diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/CMakeLists.txt b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/CMakeLists.txt
index 4f148869f84e42fbc6bdb29ba42f5a9b274b2397..ad86983d91219d4e71d32dc9a4a1632add362ecc 100755
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/CMakeLists.txt
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/CMakeLists.txt
@@ -47,6 +47,8 @@ else ()
   find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
   include_directories("${PADDLE_LIB}/paddle/include")
   link_directories("${PADDLE_LIB}/paddle/lib")
+  link_directories("${PADDLE_LIB}/third_party/install/paddle2onnx/lib")
+  link_directories("${PADDLE_LIB}/third_party/install/onnxruntime/lib")
 endif ()
 include_directories(${OpenCV_INCLUDE_DIRS})
 
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/README.md b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/README.md
index ae168ae8faea0c2cbffe1342459bcfc50ad3e47c..397ac01cec59be85eed84f15ba0b634049a9a416 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/README.md
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/README.md
@@ -91,7 +91,7 @@ cd build
 cmake  .. \
     -DWITH_CONTRIB=OFF \
     -DWITH_MKL=ON \
-    -DWITH_MKLDNN=ON  \
+    -DWITH_MKLDNN=OFF  \
     -DWITH_TESTING=OFF \
     -DCMAKE_BUILD_TYPE=Release \
     -DWITH_INFERENCE_API_TEST=OFF \
@@ -205,9 +205,6 @@ make -j
   * gpu_id：使用的GPU卡号；
   * gpu_mem：显存；
   * cpu_math_library_num_threads：底层科学计算库所用线程的数量；
-  * use_mkldnn：是否使用MKLDNN加速；
-  * use_tensorrt: 是否使用tensorRT进行加速；
-  * use_fp16：是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效；
   * cls_model_path：预测模型结构文件路径；
   * cls_params_path：预测模型参数文件路径；
   * resize_short_size：预处理时图像缩放大小；
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls.h b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls.h
index f7a8711e7d48bf48c9f068de22003650e39c9bc5..618be51d9a55d30bd6525877294d5d11f82a6b7c 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls.h
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls.h
@@ -40,16 +40,12 @@ public:
                       const std::string &params_path, const bool &use_gpu,
                       const int &gpu_id, const int &gpu_mem,
                       const int &cpu_math_library_num_threads,
-                      const bool &use_mkldnn, const bool &use_tensorrt,
-                      const bool &use_fp16, const int &resize_short_size,
+                      const int &resize_short_size,
                       const int &crop_size) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
     this->gpu_mem_ = gpu_mem;
     this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
-    this->use_mkldnn_ = use_mkldnn;
-    this->use_tensorrt_ = use_tensorrt;
-    this->use_fp16_ = use_fp16;
 
     this->resize_short_size_ = resize_short_size;
     this->crop_size_ = crop_size;
@@ -70,9 +66,6 @@ private:
   int gpu_id_ = 0;
   int gpu_mem_ = 4000;
   int cpu_math_library_num_threads_ = 4;
-  bool use_mkldnn_ = false;
-  bool use_tensorrt_ = false;
-  bool use_fp16_ = false;
 
   std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
   std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls_config.h b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls_config.h
index 231738b4b5ae21026a1b3d25919ccc187368a903..7b0fac0d85387d81b51a1b549e505b08c4dd9ec3 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls_config.h
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/include/cls_config.h
@@ -39,11 +39,6 @@ public:
     this->cpu_math_library_num_threads =
         stoi(config_map_["cpu_math_library_num_threads"]);
 
-    this->use_mkldnn = bool(stoi(config_map_["use_mkldnn"]));
-
-    this->use_tensorrt = bool(stoi(config_map_["use_tensorrt"]));
-    this->use_fp16 = bool(stoi(config_map_["use_fp16"]));
-
     this->cls_model_path.assign(config_map_["cls_model_path"]);
 
     this->cls_params_path.assign(config_map_["cls_params_path"]);
@@ -61,11 +56,6 @@ public:
 
   int cpu_math_library_num_threads = 1;
 
-  bool use_mkldnn = false;
-
-  bool use_tensorrt = false;
-  bool use_fp16 = false;
-
   std::string cls_model_path;
 
   std::string cls_params_path;
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/cls.cpp b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/cls.cpp
index febf4c70c2c104670ffe3df0284aa0598cd34015..1ba8102988550be82e98501f253a3899b9ebba3f 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/cls.cpp
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/cls.cpp
@@ -24,20 +24,8 @@ void Classifier::LoadModel(const std::string &model_path,
 
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
-    if (this->use_tensorrt_) {
-      config.EnableTensorRtEngine(
-          1 << 20, 1, 3,
-          this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
-                          : paddle_infer::Config::Precision::kFloat32,
-          false, false);
-    }
   } else {
     config.DisableGpu();
-    if (this->use_mkldnn_) {
-      config.EnableMKLDNN();
-      // cache 10 different shapes for mkldnn to avoid memory leak
-      config.SetMkldnnCacheCapacity(10);
-    }
     config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
   }
 
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/main.cpp b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/main.cpp
index 919bcabe3e6ecd2e09338f05f9340c72f26eb35c..82a564e1777968f7e8229b06acb691ddaa2f824d 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/main.cpp
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/src/main.cpp
@@ -61,8 +61,7 @@ int main(int argc, char **argv) {
 
