_name,
               PredictorRes &predict_res_batch,
               const int &pid,

core/general-server/op/general_copy_op.h

@@ -13,20 +13,12 @@
 // limitations under the License.
 
 #pragma once
-#include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include <string>
+#include <vector>
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
 #include "core/predictor/framework/resource.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_dist_kv_infer_op.h

@@ -15,17 +15,9 @@
 #pragma once
 #include <string>
 #include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_dist_kv_quant_infer_op.h

@@ -15,17 +15,9 @@
 #pragma once
 #include <string>
 #include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_infer_helper.h

@@ -15,17 +15,9 @@
 #pragma once
 
 #include <string.h>
+#include <string>
 #include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
 #include "paddle_inference_api.h"  // NOLINT
-#endif
-#include <string>
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_infer_op.cpp

@@ -60,10 +60,11 @@ int GeneralInferOp::inference() {
 
   const TensorVector *in = &input_blob->tensor_vector;
   TensorVector *out = &output_blob->tensor_vector;
-  int batch_size = input_blob->GetBatchSize();
+
+  int batch_size = input_blob->_batch_size;
   VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
 
-  output_blob->SetBatchSize(batch_size);
+  output_blob->_batch_size = batch_size;
 
   VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
 

core/general-server/op/general_infer_op.h

@@ -15,17 +15,9 @@
 #pragma once
 #include <string>
 #include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_reader_op.cpp

@@ -37,9 +37,9 @@ int conf_check(const Request *req,
                const std::shared_ptr<PaddleGeneralModelConfig> &model_config) {
   int var_num = req->insts(0).tensor_array_size();
   if (var_num != model_config->_feed_type.size()) {
-    VLOG(2) << "var num: " << var_num;
-    VLOG(2) << "model config var num: " << model_config->_feed_type.size();
-    LOG(ERROR) << "feed var number not match.";
+    LOG(ERROR) << "feed var number not match: model config["
+               << model_config->_feed_type.size() << "] vs. actual[" << var_num
+               << "]";
     return -1;
   }
 
@@ -73,7 +73,6 @@ int GeneralReaderOp::inference() {
   // reade request from client
   const Request *req = dynamic_cast<const Request *>(get_request_message());
 
-  int batch_size = req->insts_size();
   int input_var_num = 0;
   std::vector<int64_t> elem_type;
   std::vector<int64_t> elem_size;
@@ -82,26 +81,29 @@ int GeneralReaderOp::inference() {
   GeneralBlob *res = mutable_data<GeneralBlob>();
   TensorVector *out = &res->tensor_vector;
 
-  res->SetBatchSize(batch_size);
+  res->SetLogId(log_id);
 
   if (!res) {
-    LOG(ERROR) << "Failed get op tls reader object output";
+    LOG(ERROR) << "(logid=" << log_id
+               << ") Failed get op tls reader object output";
   }
 
   Timer timeline;
   int64_t start = timeline.TimeStampUS();
   int var_num = req->insts(0).tensor_array_size();
-  VLOG(2) << "var num: " << var_num;
+  VLOG(2) << "(logid=" << log_id << ") var num: " << var_num;
+
+  VLOG(2) << "(logid=" << log_id
+          << ") start to call load general model_conf op";
 
-  VLOG(2) << "start to call load general model_conf op";
   baidu::paddle_serving::predictor::Resource &resource =
       baidu::paddle_serving::predictor::Resource::instance();
 
-  VLOG(2) << "get resource pointer done.";
+  VLOG(2) << "(logid=" << log_id << ") get resource pointer done.";
   std::shared_ptr<PaddleGeneralModelConfig> model_config =
       resource.get_general_model_config();
 
-  VLOG(2) << "print general model config done.";
+  VLOG(2) << "(logid=" << log_id << ") print general model config done.";
 
   // TODO(guru4elephant): how to do conditional check?
   /*
@@ -117,17 +119,6 @@ int GeneralReaderOp::inference() {
   elem_type.resize(var_num);
   elem_size.resize(var_num);
   capacity.resize(var_num);
-/*
-message Tensor {
-  repeated bytes data = 1;
-  repeated int32 int_data = 2;
-  repeated int64 int64_data = 3;
-  repeated float float_data = 4;
-  optional int32 elem_type = 5;
-  repeated int32 shape = 6;
-  repeated int32 lod = 7; // only for fetch tensor currently
-};
-*/
   // prepare basic information for input
   for (int i = 0; i < var_num; ++i) {
     paddle::PaddleTensor lod_tensor;
@@ -143,8 +134,9 @@ message Tensor {
       elem_size[i] = sizeof(int32_t);
       lod_tensor.dtype = paddle::PaddleDType::INT32;
     }
-    //implement lod tensor here
+    // implement lod tensor here
     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);
       for (int k = 0; k < req->insts(0).tensor_array(i).lod_size(); ++k) {
         lod_tensor.lod[0].push_back(req->insts(0).tensor_array(i).lod(k));
@@ -152,21 +144,24 @@ message Tensor {
       capacity[i] = 1;
       for (int k = 0; k < req->insts(0).tensor_array(i).shape_size(); ++k) {
         int dim = req->insts(0).tensor_array(i).shape(k);
-        VLOG(2) << "shape for var[" << i << "]: " << dim;
+        VLOG(2) << "(logid=" << log_id << ") shape for var[" << i
+                << "]: " << dim;
         capacity[i] *= dim;
         lod_tensor.shape.push_back(dim);
       }
-      VLOG(2) << "var[" << i << "] is tensor, capacity: " << capacity[i];
-    }
-    else {
+      VLOG(2) << "(logid=" << log_id << ") var[" << i
+              << "] is tensor, capacity: " << capacity[i];
+    } else {
       capacity[i] = 1;
       for (int k = 0; k < req->insts(0).tensor_array(i).shape_size(); ++k) {
         int dim = req->insts(0).tensor_array(i).shape(k);
-        VLOG(2) << "shape for var[" << i << "]: " << dim;
+        VLOG(2) << "(logid=" << log_id << ") shape for var[" << i
+                << "]: " << dim;
         capacity[i] *= dim;
         lod_tensor.shape.push_back(dim);
       }
-      VLOG(2) << "var[" << i << "] is tensor, capacity: " << capacity[i];
+      VLOG(2) << "(logid=" << log_id << ") var[" << i
+              << "] is tensor, capacity: " << capacity[i];
     }
     lod_tensor.name = model_config->_feed_name[i];
     out->push_back(lod_tensor);
@@ -186,11 +181,12 @@ message Tensor {
         } else if (tensor.int_data_size() > 0) {
           data_len = tensor.int_data_size();
         }
-        VLOG(2) << "tensor size for var[" << i << "]: " << data_len;
+        VLOG(2) << "(logid=" << log_id << ") tensor size for var[" << i
+                << "]: " << data_len;
         tensor_size += data_len;
 