   Classifier classifier(config.cls_model_path, config.cls_params_path,
                         config.use_gpu, config.gpu_id, config.gpu_mem,
-                        config.cpu_math_library_num_threads, config.use_mkldnn,
-                        config.use_tensorrt, config.use_fp16,
+                        config.cpu_math_library_num_threads,
                         config.resize_short_size, config.crop_size);
 
   double elapsed_time = 0.0;
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/tools/build.sh b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/tools/build.sh
index b2ec278e67bfd628d5c429d1212526260d2a9b69..b60bd7bdd1c03e81cbdd154448fc8951cc0f390c 100755
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/tools/build.sh
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_cpp/tools/build.sh
@@ -2,7 +2,6 @@ OPENCV_DIR=../opencv-3.4.7/opencv3/
 LIB_DIR=../paddle_inference/
 CUDA_LIB_DIR=/usr/local/cuda/lib64
 CUDNN_LIB_DIR=/usr/lib64
-TENSORRT_DIR=/usr/local/TensorRT-7.2.3.4
 
 BUILD_DIR=build
 rm -rf ${BUILD_DIR}
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/README.md b/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/README.md
index 2814a6ade10cb5812b8844867a0cef9d73d6736c..6f467bc158de46c973d83ed3148b982281cd798a 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/README.md
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/README.md
@@ -13,7 +13,7 @@
 
 ## 1. 简介
 
-Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。相比于直接基于预训练模型进行预测，Paddle Inference可使用MKLDNN、CUDNN、TensorRT进行预测加速，从而实现更优的推理性能。
+Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。
 
 本文档主要基于Paddle Inference的mobilenet_v3_small模型推理。
 
diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py b/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py
index 24d58bd99e25e21a3eca8672a47e28d78ccb55a0..5bbcdd6046f66ba96ec3a5de319b0a351fd2f884 100644
--- a/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py
+++ b/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py
@@ -80,6 +80,8 @@ class InferenceEngine(object):
             config.enable_use_gpu(1000, 0)
         else:
             config.disable_gpu()
+            # The thread num should not be greater than the number of cores in the CPU.
+            config.set_cpu_math_library_num_threads(4)
 
         # enable memory optim
         config.enable_memory_optim()
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md b/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md
index 238c3802ce8c198ca37411761e005b2b5fac8e36..0a928deda27276a1dc176647d702b81b614eab68 100644
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/README.md
@@ -16,6 +16,7 @@
 - Slim训练部署：包括PACT在线量化、离线量化。
 - 更多训练环境：包括Windows GPU/CPU、Linux NPU、Linux DCU等多种环境。
 
+
 | 算法论文 | 模型名称 | 模型类型 | 基础<br>训练预测 | 更多<br>训练方式 | 更多<br>部署方式 | Slim<br>训练部署 |  更多<br>训练环境  |
 | :--- | :--- |  :----:  | :--------: |  :----:  |   :----:  |   :----:  |   :----:  |
 | MobileNetV3     | mobilenet_v3_small |  分类  | 支持 | 混合精度 | PYTHON 服务化部署<br>Paddle2ONNX 部署| PACT量化<br>离线量化 | Windows GPU/CPU |
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/common_func.sh b/tutorials/mobilenetv3_prod/Step6/test_tipc/common_func.sh
index 9fe74f0d7728f4cec7ce6ab855d577834854c3fa..8a3415430759a78c840614d6421a3750c6ef643a 100644
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/common_func.sh
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/common_func.sh
@@ -65,10 +65,11 @@ function status_check(){
     last_status=$1   # the exit code
     run_command=$2
     run_log=$3
+    model_name=$4
     if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+        echo -e "\033[33m Run successfully with command - ${model_name} - ${run_command}!  \033[0m" | tee -a ${run_log}
     else
-        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+        echo -e "\033[33m Run failed with command - ${model_name} - ${run_command}!  \033[0m" | tee -a ${run_log}
     fi
 }
 
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/inference_cpp.txt b/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/inference_cpp.txt
index d7eafc9cf85e8b01c9342c2c3aa10c5bead2aa6f..cc4cc848f422520e7ae84128e9978ba1fc6f24d9 100755
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/inference_cpp.txt
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/inference_cpp.txt
@@ -1,14 +1,12 @@
 # model load config
+model_name mobilenet_v3_small
 use_gpu  0
 gpu_id  0
 gpu_mem  4000
 cpu_math_library_num_threads  10
-use_mkldnn 1
-use_tensorrt 0
-use_fp16 0
 
 # cls config
 cls_model_path  ./deploy/inference_cpp/mobilenet_v3_small_infer/inference.pdmodel
 cls_params_path ./deploy/inference_cpp/mobilenet_v3_small_infer/inference.pdiparams
 resize_short_size 256
-crop_size 224
+crop_size 224
\ No newline at end of file
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/prepare.sh b/tutorials/mobilenetv3_prod/Step6/test_tipc/prepare.sh
index 4ea8bb9725cae4120293757b9b9b462f6ec420ae..0dd45279b5b730e442b3599e8cda2eca57129a78 100644
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/prepare.sh
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/prepare.sh
@@ -18,7 +18,7 @@ model_name=$(func_parser_value "${lines[1]}")
 
 trainer_list=$(func_parser_value "${lines[12]}")
 
-
+pip install -r requirements.txt
 if [ ${MODE} = "lite_train_lite_infer" ];then
     # prepare lite data
     tar -xf ./test_images/lite_data.tar
@@ -54,6 +54,55 @@ elif [ ${MODE} = "serving_infer" ];then
         wget -nc -P  ./inference https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_infer.tar  --no-check-certificate
         cd ./inference && tar xf mobilenet_v3_small_infer.tar && cd ../
     fi
+elif [ ${MODE} = "cpp_infer" ];then
+    PADDLEInfer=$3
+    # wget model
+    wget -nc -P  ./deploy/inference_cpp/ https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_infer.tar  --no-check-certificate
+    cd ./deploy/inference_cpp/ && tar xf mobilenet_v3_small_infer.tar
+    if [ "" = "$PADDLEInfer" ];then
+        wget -nc https://paddle-inference-lib.bj.bcebos.com/2.2.2/cxx_c/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda11.1_cudnn8.1.1_trt7.2.3.4/paddle_inference.tgz --no-check-certificate
+    else
+        wget -nc $PADDLEInfer --no-check-certificate
+    fi
+    tar zxf paddle_inference.tgz
+    if [ ! -d "paddle_inference" ]; then
+        ln -s paddle_inference_install_dir paddle_inference
+    fi
+    wget -nc https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz --no-check-certificate
+    tar zxf opencv-3.4.7.tar.gz
+    # build opencv
+    cd opencv-3.4.7/
+    root_path=$PWD
+    install_path=${root_path}/opencv3
+    build_dir=${root_path}/build
+
+    rm -rf ${build_dir}
+    mkdir ${build_dir}
+    cd ${build_dir}
+
+    cmake .. \
+    -DCMAKE_INSTALL_PREFIX=${install_path} \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DBUILD_SHARED_LIBS=OFF \
+    -DWITH_IPP=OFF \
+    -DBUILD_IPP_IW=OFF \
+    -DWITH_LAPACK=OFF \
+    -DWITH_EIGEN=OFF \
+    -DCMAKE_INSTALL_LIBDIR=lib64 \
+    -DWITH_ZLIB=ON \
+    -DBUILD_ZLIB=ON \
+    -DWITH_JPEG=ON \
+    -DBUILD_JPEG=ON \
+    -DWITH_PNG=ON \
+    -DBUILD_PNG=ON \
+    -DWITH_TIFF=ON \
+    -DBUILD_TIFF=ON
+    make -j
+    make install
+    cd ../../
+    # build cpp
+    bash tools/build.sh
+
 elif [ ${MODE} = "paddle2onnx_infer" ];then
     # get data
     tar -xf ./test_images/lite_data.tar
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/test_inference_cpp.sh b/tutorials/mobilenetv3_prod/Step6/test_tipc/test_inference_cpp.sh
index ad60905753b7faa60a185a63548a245aade6d841..0df3fbbdbb656c5cb7362838da642f963c7e4381 100644
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/test_inference_cpp.sh
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/test_inference_cpp.sh
@@ -28,14 +28,9 @@ IFS=$'\n'
 lines=(${dataline})
 
 # parser load config
-use_gpu_key=$(func_parser_key_cpp "${lines[1]}")
-use_gpu_value=$(func_parser_value_cpp "${lines[1]}")
-use_mkldnn_key=$(func_parser_key_cpp "${lines[5]}")
-use_mkldnn_value=$(func_parser_value_cpp "${lines[5]}")
-use_tensorrt_key=$(func_parser_key_cpp "${lines[6]}")
-use_tensorrt_value=$(func_parser_value_cpp "${lines[6]}")
-use_fp16_key=$(func_parser_key_cpp "${lines[7]}")
-use_fp16_value=$(func_parser_value_cpp "${lines[7]}")
+model_name=$(func_parser_value_cpp "${lines[1]}")
+use_gpu_key=$(func_parser_key_cpp "${lines[2]}")
+use_gpu_value=$(func_parser_value_cpp "${lines[2]}")
 
 LOG_PATH="./log/infer_cpp"
 mkdir -p ${LOG_PATH}
@@ -43,22 +38,10 @@ status_log="${LOG_PATH}/results_infer_cpp.log"
 