         int cur_len = out->at(i).lod[0].back();
-        VLOG(2) << "current len: " << cur_len;
+        VLOG(2) << "(logid=" << log_id << ") current len: " << cur_len;
 
         int sample_len = 0;
         if (tensor.shape_size() == 1) {
@@ -199,21 +195,21 @@ message Tensor {
           sample_len = tensor.shape(0);
         }
         out->at(i).lod[0].push_back(cur_len + sample_len);
-        VLOG(2) << "new len: " << cur_len + sample_len;
+        VLOG(2) << "(logid=" << log_id << ") new len: " << cur_len + sample_len;
       }
       out->at(i).data.Resize(tensor_size * elem_size[i]);
       out->at(i).shape = {};
       for (int j = 1; j < req->insts(0).tensor_array(i).shape_size(); ++j) {
         out->at(i).shape.push_back(req->insts(0).tensor_array(i).shape(j));
       }
-      //if (out->at(i).shape.size() == 1) {
+      // if (out->at(i).shape.size() == 1) {
       //  out->at(i).shape.push_back(1);
       //}
-      VLOG(2) << "var[" << i
+      VLOG(2) << "(logid=" << log_id << ") var[" << i
               << "] is lod_tensor and len=" << out->at(i).lod[0].back();
     } else {
       out->at(i).data.Resize(batch_size * capacity[i] * elem_size[i]);
-      VLOG(2) << "var[" << i
+      VLOG(2) << "(logid=" << log_id << ") var[" << i
               << "] is tensor and capacity=" << batch_size * capacity[i];
     }
   }
@@ -222,8 +218,8 @@ message Tensor {
   for (int i = 0; i < var_num; ++i) {
     if (elem_type[i] == 0) {
       int64_t *dst_ptr = static_cast<int64_t *>(out->at(i).data.data());
-      VLOG(2) << "first element data in var[" << i << "] is "
-              << req->insts(0).tensor_array(i).int64_data(0);
+      VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
+              << "] is " << req->insts(0).tensor_array(i).int64_data(0);
       int offset = 0;
       for (int j = 0; j < batch_size; ++j) {
         int elem_num = req->insts(j).tensor_array(i).int64_data_size();
@@ -233,8 +229,8 @@ message Tensor {
       }
     } else if (elem_type[i] == 1) {
       float *dst_ptr = static_cast<float *>(out->at(i).data.data());
-      VLOG(2) << "first element data in var[" << i << "] is "
-              << req->insts(0).tensor_array(i).float_data(0);
+      VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
+              << "] is " << req->insts(0).tensor_array(i).float_data(0);
       int offset = 0;
       for (int j = 0; j < batch_size; ++j) {
         int elem_num = req->insts(j).tensor_array(i).float_data_size();
@@ -244,8 +240,8 @@ message Tensor {
       }
     } else if (elem_type[i] == 2) {
       int32_t *dst_ptr = static_cast<int32_t *>(out->at(i).data.data());
-      VLOG(2) << "first element data in var[" << i << "] is "
-              << req->insts(0).tensor_array(i).int_data(0);
+      VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
+              << "] is " << req->insts(0).tensor_array(i).int_data(0);
       int offset = 0;
       for (int j = 0; j < batch_size; ++j) {
         int elem_num = req->insts(j).tensor_array(i).int_data_size();
@@ -256,15 +252,15 @@ message Tensor {
     }
   }
 
-  VLOG(2) << "output size: " << out->size();
-
+  VLOG(2) << "(logid=" << log_id << ") output size: " << out->size();
   timeline.Pause();
   int64_t end = timeline.TimeStampUS();
   res->p_size = 0;
+  res->_batch_size = batch_size;
   AddBlobInfo(res, start);
   AddBlobInfo(res, end);
 
-  VLOG(2) << "read data from client success";
+  VLOG(2) << "(logid=" << log_id << ") read data from client success";
   return 0;
 }
 DEFINE_OP(GeneralReaderOp);

core/general-server/op/general_reader_op.h

@@ -13,21 +13,13 @@
 // limitations under the License.
 
 #pragma once
-#include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include <string>
+#include <vector>
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/load_general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
 #include "core/predictor/framework/resource.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_response_op.h

@@ -15,16 +15,8 @@
 #pragma once
 #include <string>
 #include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include "core/general-server/general_model_service.pb.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_text_reader_op.h

@@ -13,21 +13,13 @@
 // limitations under the License.
 