 function func_infer_cpp(){
     # inference cpp
-    if test $use_gpu_value -gt 0; then
-        if test $use_tensorrt_value -gt 0; then
-            if test $use_fp16_value -gt 0; then
-                _save_log_path="${LOG_PATH}/infer_cpp_${use_gpu_key}_${use_tensorrt_key}_${use_fp16_key}.log"
-            else
-                _save_log_path="${LOG_PATH}/infer_cpp_${use_gpu_key}_${use_tensorrt_key}.log"
-            fi
-        else
-            _save_log_path="${LOG_PATH}/infer_cpp_${use_gpu_key}.log"
-        fi
+    if test $use_gpu_value -gt 0; then     
+        _save_log_path="${LOG_PATH}/infer_cpp_use_cpu.log" 
     else
-        if test $use_mkldnn_value -gt 0; then
-            _save_log_path="${LOG_PATH}/infer_cpp_use_cpu_${use_mkldnn_key}.log"
-        else
-            _save_log_path="${LOG_PATH}/infer_cpp_use_cpu.log"
-        fi    
+        _save_log_path="${LOG_PATH}/infer_cpp_${use_gpu_key}.log"
     fi
     # run infer cpp
     inference_cpp_cmd="./deploy/inference_cpp/build/clas_system"
@@ -66,7 +49,7 @@ function func_infer_cpp(){
     infer_cpp_full_cmd="${inference_cpp_cmd} ${FILENAME} ${inference_cpp_img} > ${_save_log_path} 2>&1 "   
     eval $infer_cpp_full_cmd
     last_status=${PIPESTATUS[0]}
-    status_check $last_status "${infer_cpp_full_cmd}" "${status_log}"
+    status_check $last_status "${infer_cpp_full_cmd}" "${status_log}"  "${model_name}"
 }
 
 echo "################### run test cpp inference ###################"
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/test_ptq_inference_python.sh b/tutorials/mobilenetv3_prod/Step6/test_tipc/test_ptq_inference_python.sh
index 3fd84c70fe8c4d075b0406f37454ebac430d3428..c17fdd72eb4366941a6d61b09eb248271f849f28 100644
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/test_ptq_inference_python.sh
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/test_ptq_inference_python.sh
@@ -65,7 +65,7 @@ function func_inference(){
                 eval $command
                 last_status=${PIPESTATUS[0]}
                 eval "cat ${_save_log_path}"
-                status_check $last_status "${command}" "${status_log}"
+                status_check $last_status "${command}" "${status_log}" "${model_name}"
             done
         # gpu        
         elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
@@ -80,7 +80,7 @@ function func_inference(){
                 eval $command
                 last_status=${PIPESTATUS[0]}
                 eval "cat ${_save_log_path}"
-                status_check $last_status "${command}" "${status_log}"   
+                status_check $last_status "${command}" "${status_log}"  "${model_name}" 
             done      
         else
             echo "Does not support hardware other than CPU and GPU Currently!"
@@ -107,7 +107,7 @@ if [ ${MODE} = "whole_infer" ]; then
     echo $export_cmd
     # eval $export_cmd
     status_export=$?
-    status_check $status_export "${export_cmd}" "${status_log}"
+    status_check $status_export "${export_cmd}" "${status_log}" "${model_name}"
     
     save_infer_dir=${output_dir_value}
     #run inference
diff --git a/tutorials/mobilenetv3_prod/Step6/test_tipc/test_train_inference_python.sh b/tutorials/mobilenetv3_prod/Step6/test_tipc/test_train_inference_python.sh
index 9f0c83a2cc15082664cc5217d5799deb95597522..ef6b5ced493b17f15bb8f2c9ece8ba4c3ee82695 100644
--- a/tutorials/mobilenetv3_prod/Step6/test_tipc/test_train_inference_python.sh
+++ b/tutorials/mobilenetv3_prod/Step6/test_tipc/test_train_inference_python.sh
@@ -87,7 +87,7 @@ function func_inference(){
                 eval $command
                 last_status=${PIPESTATUS[0]}
                 eval "cat ${_save_log_path}"
-                status_check $last_status "${command}" "${status_log}"
+                status_check $last_status "${command}" "${status_log}" "${model_name}"
             done
         # gpu        
         elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
@@ -101,7 +101,7 @@ function func_inference(){
                 eval $command
                 last_status=${PIPESTATUS[0]}
                 eval "cat ${_save_log_path}"
-                status_check $last_status "${command}" "${status_log}"   
+                status_check $last_status "${command}" "${status_log}"  "${model_name}"
             done      
         else
             echo "Does not support hardware other than CPU and GPU Currently!"
@@ -131,7 +131,7 @@ if [ ${MODE} = "whole_infer" ]; then
         echo $export_cmd
         eval $export_cmd
         status_export=$?
-        status_check $status_export "${export_cmd}" "${status_log}"
+        status_check $status_export "${export_cmd}" "${status_log}" "${model_name}"
     else
         save_infer_dir=${save_infer_dir}
     fi
@@ -196,7 +196,7 @@ else
             fi
             # run train
             eval $cmd
-            status_check $? "${cmd}" "${status_log}"
+            status_check $? "${cmd}" "${status_log}" "${model_name}"
             # TODO ensure model name
             set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
 
@@ -205,7 +205,7 @@ else
                 eval ${env}
                 eval_cmd="${python} ${eval_py} ${set_eval_pretrain}" 
                 eval $eval_cmd
-                status_check $? "${eval_cmd}" "${status_log}"
+                status_check $? "${eval_cmd}" "${status_log}" "${model_name}"
             fi
             # run export model
             if [ ${run_export} != "null" ]; then 
@@ -215,7 +215,7 @@ else
                 set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
                 export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
                 eval $export_cmd
-                status_check $? "${export_cmd}" "${status_log}"
+                status_check $? "${export_cmd}" "${status_log}" "${model_name}"
 