 #pragma once
-#include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include <string>
+#include <vector>
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/load_general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
 #include "core/predictor/framework/resource.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/general-server/op/general_text_response_op.h

@@ -15,17 +15,9 @@
 #pragma once
 #include <string>
 #include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h"  // NOLINT
-#endif
 #include "core/general-server/general_model_service.pb.h"
 #include "core/general-server/op/general_infer_helper.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace baidu {
 namespace paddle_serving {

core/predictor/common/inner_common.h

@@ -50,7 +50,7 @@
 #include "butil/time.h"
 #endif
 
-#include "glog/raw_logging.h"
+#define ERROR_STRING_LEN 10240
 
 #include "core/configure/general_model_config.pb.h"
 #include "core/configure/include/configure_parser.h"

core/predictor/framework/factory.h

@@ -17,7 +17,7 @@
 #include <string>
 #include <utility>
 #include "core/predictor/common/inner_common.h"
-#include "glog/raw_logging.h"
+
 namespace baidu {
 namespace paddle_serving {
 namespace predictor {
@@ -28,7 +28,12 @@ namespace predictor {
     FactoryDerive<D, B>* factory = new (std::nothrow) FactoryDerive<D, B>(); \
     if (factory == NULL ||                                                   \
         FactoryPool<B>::instance().register_factory(tag, factory) != 0) {    \
-      RAW_LOG_FATAL("Failed regist factory: %s in macro!", #D);              \
+      char err_str[ERROR_STRING_LEN];                                        \
+      snprintf(err_str,                                                      \
+               ERROR_STRING_LEN - 1,                                         \
+               "Failed regist factory: %s in macro!",                        \
+               #D);                                                          \
+      RAW_LOG(FATAL, err_str);                                               \
       return -1;                                                             \
     }                                                                        \
     return 0;                                                                \
@@ -54,7 +59,13 @@ namespace predictor {
     if (factory == NULL ||                                                     \
         ::baidu::paddle_serving::predictor::FactoryPool<B>::instance()         \
                 .register_factory(#D, factory) != 0) {                         \
-      RAW_LOG_FATAL("Failed regist factory: %s->%s in macro!", #D, #B);        \
+      char err_str[ERROR_STRING_LEN];                                          \
+      snprintf(err_str,                                                        \
+               ERROR_STRING_LEN - 1,                                           \
+               "Failed regist factory: %s->%s in macro!",                      \
+               #D,                                                             \
+               #B);                                                            \
+      RAW_LOG(FATAL, err_str);                                                 \
       return;                                                                  \
     }                                                                          \
     return;                                                                    \
@@ -66,15 +77,26 @@ namespace predictor {
     ::baidu::paddle_serving::predictor::FactoryDerive<D, B>* factory = new (   \
         ::std::nothrow)::baidu::paddle_serving::predictor::FactoryDerive<D,    \
                                                                          B>(); \
+    char err_str[ERROR_STRING_LEN];                                            \
     if (factory == NULL ||                                                     \
         ::baidu::paddle_serving::predictor::FactoryPool<B>::instance()         \
                 .register_factory(N, factory) != 0) {                          \
-      RAW_LOG_FATAL(                                                           \
-          "Failed regist factory: %s->%s, tag: %s in macro!", #D, #B, N);      \
+      snprintf(err_str,                                                        \
+               ERROR_STRING_LEN - 1,                                           \
+               "Failed regist factory: %s->%s, tag: %s in macro!",             \
+               #D,                                                             \
+               #B,                                                             \
+               N);                                                             \
+      RAW_LOG(FATAL, err_str);                                                 \
       return;                                                                  \
     }                                                                          \
-    RAW_LOG_WARNING(                                                           \
-        "Succ regist factory: %s->%s, tag: %s in macro!", #D, #B, N);          \
+    snprintf(err_str,                                                          \
+             ERROR_STRING_LEN - 1,                                             \
+             "Succ regist factory: %s->%s, tag: %s in macro!",                 \
+             #D,                                                               \
+             #B,                                                               \
+             N);                                                               \
+    RAW_LOG(WARNING, err_str);                                                 \
     return;                                                                    \
   }
 
@@ -102,24 +124,35 @@ class FactoryPool {
   }
 
   int register_factory(const std::string& tag, FactoryBase<B>* factory) {
+    char err_str[ERROR_STRING_LEN];
     typename std::map<std::string, FactoryBase<B>*>::iterator it =
         _pool.find(tag);
     if (it != _pool.end()) {
-      RAW_LOG_FATAL("Insert duplicate with tag: %s", tag.c_str());
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Insert duplicate with tag: %s",
+               tag.c_str());
+      RAW_LOG(FATAL, err_str);
       return -1;
     }
 
     std::pair<typename std::map<std::string, FactoryBase<B>*>::iterator, bool>
         r = _pool.insert(std::make_pair(tag, factory));
     if (!r.second) {
-      RAW_LOG_FATAL("Failed insert new factory with: %s", tag.c_str());
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Failed insert new factory with: %s",
+               tag.c_str());
+      RAW_LOG(FATAL, err_str);
       return -1;
     }
 
-    RAW_LOG_INFO("Succ insert one factory, tag: %s, base type %s",
-                 tag.c_str(),
-                 typeid(B).name());
-
+    snprintf(err_str,
+             ERROR_STRING_LEN - 1,
+             "Succ insert one factory, tag: %s, base type %s",
+             tag.c_str(),
+             typeid(B).name());
+    RAW_LOG(INFO, err_str);
     return 0;
   }
 
@@ -127,9 +160,13 @@ class FactoryPool {
     typename std::map<std::string, FactoryBase<B>*>::iterator it =
         _pool.find(tag);
     if (it == _pool.end() || it->second == NULL) {
-      RAW_LOG_FATAL("Not found factory pool, tag: %s, pool size %u",
-                    tag.c_str(),
-                    _pool.size());
+      char err_str[ERROR_STRING_LEN];
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Not found factory pool, tag: %s, pool size %u",
+               tag.c_str(),
+               _pool.size());
+      RAW_LOG(FATAL, err_str);
       return NULL;
     }
 

core/predictor/framework/infer.h

@@ -603,6 +603,7 @@ class VersionedInferEngine : public InferEngine {
       LOG(ERROR) << "Failed generate engine with type:" << engine_type;
       return -1;
     }
+#ifndef BCLOUD
     VLOG(2) << "FLAGS_logtostderr " << FLAGS_logtostderr;
     int tmp = FLAGS_logtostderr;
     if (engine->proc_initialize(conf, version) != 0) {
@@ -611,6 +612,12 @@ class VersionedInferEngine : public InferEngine {
     }
     VLOG(2) << "FLAGS_logtostderr " << FLAGS_logtostderr;
     FLAGS_logtostderr = tmp;
+#else
+    if (engine->proc_initialize(conf, version) != 0) {
+      LOG(ERROR) << "Failed initialize engine, type:" << engine_type;
+      return -1;
+    }
+#endif
     auto r = _versions.insert(std::make_pair(engine->version(), engine));
     if (!r.second) {
       LOG(ERROR) << "Failed insert item: " << engine->version()

core/predictor/framework/op_repository.h

@@ -62,7 +62,10 @@ class OpRepository {
   template <typename OP_TYPE>
   void regist_op(std::string op_type) {
     _repository[op_type] = &OpFactory<OP_TYPE>::instance();
-    RAW_LOG_INFO("Succ regist op: %s", op_type.c_str());
+    char err_str[ERROR_STRING_LEN];
+    snprintf(
+        err_str, ERROR_STRING_LEN - 1, "Succ regist op: %s", op_type.c_str());
+    RAW_LOG(INFO, err_str);
   }
 
   Op* get_op(std::string op_type);

core/predictor/framework/resource.cpp

@@ -17,6 +17,9 @@
 #include <string>
 #include "core/predictor/common/inner_common.h"
 #include "core/predictor/framework/kv_manager.h"
+#ifdef BCLOUD
+#include "aipe_sec_client.h"  // NOLINT
+#endif
 namespace baidu {
 namespace paddle_serving {
 namespace predictor {
@@ -109,6 +112,42 @@ int Resource::initialize(const std::string& path, const std::string& file) {
   }
   LOG(WARNING) << "Successfully proc initialized mempool wrapper";
 
+#ifdef WITH_AUTH
+  std::string product_name_str = resource_conf.auth_product_name();
+  std::string container_id_str = resource_conf.auth_container_id();
+
+  char* product_name = new char[product_name_str.size() + 1];
+  snprintf(product_name,
+           product_name_str.size() + 1,
+           "%s",
+           product_name_str.c_str());
+  char* container_id = new char[container_id_str.size() + 1];
+  snprintf(container_id,
+           container_id_str.size() + 1,
+           "%s",
+           container_id_str.c_str());
+
+  aipe_auth_request request;
+  request.product_name = product_name;
+  request.container_id = container_id;
+  request.request_ts = (int64_t)time(NULL);
+
+  LOG(INFO) << "\nEasypack info"
+            << "\nproduct name: " << request.product_name
+            << "\ncontainer_id: " << request.container_id
+            << "\nrequest time stamp: " << request.request_ts;
+
+  aipe_auth_response response;
+  response = check_auth(request);
+
+  if (response.result == 0) {
+    LOG(INFO) << "Authentication succeed.";
+  } else {
+    LOG(ERROR) << "Authentication failed. Error code: " << response.result;
+    return -1;
+  }
+#endif
+
   if (FLAGS_enable_model_toolkit) {
     int err = 0;
     std::string model_toolkit_path = resource_conf.model_toolkit_path();

core/predictor/framework/service_manager.h

@@ -23,17 +23,24 @@ namespace predictor {
 
 #define REGIST_FORMAT_SERVICE(svr_name, svr)                                 \
   do {                                                                       \
+    char err_str[ERROR_STRING_LEN];                                          \
     int ret =                                                                \
         ::baidu::paddle_serving::predictor::FormatServiceManager::instance() \
             .regist_service(svr_name, svr);                                  \
     if (ret != 0) {                                                          \
-      RAW_LOG_ERROR("Failed regist service[%s][%s]",                         \
-                    svr_name.c_str(),                                        \
-                    typeid(svr).name());                                     \
+      snprintf(err_str,                                                      \
+               ERROR_STRING_LEN - 1,                                         \
+               "Failed regist service[%s][%s]",                              \
+               svr_name.c_str(),                                             \
+               typeid(svr).name());                                          \
+      RAW_LOG(ERROR, err_str);                                               \
     } else {                                                                 \
-      RAW_LOG_INFO("Success regist service[%s][%s]",                         \
-                   svr_name.c_str(),                                         \
-                   typeid(svr).name());                                      \
+      snprintf(err_str,                                                      \
+               ERROR_STRING_LEN - 1,                                         \
+               "Success regist service[%s][%s]",                             \
+               svr_name.c_str(),                                             \
+               typeid(svr).name());                                          \
+      RAW_LOG(INFO, err_str);                                                \
     }                                                                        \
   } while (0)
 