                 #run inference
                 eval $env
diff --git a/tutorials/tipc/infer_cpp/infer_cpp.md b/tutorials/tipc/infer_cpp/infer_cpp.md
index a0fe25043f135cb738929844769a4ce2ef5f442c..38d50416885f8ebcf55befe7e1a775e60c148867 100644
--- a/tutorials/tipc/infer_cpp/infer_cpp.md
+++ b/tutorials/tipc/infer_cpp/infer_cpp.md
@@ -71,20 +71,8 @@ void Classifier::LoadModel(const std::string &model_path,
 
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
-    if (this->use_tensorrt_) {
-      config.EnableTensorRtEngine(
-          1 << 20, 1, 3,
-          this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
-                          : paddle_infer::Config::Precision::kFloat32,
-          false, false);
-    }
   } else {
     config.DisableGpu();
-    if (this->use_mkldnn_) {
-      config.EnableMKLDNN();
-      // cache 10 different shapes for mkldnn to avoid memory leak
-      config.SetMkldnnCacheCapacity(10);
-    }
     config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
   }
 
diff --git a/tutorials/tipc/infer_cpp/test_infer_cpp.md b/tutorials/tipc/infer_cpp/test_infer_cpp.md
index e66544b768360e571d5ad95ae958d85a0b1cb8c9..54b00438700f19dac0120127537571ca200c5ffc 100644
--- a/tutorials/tipc/infer_cpp/test_infer_cpp.md
+++ b/tutorials/tipc/infer_cpp/test_infer_cpp.md
@@ -21,7 +21,7 @@
 
 ## 1. 简介
 
-Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。相比于直接基于预训练模型进行预测，Paddle Inference可使用MKLDNN、CUDNN、TensorRT进行预测加速，从而实现更优的推理性能。
+Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。
 更多关于Paddle Inference推理引擎的介绍，可以参考[Paddle Inference官网教程](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/inference/inference_cn.html)。
 本文档主要介绍飞桨模型在 Linux GPU/CPU 下基于C++预测引擎的推理过程开发。
 