@@ -42,31 +49,46 @@ class FormatServiceManager {
   typedef google::protobuf::Service Service;
 
   int regist_service(const std::string& svr_name, Service* svr) {
+    char err_str[ERROR_STRING_LEN];
     if (_service_map.find(svr_name) != _service_map.end()) {
-      RAW_LOG_ERROR("Service[%s][%s] already exist!",
-                    svr_name.c_str(),
-                    typeid(svr).name());
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Service[%s][%s] already exist!",
+               svr_name.c_str(),
+               typeid(svr).name());
+      RAW_LOG(ERROR, err_str);
       return -1;
     }
 
     std::pair<boost::unordered_map<std::string, Service*>::iterator, bool> ret;
     ret = _service_map.insert(std::make_pair(svr_name, svr));
     if (ret.second == false) {
-      RAW_LOG_ERROR("Service[%s][%s] insert failed!",
-                    svr_name.c_str(),
-                    typeid(svr).name());
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Service[%s][%s] insert failed!",
+               svr_name.c_str(),
+               typeid(svr).name());
+      RAW_LOG(ERROR, err_str);
       return -1;
     }
 
-    RAW_LOG_INFO("Service[%s] insert successfully!", svr_name.c_str());
+    snprintf(err_str,
+             ERROR_STRING_LEN - 1,
+             "Service[%s] insert successfully!",
+             svr_name.c_str());
+    RAW_LOG(INFO, err_str);
     return 0;
   }
 
   Service* get_service(const std::string& svr_name) {
+    char err_str[ERROR_STRING_LEN];
     boost::unordered_map<std::string, Service*>::iterator res;
     if ((res = _service_map.find(svr_name)) == _service_map.end()) {
-      RAW_LOG_WARNING("Service[%s] not found in service manager!",
-                      svr_name.c_str());
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Service[%s] not found in service manager!",
+               svr_name.c_str());
+      RAW_LOG(WARNING, err_str);
       return NULL;
     }
     return (*res).second;

core/predictor/src/pdserving.cpp

@@ -202,8 +202,6 @@ int main(int argc, char** argv) {
   }
   VLOG(2) << "Succ call pthread worker start function";
 
-#ifndef BCLOUD
-
   if (Resource::instance().general_model_initialize(FLAGS_resource_path,
                                                     FLAGS_resource_file) != 0) {
     LOG(ERROR) << "Failed to initialize general model conf: "
@@ -213,6 +211,7 @@ int main(int argc, char** argv) {
 
   VLOG(2) << "Succ initialize general model";
 
+#ifndef BCLOUD
   // FATAL messages are output to stderr
   FLAGS_stderrthreshold = 3;
 #endif

core/sdk-cpp/include/abtest.h

@@ -50,9 +50,9 @@ class WeightedRandomRender : public EndpointRouterBase {
     Factory<WeightedRandomRender, EndpointRouterBase>* factory =
         new (std::nothrow) Factory<WeightedRandomRender, EndpointRouterBase>();
     if (factory == NULL) {
-      RAW_LOG_ERROR(
-          "Failed regist factory: WeightedRandomRender->EndpointRouterBase in "
-          "macro!");
+      RAW_LOG(ERROR,
+              "Failed regist factory: WeightedRandomRender->EndpointRouterBase \
+          in macro!");
       return -1;
     }
 
@@ -62,9 +62,9 @@ class WeightedRandomRender : public EndpointRouterBase {
     // together.
     if (FactoryPool<EndpointRouterBase>::instance().register_factory(
             "WeightedRandomRender", factory) != 0) {
-      RAW_LOG_INFO(
-          "Factory has been registed: "
-          "WeightedRandomRender->EndpointRouterBase.");
+      RAW_LOG(INFO,
+              "Factory has been registed: \
+              WeightedRandomRender->EndpointRouterBase.");
     }
 
     return 0;

core/sdk-cpp/include/factory.h

@@ -18,7 +18,6 @@
 #include <utility>
 #include "core/sdk-cpp/include/common.h"
 #include "core/sdk-cpp/include/stub_impl.h"
-#include "glog/raw_logging.h"
 
 namespace baidu {
 namespace paddle_serving {
@@ -28,12 +27,20 @@ namespace sdk_cpp {
 namespace brpc = baidu::rpc;
 #endif
 