@@ -39,15 +39,15 @@ run_scripts configs_path img_path
 ```
 
 * `run_scripts`：最终编译好的可执行命令。
-* `configs_path`：设置模型路径、是否使用GPU、是否开启mkldnn、是否开启TensorRT等。
+* `configs_path`：设置模型路径、是否使用GPU等。
 * `img_path`：待预测的图像路径。
 
 <a name="2.2"></a>
 
 ### 2.2 配置文件解析
-完整的`inference_cpp.txt`配置文件共有14行，包含两个方面的内容。
-* 运行环境参数配置：第1~8行
-* 模型参数配置：第10~14行
+完整的`inference_cpp.txt`配置文件共有12行，包含两个方面的内容。
+* 运行环境参数配置：第1~5行
+* 模型参数配置：第7~11行
 
 具体内容见[inference_cpp.txt](../../mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/inference_cpp.txt)
 
@@ -62,17 +62,15 @@ run_scripts configs_path img_path
 
 | 行号 | 参考内容                                | 含义            | key是否需要修改 | value是否需要修改 | 修改内容                             |
 |----|-------------------------------------|---------------|-----------|-------------|----------------------------------|
-| 2  | use_gpu      | 是否使用GPU    | 否         | 是           | value根据是否使用GPU进行修改               |
-| 3  | gpu_id       | 使用的GPU卡号  | 否         | 是           | value修改为自己的GPU ID              |
-| 4  | gpu_mem      | 显存          | 否         | 是           | value修改为自己的GPU 显存             |
-| 5  | cpu_math_library_num_thread | 底层科学计算库所用线程的数量  | 否      | 是           | value修改为合适的线程数         |
-| 6  | use_mkldnn   | 是否使用MKLDNN加速    | 否        | 是          | value根据是否使用MKLDNN进行修改          |
-| 7  | use_tensorrt | 是否使用tensorRT进行加速          | 否         | 是           | value根据是否使用tensorRT进行修改             |
-| 8  | use_fp16 | 是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效 | 否         | 是          | value根据在开启tensorRT时是否使用半精度进行修改|
-| 11 | cls_model_path  | 预测模型结构文件路径         | 否         | 是           | value修改为预测模型结构文件路径 |
-| 12 | cls_params_path | 预测模型参数文件路径  | 否         | 是           | vvalue修改为预测模型参数文件路径 |
-| 13 | resize_short_size  | 预处理时图像缩放大小         | 否         | 是           | value修改为预处理时图像缩放大小  
-| 14 | crop_size          | 预处理时图像裁剪后的大小      | 否         | 是           | value修改为预处理时图像裁剪后的大小  
+| 2  | model_name      | 模型名称    | 否         | 是           | value根据模型名称修改               |
+| 3  | use_gpu      | 是否使用GPU    | 否         | 是           | value根据是否使用GPU进行修改               |
+| 4  | gpu_id       | 使用的GPU卡号  | 否         | 是           | value修改为自己的GPU ID              |
+| 5  | gpu_mem      | 显存          | 否         | 是           | value修改为自己的GPU 显存             |
+| 6  | cpu_math_library_num_thread | 底层科学计算库所用线程的数量  | 否      | 是           | value修改为合适的线程数         |
+| 9 | cls_model_path  | 预测模型结构文件路径         | 否         | 是           | value修改为预测模型结构文件路径 |
+| 10 | cls_params_path | 预测模型参数文件路径  | 否         | 是           | vvalue修改为预测模型参数文件路径 |
+| 11 | resize_short_size  | 预处理时图像缩放大小         | 否         | 是           | value修改为预处理时图像缩放大小  
+| 12 | crop_size          | 预处理时图像裁剪后的大小      | 否         | 是           | value修改为预处理时图像裁剪后的大小  
 
 
 </details>
@@ -104,172 +102,7 @@ run_scripts configs_path img_path
 <a name="3.3"></a>
 
 ### 3.3 准备推理所需代码
-基于预测引擎的推理过程包含4个步骤：初始化预测引擎、预处理、推理、后处理。
-
-#### 3.3.1 初始化预测引擎
-
-**【基本内容】**
-该部分主要根据配置文件对预测引擎进行初始化，包括设置模型结构和参数文件路径、是否使用GPU、是否开启MKLDNN、是否开启TensorRT等。
-**【实战】**
-以mobilenet_v3_small模型为例，推理引擎初始化函数实现如下，其中模型结构和参数文件路径、是否使用GPU、是否开启MKLDNN等内容都是可以配置的。
-主要实现在[cls.cpp](../../mobilenetv3_prod/Step6/deploy/inference_cpp/src/cls.cpp)
-```c++
-void Classifier::LoadModel(const std::string &model_path,
-                           const std::string &params_path) {
-  paddle_infer::Config config;
-  config.SetModel(model_path, params_path);
-
-  if (this->use_gpu_) {
-    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
-    if (this->use_tensorrt_) {
-      config.EnableTensorRtEngine(
-          1 << 20, 1, 3,
-          this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
-                          : paddle_infer::Config::Precision::kFloat32,
-          false, false);
-    }
-  } else {
-    config.DisableGpu();
-    if (this->use_mkldnn_) {
-      config.EnableMKLDNN();
-      // cache 10 different shapes for mkldnn to avoid memory leak
-      config.SetMkldnnCacheCapacity(10);
-    }
-    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
-  }
-
-  config.SwitchUseFeedFetchOps(false);
-  // true for multiple input
-  config.SwitchSpecifyInputNames(true);
-
-  config.SwitchIrOptim(true);
-
-  config.EnableMemoryOptim();
-  config.DisableGlogInfo();
-
-  this->predictor_ = CreatePredictor(config);
-}
-
-```
-#### 3.3.2 预处理
-**【基本内容】**
-该部分主要用来读取指定图像，对其进行数据变换，转化为符合模型推理所需要的输入格式,
-**【实战】**
-以mobilenet_v3_small模型为例，使用的数据预处理如下:
-
-* resize
-* crop
-* normalize
-* RGB -> CHW
-
-主要实现在[preprocess_op.cpp](../../mobilenetv3_prod/Step6/deploy/inference_cpp/src/preprocess_op.cpp)中。
-```c++
-//Resize
-class ResizeImg {
-public:
-  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
-};
-//Crop
-class CenterCropImg {
-public:
-  virtual void Run(cv::Mat &im, const int crop_size = 224);
-};
-//Norm
-class Normalize {
-public:
-  virtual void Run(cv::Mat *im, const std::vector<float> &mean,
-                   const std::vector<float> &scale, const bool is_scale = true);
-};
-// RGB -> CHW
-class Permute {
-public:
-  virtual void Run(const cv::Mat *im, float *data);
-};
-```
-#### 3.3.3 推理
-**【基本内容】**
-前向推理是主要步骤，会将预处理好的输入图像输出到预测引擎中，得到输出结果。
-**【实战】**
-以mobilenet_v3_small模型为例，前向推理主要实现在[cls.cpp](../../mobilenetv3_prod/Step6/deploy/inference_cpp/src/cls.cpp)。
-```C++
-  auto input_names = this->predictor_->GetInputNames();
-  auto input_t = this->predictor_->GetInputHandle(input_names[0]);
-  input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
-  auto start = std::chrono::system_clock::now();
-  input_t->CopyFromCpu(input.data());
-  this->predictor_->Run();
-
-  std::vector<float> out_data;
-  auto output_names = this->predictor_->GetOutputNames();
-  auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
-  std::vector<int> output_shape = output_t->shape();
-  int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
-                                std::multiplies<int>());  
-  out_data.resize(out_num);
-  output_t->CopyToCpu(out_data.data());
-```
-
-#### 3.3.4 后处理
-**【基本内容】**
-模型最后的输出可能是数组，一般并不是我们最后想要获取的结果，因此需要对模型的输出做后处理。
-**【实战】**
-以mobilenet_v3_small模型为例，模型输出的是一个一维的数组，代表输入图片分类到每个类目的概率，为了得到有实际含义的输出，需要获取该数组中最大值的位置和大小，mobilenet_v3_small的后处理代码如下所示。
-
-```c++
-int maxPosition = max_element(out_data.begin(), out_data.end()) - out_data.begin();
-int score = out_data[maxPosition];
-```
-<a name="3.4"></a>
-
-### 3.4 编译得到可执行代码
-**【基本内容】**
-在准备好相应的代码后需要开始准备编译，这里可以利用cmake来实现。
-**【实战】**
-以mobilenet_v3_small模型为例，代码示例如：[CMakeLists.txt](../../mobilenetv3_prod/Step6/deploy/inference_cpp/CMakeLists.txt)
-```bash
-set(DEPS ${DEPS} ${OpenCV_LIBS})
-AUX_SOURCE_DIRECTORY(./src SRCS)
-add_executable(${DEMO_NAME} ${SRCS})
-target_link_libraries(${DEMO_NAME} ${DEPS})
-```
-执行脚本：
-```bash
-OPENCV_DIR=../opencv-3.4.7/opencv3/
-LIB_DIR=../paddle_inference/
-CUDA_LIB_DIR=/usr/local/cuda/lib64
-CUDNN_LIB_DIR=/usr/lib64
-TENSORRT_DIR=/usr/local/TensorRT-7.2.3.4
-
-BUILD_DIR=build
-rm -rf ${BUILD_DIR}
-mkdir ${BUILD_DIR}
-cd ${BUILD_DIR}
-cmake .. \
-    -DPADDLE_LIB=${LIB_DIR} \
-    -DWITH_MKL=ON \
-    -DWITH_GPU=OFF \
-    -DWITH_STATIC_LIB=OFF \
-    -DUSE_TENSORRT=OFF \
-    -DOPENCV_DIR=${OPENCV_DIR} \
-    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
-    -DCUDA_LIB=${CUDA_LIB_DIR} \
-
-make -j
-```
-
-上述命令中，Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
-
-* `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）；
-
-* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
-
-* `CUDA_LIB_DIR`为cuda库文件地址，在docker中一般为`/usr/local/cuda/lib64`；
-
-* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中一般为`/usr/lib64`。
-
-* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中一般为`/usr/local/TensorRT-7.2.3.4/`，TensorRT需要结合GPU使用。
-
-在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
+基于预测引擎的推理过程包含4个步骤：初始化预测引擎、预处理、推理、后处理。参考[文档](./infer_cpp.md)准备预测引擎推理代码并编译成功。
 
 <a name="3.5"></a>
 
@@ -299,10 +132,11 @@ mobilenet_v3_small的测试开发配置文件可以参考：[inference_cpp.txt](
 
 **【基本内容】**
 
-基于修改完的配置，运行
+基于修改完的配置，运行方法如下
 