+#define ERROR_STRING_LEN 10240
+
 #define INLINE_REGIST_OBJECT(D, B, E)                                    \
   do {                                                                   \
     Factory<D, B>* factory = new (std::nothrow) Factory<D, B>();         \
     if (factory == NULL ||                                               \
         FactoryPool<B>::instance().register_factory(#D, factory) != 0) { \
-      RAW_LOG_ERROR("Failed regist factory: %s->%s in macro!", #D, #B);  \
+      char err_str[ERROR_STRING_LEN];                                    \
+      snprintf(err_str,                                                  \
+               ERROR_STRING_LEN - 1,                                     \
+               "Failed regist factory: %s->%s in macro!",                \
+               #D,                                                       \
+               #B);                                                      \
+      RAW_LOG(ERROR, err_str);                                           \
       return E;                                                          \
     }                                                                    \
   } while (0)
@@ -43,7 +50,12 @@ namespace brpc = baidu::rpc;
     Factory<D, B>* factory = new (std::nothrow) Factory<D, B>();          \
     if (factory == NULL ||                                                \
         FactoryPool<B>::instance().register_factory(tag, factory) != 0) { \
-      RAW_LOG_ERROR("Failed regist factory: %s in macro!", #D);           \
+      char err_str[ERROR_STRING_LEN];                                     \
+      snprintf(err_str,                                                   \
+               ERROR_STRING_LEN - 1,                                      \
+               "Failed regist factory: %s in macro!",                     \
+               #D);                                                       \
+      RAW_LOG(ERROR, err_str);                                            \
       return -1;                                                          \
     }                                                                     \
     return 0;                                                             \
@@ -66,7 +78,13 @@ namespace brpc = baidu::rpc;
     if (factory == NULL ||                                                     \
         ::baidu::paddle_serving::sdk_cpp::FactoryPool<B>::instance()           \
                 .register_factory(#D, factory) != 0) {                         \
-      RAW_LOG_ERROR("Failed regist factory: %s->%s in macro!", #D, #B);        \
+      char err_str[ERROR_STRING_LEN];                                          \
+      snprintf(err_str,                                                        \
+               ERROR_STRING_LEN - 1,                                           \
+               "Failed regist factory: %s->%s in macro!",                      \
+               #D,                                                             \
+               #B);                                                            \
+      RAW_LOG(ERROR, err_str);                                                 \
       return;                                                                  \
     }                                                                          \
     return;                                                                    \
@@ -80,8 +98,14 @@ namespace brpc = baidu::rpc;
     if (factory == NULL ||                                                     \
         ::baidu::paddle_serving::sdk_cpp::FactoryPool<B>::instance()           \
                 .register_factory(T, factory) != 0) {                          \
-      RAW_LOG_ERROR(                                                           \
-          "Failed regist factory: %s->%s, tag %s in macro!", #D, #B, T);       \
+      char err_str[ERROR_STRING_LEN];                                          \
+      snprintf(err_str,                                                        \
+               ERROR_STRING_LEN - 1,                                           \
+               "Failed regist factory: %s->%s, tag %s in macro!",              \
+               #D,                                                             \
+               #B,                                                             \
+               T);                                                             \
+      RAW_LOG(ERROR, err_str);                                                 \
       return;                                                                  \
     }                                                                          \
     return;                                                                    \
@@ -108,8 +132,13 @@ namespace brpc = baidu::rpc;
         ::baidu::paddle_serving::sdk_cpp::FactoryPool<                     \
             ::baidu::paddle_serving::sdk_cpp::Stub>::instance()            \
                 .register_factory(T, factory) != 0) {                      \
-      RAW_LOG_ERROR(                                                       \
-          "Failed regist factory: %s->Stub, tag: %s in macro!", #D, T);    \
+      char err_str[ERROR_STRING_LEN];                                      \
+      snprintf(err_str,                                                    \
+               ERROR_STRING_LEN - 1,                                       \
+               "Failed regist factory: %s->Stub, tag: %s in macro!",       \
+               #D,                                                         \
+               T);                                                         \
+      RAW_LOG(ERROR, err_str);                                             \
       return;                                                              \
     }                                                                      \
     return;                                                                \
@@ -146,14 +175,24 @@ class FactoryPool {
     typename std::map<std::string, FactoryBase<B>*>::iterator it =
         _pool.find(tag);
     if (it != _pool.end()) {
-      RAW_LOG_ERROR("Insert duplicate with tag: %s", tag.c_str());
+      char err_str[ERROR_STRING_LEN];
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Insert duplicate with tag: %s",
+               tag.c_str());
+      RAW_LOG(ERROR, err_str);
       return -1;
     }
 
     std::pair<typename std::map<std::string, FactoryBase<B>*>::iterator, bool>
         r = _pool.insert(std::make_pair(tag, factory));
     if (!r.second) {
-      RAW_LOG_ERROR("Failed insert new factory with: %s", tag.c_str());
+      char err_str[ERROR_STRING_LEN];
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Failed insert new factory with: %s",
+               tag.c_str());
+      RAW_LOG(ERROR, err_str);
       return -1;
     }
 
@@ -164,9 +203,13 @@ class FactoryPool {
     typename std::map<std::string, FactoryBase<B>*>::iterator it =
         _pool.find(tag);
     if (it == _pool.end() || it->second == NULL) {
-      RAW_LOG_ERROR("Not found factory pool, tag: %s, pool size: %u",
-                    tag.c_str(),
-                    _pool.size());
+      char err_str[ERROR_STRING_LEN];
+      snprintf(err_str,
+               ERROR_STRING_LEN - 1,
+               "Not found factory pool, tag: %s, pool size: %u",
+               tag.c_str(),
+               _pool.size());
+      RAW_LOG(ERROR, err_str);
       return NULL;
     }
 

doc/PIPELINE_OP.md

@@ -90,5 +90,3 @@ rec_op = RecOp(
 | client_config       | 客户端配置文件  | Op接收请求作为客户端访问rpc服务，因此需要客户端的配置文件    |
 | local predictor限定 |                 |                                                              |
 | model_config        | 模型配置文件    | 由于local predictor和Op运行在一台机器上，因此需要模型配置来启动local predictor |
-
-

paddle_inference/inferencer-fluid-cpu/include/fluid_cpu_engine.h

@@ -23,6 +23,7 @@
 #include "core/configure/inferencer_configure.pb.h"
 #include "core/predictor/framework/infer.h"
 #include "paddle_inference_api.h"  // NOLINT
+//#include "predictor/framework/infer.h"
 
 namespace baidu {
 namespace paddle_serving {

python/paddle_serving_app/local_predict.py

@@ -121,7 +121,7 @@ class Debugger(object):
                     name])
             if self.feed_types_[name] == 0:
                 feed[name] = feed[name].astype("int64")
-            elif self.feed_types_[name] == 1:    
+            elif self.feed_types_[name] == 1:
                 feed[name] = feed[name].astype("float32")
             elif self.feed_types_[name] == 2:
                 feed[name] = feed[name].astype("int32")
@@ -131,7 +131,7 @@ class Debugger(object):
             if "{}.lod".format(name) in feed:
                 input_tensor.set_lod([feed["{}.lod".format(name)]])
             if batch == False:
-                input_tensor.copy_from_cpu(feed[name][np.newaxis,:])
+                input_tensor.copy_from_cpu(feed[name][np.newaxis, :])
             else:
                 input_tensor.copy_from_cpu(feed[name])
         output_tensors = []

python/paddle_serving_client/__init__.py

@@ -233,7 +233,12 @@ class Client(object):
             #    key))
             pass
 
-    def predict(self, feed=None, fetch=None, batch=False, need_variant_tag=False, log_id=0):
+    def predict(self,
+                feed=None,
+                fetch=None,
+                batch=False,
+                need_variant_tag=False,
+                log_id=0):
         self.profile_.record('py_prepro_0')
 
         if feed is None or fetch is None:
@@ -262,7 +267,7 @@ class Client(object):
         int_shape = []
         lod_slot_batch = []
         float_shape = []
-        
+
         fetch_names = []
         counter = 0
         batch_size = len(feed_batch)
@@ -292,7 +297,7 @@ class Client(object):
                         int_feed_names.append(key)
                         shape_lst = []
                         if batch == False:
-                            feed_i[key] = feed_i[key][np.newaxis,:]
+                            feed_i[key] = feed_i[key][np.newaxis, :]
                             shape_lst.append(1)
                         if isinstance(feed_i[key], np.ndarray):
                             print("feed_i_key shape", feed_i[key].shape)
@@ -318,7 +323,7 @@ class Client(object):
                         float_feed_names.append(key)
                         shape_lst = []
                         if batch == False:
-                            feed_i[key] = feed_i[key][np.newaxis,:]
+                            feed_i[key] = feed_i[key][np.newaxis, :]
                             shape_lst.append(1)
                         if isinstance(feed_i[key], np.ndarray):
                             print("feed_i_key shape", feed_i[key].shape)
@@ -349,10 +354,11 @@ class Client(object):
         if self.all_numpy_input:
             res = self.client_handle_.numpy_predict(
                 float_slot_batch, float_feed_names, float_shape, int_slot_batch,
-                int_feed_names, int_shape, lod_slot_batch, fetch_names, result_batch_handle,
-                self.pid, log_id)
+                int_feed_names, int_shape, lod_slot_batch, fetch_names,
+                result_batch_handle, self.pid, log_id)
         elif self.has_numpy_input == False:
-            raise ValueError("Please make sure all of your inputs are numpy array")
+            raise ValueError(
+                "Please make sure all of your inputs are numpy array")
         else:
             raise ValueError(
                 "Please make sure the inputs are all in list type or all in numpy.array type"

python/paddle_serving_server/__init__.py

@@ -103,8 +103,8 @@ class OpSeqMaker(object):
             elif len(node.dependencies) == 1:
                 if node.dependencies[0].name != self.workflow.nodes[-1].name:
                     raise Exception(
-                        'You must add op in order in OpSeqMaker. The previous op is {}, but the current op is followed by {}.'.
-                        format(node.dependencies[0].name, self.workflow.nodes[
+                        'You must add op in order in OpSeqMaker. The previous op is {}, but the current op is followed by {}.'
+                        .format(node.dependencies[0].name, self.workflow.nodes[
                             -1].name))
         self.workflow.nodes.extend([node])
 
@@ -157,6 +157,8 @@ class Server(object):
         self.cur_path = os.getcwd()
         self.use_local_bin = False
         self.mkl_flag = False
+        self.product_name = None
+        self.container_id = None
         self.model_config_paths = None  # for multi-model in a workflow
 
     def set_max_concurrency(self, concurrency):
@@ -191,6 +193,16 @@ class Server(object):
     def set_ir_optimize(self, flag=False):
         self.ir_optimization = flag
 
+    def set_product_name(self, product_name=None):
+        if product_name == None:
+            raise ValueError("product_name can't be None.")
+        self.product_name = product_name
+
+    def set_container_id(self, container_id):
+        if container_id == None:
+            raise ValueError("container_id can't be None.")
+        self.container_id = container_id
+
     def check_local_bin(self):
         if "SERVING_BIN" in os.environ:
             self.use_local_bin = True
@@ -254,6 +266,10 @@ class Server(object):
             self.resource_conf.model_toolkit_file = self.model_toolkit_fn
             self.resource_conf.general_model_path = workdir
             self.resource_conf.general_model_file = self.general_model_config_fn
+            if self.product_name != None:
+                self.resource_conf.auth_product_name = self.product_name
+            if self.container_id != None:
+                self.resource_conf.auth_container_id = self.container_id
 
     def _write_pb_str(self, filepath, pb_obj):
         with open(filepath, "w") as fout:
@@ -351,8 +367,8 @@ class Server(object):
                 if os.path.exists(tar_name):
                     os.remove(tar_name)
                 raise SystemExit(
-                    'Download failed, please check your network or permission of {}.'.
-                    format(self.module_path))
+                    'Download failed, please check your network or permission of {}.'
+                    .format(self.module_path))
             else:
                 try:
                     print('Decompressing files ..')
@@ -363,8 +379,8 @@ class Server(object):
                     if os.path.exists(exe_path):
                         os.remove(exe_path)
                     raise SystemExit(
-                        'Decompressing failed, please check your permission of {} or disk space left.'.
-                        format(self.module_path))
+                        'Decompressing failed, please check your permission of {} or disk space left.'
+                        .format(self.module_path))
                 finally:
                     os.remove(tar_name)
         #release lock
@@ -541,7 +557,6 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
         results, tag = ret
         resp.tag = tag
         resp.err_code = 0
-
         if not self.is_multi_model_:
             results = {'general_infer_0': results}
         for model_name, model_result in results.items():

python/paddle_serving_server/serve.py

@@ -58,6 +58,16 @@ def parse_args():  # pylint: disable=doc-string-missing
         default=False,
         action="store_true",
         help="Use Multi-language-service")
+    parser.add_argument(
+        "--product_name",
+        type=str,
+        default=None,
+        help="product_name for authentication")
+    parser.add_argument(
+        "--container_id",
+        type=str,
+        default=None,
+        help="container_id for authentication")
     return parser.parse_args()
 