 ```bash
-bash test_tipc/test_inference_cpp.sh ${your_params_file}
+bash test_tipc/prepare.sh  ${your_params_file}   cpp_infer
+bash test_tipc/test_inference_cpp.sh  ${your_params_file} 
 ```
 
 **【注意事项】**
@@ -310,7 +144,7 @@ bash test_tipc/test_inference_cpp.sh ${your_params_file}
 如果运行失败，会输出具体的报错命令，可以根据输出的报错命令排查下配置文件的问题并修改，示例报错如下所示。
 
 ```
-Run failed with command - ./deploy/inference_cpp/build/clas_system test_tipc/configs/mobilenet_v3_small/inference_cpp.txt ./images/demo.jpg > ./log/infer_cpp/infer_cpp_use_cpu_use_mkldnn.log 2>&1 !
+Run failed with command - ./deploy/inference_cpp/build/clas_system test_tipc/configs/mobilenet_v3_small/inference_cpp.txt ./images/demo.jpg > ./log/infer_cpp/infer_cpp_use_cpu.log 2>&1 !
 ```
 
 **【实战】**
@@ -318,16 +152,17 @@ Run failed with command - ./deploy/inference_cpp/build/clas_system test_tipc/con
 以mobilenet_v3_small的`Linux GPU/CPU C++推理功能测试` 为例，命令如下所示。
 
 ```bash
+bash test_tipc/prepare.sh test_tipc/configs/mobilenet_v3_small/inference_cpp.txt cpp_infer
 bash test_tipc/test_inference_cpp.sh test_tipc/configs/mobilenet_v3_small/inference_cpp.txt
 ```
 
 输出结果如下，表示命令运行成功。
 