 
@@ -101,6 +111,10 @@ def start_standard_model():  # pylint: disable=doc-string-missing
     server.use_mkl(use_mkl)
     server.set_max_body_size(max_body_size)
     server.set_port(port)
+    if args.product_name != None:
+        server.set_product_name(args.product_name)
+    if args.container_id != None:
+        server.set_container_id(args.container_id)
 
     server.load_model_config(model)
     server.prepare_server(workdir=workdir, port=port, device=device)

python/paddle_serving_server_gpu/__init__.py

@@ -73,6 +73,16 @@ def serve_args():
         default=False,
         action="store_true",
         help="Use Multi-language-service")
+    parser.add_argument(
+        "--product_name",
+        type=str,
+        default=None,
+        help="product_name for authentication")
+    parser.add_argument(
+        "--container_id",
+        type=str,
+        default=None,
+        help="container_id for authentication")
     return parser.parse_args()
 
 
@@ -141,8 +151,8 @@ class OpSeqMaker(object):
             elif len(node.dependencies) == 1:
                 if node.dependencies[0].name != self.workflow.nodes[-1].name:
                     raise Exception(
-                        'You must add op in order in OpSeqMaker. The previous op is {}, but the current op is followed by {}.'.
-                        format(node.dependencies[0].name, self.workflow.nodes[
+                        'You must add op in order in OpSeqMaker. The previous op is {}, but the current op is followed by {}.'
+                        .format(node.dependencies[0].name, self.workflow.nodes[
                             -1].name))
         self.workflow.nodes.extend([node])
 
@@ -196,6 +206,8 @@ class Server(object):
         self.use_local_bin = False
         self.gpuid = 0
         self.model_config_paths = None  # for multi-model in a workflow
+        self.product_name = None
+        self.container_id = None
 
     def set_max_concurrency(self, concurrency):
         self.max_concurrency = concurrency
@@ -229,6 +241,16 @@ class Server(object):
     def set_ir_optimize(self, flag=False):
         self.ir_optimization = flag
 
+    def set_product_name(self, product_name=None):
+        if product_name == None:
+            raise ValueError("product_name can't be None.")
+        self.product_name = product_name
+
+    def set_container_id(self, container_id):
+        if container_id == None:
+            raise ValueError("container_id can't be None.")
+        self.container_id = container_id
+
     def check_local_bin(self):
         if "SERVING_BIN" in os.environ:
             self.use_local_bin = True
@@ -302,6 +324,10 @@ class Server(object):
             self.resource_conf.model_toolkit_file = self.model_toolkit_fn
             self.resource_conf.general_model_path = workdir
             self.resource_conf.general_model_file = self.general_model_config_fn
+            if self.product_name != None:
+                self.resource_conf.auth_product_name = self.product_name
+            if self.container_id != None:
+                self.resource_conf.auth_container_id = self.container_id
 
     def _write_pb_str(self, filepath, pb_obj):
         with open(filepath, "w") as fout:
@@ -393,8 +419,8 @@ class Server(object):
                 if os.path.exists(tar_name):
                     os.remove(tar_name)
                 raise SystemExit(
-                    'Download failed, please check your network or permission of {}.'.
-                    format(self.module_path))
+                    'Download failed, please check your network or permission of {}.'
+                    .format(self.module_path))
             else:
                 try:
                     print('Decompressing files ..')
@@ -405,8 +431,8 @@ class Server(object):
                     if os.path.exists(exe_path):
                         os.remove(exe_path)
                     raise SystemExit(
-                        'Decompressing failed, please check your permission of {} or disk space left.'.
-                        format(self.module_path))
+                        'Decompressing failed, please check your permission of {} or disk space left.'
+                        .format(self.module_path))
                 finally:
                     os.remove(tar_name)
         #release lock

python/paddle_serving_server_gpu/serve.py

@@ -65,6 +65,11 @@ def start_gpu_card_model(index, gpuid, args):  # pylint: disable=doc-string-miss
     server.set_ir_optimize(ir_optim)
     server.set_max_body_size(max_body_size)
 
+    if args.product_name != None:
+        server.set_product_name(args.product_name)
+    if args.container_id != None:
+        server.set_container_id(args.container_id)
+
     server.load_model_config(model)
     server.prepare_server(workdir=workdir, port=port, device=device)
     if gpuid >= 0:
@@ -83,8 +88,8 @@ def start_multi_card(args):  # pylint: disable=doc-string-missing
             for ids in gpus:
                 if int(ids) >= len(env_gpus):
                     print(
-                        " Max index of gpu_ids out of range, the number of CUDA_VISIBLE_DEVICES is {}.".
-                        format(len(env_gpus)))
+                        " Max index of gpu_ids out of range, the number of CUDA_VISIBLE_DEVICES is {}."
+                        .format(len(env_gpus)))
                     exit(-1)
         else:
             env_gpus = []

python/setup.py.app.in

@@ -43,7 +43,7 @@ if '${PACK}' == 'ON':
 
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'sentencepiece', 'opencv-python<=4.2.0.32', 'pillow',
-    'shapely', 'pyclipper'
+    'shapely<=1.6.1', 'pyclipper'
 ]
 
 packages=['paddle_serving_app',