 ```bash
-Run successfully with command - ./deploy/inference_cpp/build/clas_system test_tipc/configs/mobilenet_v3_small/inference_cpp.txt ./images/demo.jpg > ./log/infer_cpp/infer_cpp_use_cpu_use_mkldnn.log 2>&1 !
+Run successfully with command - mobilenet_v3_small - ./deploy/inference_cpp/build/clas_system test_tipc/configs/mobilenet_v3_small/inference_cpp.txt ./images/demo.jpg > ./log/infer_cpp/infer_cpp_use_cpu.log 2>&1 !
 ```
 
-也可以在`./log/infer_cpp/infer_cpp_use_cpu_use_mkldnn.log`中查看详细的输出结果。
+也可以在`./log/infer_cpp/infer_cpp_use_cpu.log`中查看详细的输出结果。
 
 **【核验】**
 
diff --git a/tutorials/tipc/train_infer_python/infer_python.md b/tutorials/tipc/train_infer_python/infer_python.md
index 7e2cba75594f0c8d781d2fb417c797e543c38618..73e324d3d2a46dc129d8dde6245f960bab0cae97 100644
--- a/tutorials/tipc/train_infer_python/infer_python.md
+++ b/tutorials/tipc/train_infer_python/infer_python.md
@@ -201,21 +201,10 @@ class InferenceEngine(object):
         config.enable_memory_optim()
         if args.use_gpu:
             config.enable_use_gpu(100, 0)
-            config.enable_tensorrt_engine(workspace_size=1 << 30,
-                                      max_batch_size=10,
-                                      min_subgraph_size=5,
-                                      precision_mode=PrecisionType.Float32,
-                                      use_static=False,
-                                      use_calib_mode=False)
-            config.set_trt_dynamic_shape_info(
-                                      min_input_shape={"input": [1, 3, 1, 1]},
-                                      max_input_shape={"input": [10, 3, 1200, 1200]},
-                                      optim_input_shape={"input": [1, 3, 224, 224]})
         else:
-            # If not specific mkldnn, you can set the blas thread.
+            config.disable_gpu()
             # The thread num should not be greater than the number of cores in the CPU.
             config.set_cpu_math_library_num_threads(4)
-            config.enable_mkldnn()
         # creat predictor
         predictor = create_predictor(config)
         # get input and output tensor property
diff --git a/tutorials/tipc/train_infer_python/test_train_infer_python.md b/tutorials/tipc/train_infer_python/test_train_infer_python.md
index 0253e9f81fdfad16935f4ed4fe12bbd6c95c82eb..d4ba616bb04e3117e7bfab38b6c66856950ae456 100644
--- a/tutorials/tipc/train_infer_python/test_train_infer_python.md
+++ b/tutorials/tipc/train_infer_python/test_train_infer_python.md
@@ -214,15 +214,37 @@ python deploy/inference_python/infer.py --model-dir=./mobilenet_v3_small_infer/
 
 <a name="3.2"></a>
 
-### 3.2 准备数据与环境
+### 3.2 准备数据与环境与规范训练日志
 
 **【基本内容】**
 
-1. 数据集：为方便快速验证训练/评估/推理过程，需要准备一个小数据集（训练集和验证集各8~16张图像即可，压缩后数据大小建议在`20M`以内），放在`lite_data`文件夹下。
+1. 数据集：为方便快速验证训练/评估/推理过程，需要准备一个小数据集（训练集和验证集各8~16张图像即可，压缩后数据大小建议在`20M`以内，确保基础训练推理总时间不超过十分钟），放在`lite_data`文件夹下。
 
     相关文档可以参考[论文复现赛指南3.2章节](../../../docs/lwfx/ArticleReproduction_CV.md)，代码可以参考`基于ImageNet准备小数据集的脚本`：[prepare.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/tipc/pipeline/Step2/prepare.py)。
 
-2. 环境：安装好PaddlePaddle即可进行基础训练推理测试开发
+2. 规范训练日志格式：训练日志中，除了打印loss、精度等信息，还需要有以下信息：
+
+- reader_cost：1个Step数据加载用时，单位：秒(sec)。
+	1). N个Step打印1条日志时，reader_cost为N个Step数据加载用时的平均值
+	2). 建议(但不强制)使用DataLoader，而非DataFeeder
+- batch_cost：1个Step训练用时，单位：秒(sec)。batch_cost = reader_cost + model.forward()（训练）时间。
+- ips：单卡每秒处理的样本数，单位如：images/sec、sequences/sec、tokens/sec、words/sec、frames/sec等。
+- samples： samples代表上次打印到本次打印，新完成训练的样本数量。对于每个Step样本数可能不同的模型（大多为NLP模型），需要计算samples。
+
+    最终打印期望格式如下：
+    ```
+    ..., ... , loss: 0.12345, avg_reader_cost: 0.12345 sec, avg_batch_cost: 0.12345 sec, avg_samples: 100, avg_ips: 0.12345 images/sec
+    ```
+    - avg_reader_cost、avg_batch_cost、avg_samples、avg_ips算法如下：
+    假如，模型每N个Step打印1次日志，每Step reader用时为：`R1, R2,...Rn`，每Step训练用时：`T1, T2,...Tn`，每Step **单卡BatchSize** 为`S1, S2,...Sn`。
+    `avg_reader_cost = sum(R1, R2,...Rn) / N`
+    `avg_batch_cost = avg_reader_cost + sum(T1, T2,...Tn) / N`
+    `avg_samples = sum(S1, S2,...Sn) / N`
+    `avg_ips = samples / batch_cost `
+    ips 单位如：images/sec、sequences/sec、tokens/sec、words/sec、frames/sec等。
+
+
+3. 环境：安装好PaddlePaddle即可进行基础训练推理测试开发
 
 **【注意事项】**
 
@@ -258,6 +280,9 @@ python deploy/inference_python/infer.py --model-dir=./mobilenet_v3_small_infer/
 
 mobilenet_v3_small的测试开发配置文件可以参考：[train_infer_python.txt](../../mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_infer_python.txt)。
 
+为了确保基础训练、推理总时间不超过10分钟，需要在`train_infer_python.txt`中设置较小的`epoch`数或者迭代次数。以[train_infer_python.txt](../../mobilenetv3_prod/Step6/test_tipc/configs/mobilenet_v3_small/train_infer_python.txt)为例，设置`--epochs:lite_train_lite_infer=5`，数值5表示训练5个epoch，不同模型根据训练耗时调整该数值，确保运行耗时不超过10分钟。
+
+
 <a name="3.5"></a>
 
 ### 3.5 验证配置正确性