diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index 8437b2b21942ead544dab8636db1b355b7cf7bd5..02ca8a45a851d262eed6962a9a227b5009ef03a5 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -8,27 +8,6 @@ cc_library(paddle_fluid_api
 # Merge all modules into a simgle static library
 cc_library(paddle_fluid DEPS paddle_fluid_api ${FLUID_CORE_MODULES})
 
-# ptools
-# just for testing, we may need to change the storing format for inference_model
-# and move the dependent of pickle.
-# download from http://www.picklingtools.com/
-# build in the C++ sub-directory, using command
-#     make -f Makefile.Linux libptools.so
-set(PTOOLS_LIB)
-set(PTOOLS_ROOT $ENV{PTOOLS_ROOT} CACHE PATH "Folder contains PicklingTools")
-find_path(PTOOLS_INC_DIR chooseser.h PATHS ${PTOOLS_ROOT}/C++)
-find_library(PTOOLS_SHARED_LIB NAMES ptools PATHS ${PTOOLS_ROOT}/C++)
-if(PTOOLS_INC_DIR AND PTOOLS_SHARED_LIB)
-  add_definitions(-DPADDLE_USE_PTOOLS)
-  set(PTOOLS_LIB ptools)
-  message(STATUS "Found PicklingTools: ${PTOOLS_SHARED_LIB}")
-  add_library(${PTOOLS_LIB} SHARED IMPORTED GLOBAL)
-  set_property(TARGET ${PTOOLS_LIB} PROPERTY IMPORTED_LOCATION ${PTOOLS_SHARED_LIB})
-  include_directories(${PTOOLS_ROOT}/C++)
-  include_directories(${PTOOLS_ROOT}/C++/opencontainers_1_8_5/include)
-  add_definitions(-DOC_NEW_STYLE_INCLUDES) # used in ptools
-endif()
-
 add_executable(example example.cc)
 if(APPLE)
   set(OPTIONAL_LINK_FLAGS)
diff --git a/paddle/inference/example.cc b/paddle/inference/example.cc
index 9711b20e6fb4099a2cc497029468ebd1fd0b3456..0c18b45624dedcb5839d4b771e044b4a7b32af52 100644
--- a/paddle/inference/example.cc
+++ b/paddle/inference/example.cc
@@ -18,33 +18,21 @@ limitations under the License. */
 #include "paddle/inference/inference.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
-DEFINE_string(feed_var_names, "", "Names of feeding variables");
-DEFINE_string(fetch_var_names, "", "Names of fetching variables");
 
 int main(int argc, char** argv) {
   google::ParseCommandLineFlags(&argc, &argv, true);
-  if (FLAGS_dirname.empty() || FLAGS_feed_var_names.empty() ||
-      FLAGS_fetch_var_names.empty()) {
+  if (FLAGS_dirname.empty()) {
     // Example:
     //   ./example --dirname=recognize_digits_mlp.inference.model
-    //             --feed_var_names="x"
-    //             --fetch_var_names="fc_2.tmp_2"
-    std::cout << "Usage: ./example --dirname=path/to/your/model "
-                 "--feed_var_names=x --fetch_var_names=y"
-              << std::endl;
+    std::cout << "Usage: ./example --dirname=path/to/your/model" << std::endl;
     exit(1);
   }
 
   std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
-  std::cout << "FLAGS_feed_var_names: " << FLAGS_feed_var_names << std::endl;
-  std::cout << "FLAGS_fetch_var_names: " << FLAGS_fetch_var_names << std::endl;
-
   std::string dirname = FLAGS_dirname;
-  std::vector<std::string> feed_var_names = {FLAGS_feed_var_names};
-  std::vector<std::string> fetch_var_names = {FLAGS_fetch_var_names};
 
   paddle::InferenceEngine* engine = new paddle::InferenceEngine();
-  engine->LoadInferenceModel(dirname, feed_var_names, fetch_var_names);
+  engine->LoadInferenceModel(dirname);
 
   paddle::framework::LoDTensor input;
   srand(time(0));
diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
index 37b8b20ddfcf2566b8410f950308309e5b2b2a7c..49001778808173b82865a4b6632a6b175ef96242 100644
--- a/paddle/inference/inference.cc
+++ b/paddle/inference/inference.cc
@@ -25,19 +25,37 @@ limitations under the License. */
 
 namespace paddle {
 
+void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
+  std::string model_filename = dirname + "/__model__.dat";
+  LOG(INFO) << "loading model from " << model_filename;
+  std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
+  std::string program_desc_str;
+  inputfs.seekg(0, std::ios::end);
+  program_desc_str.resize(inputfs.tellg());
+  inputfs.seekg(0, std::ios::beg);
+  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
+  inputfs.read(&program_desc_str[0], program_desc_str.size());
+  inputfs.close();
+
+  program_ = new framework::ProgramDesc(program_desc_str);
+  GenerateLoadProgram(dirname);
+
+  framework::BlockDesc* global_block = program_->MutableBlock(0);
+  feed_var_names_.clear();
+  fetch_var_names_.clear();
+  for (auto* op : global_block->AllOps()) {
+    if (op->Type() == "feed") {
+      feed_var_names_.insert(feed_var_names_.begin(), op->Output("Out")[0]);
+    } else if (op->Type() == "fetch") {
+      fetch_var_names_.push_back(op->Input("X")[0]);
+    }
+  }
+}
+
 void InferenceEngine::LoadInferenceModel(
     const std::string& dirname,
     const std::vector<std::string>& feed_var_names,
     const std::vector<std::string>& fetch_var_names) {
-#ifdef PADDLE_USE_PTOOLS
-  std::string model_filename = dirname + "/__model__";
-  LOG(INFO) << "Using PicklingTools, loading model from " << model_filename;
-  Val v;
-  LoadValFromFile(model_filename.c_str(), v, SERIALIZE_P0);
-  std::string program_desc_str = v["program_desc_str"];
-  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
-// PicklingTools cannot parse the vector of strings correctly.
-#else
   std::string model_filename = dirname + "/__model__.dat";
   LOG(INFO) << "loading model from " << model_filename;
   std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
@@ -48,7 +66,7 @@ void InferenceEngine::LoadInferenceModel(
   LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
   inputfs.read(&program_desc_str[0], program_desc_str.size());
   inputfs.close();
-#endif
+
   program_ = new framework::ProgramDesc(program_desc_str);
   GenerateLoadProgram(dirname);
 
@@ -62,7 +80,7 @@ void InferenceEngine::LoadInferenceModel(
 }
 
 bool InferenceEngine::IsParameter(const framework::VarDesc* var) {
-  if (var->Persistable()) {
+  if (var->Persistable() && var->Name() != "feed" && var->Name() != "fetch") {
     // There are many unreachable variables in the program
     for (size_t i = 0; i < program_->Size(); ++i) {
       const framework::BlockDesc& block = program_->Block(i);
diff --git a/paddle/inference/inference.h b/paddle/inference/inference.h
index a3f3ef4b440036a0b27353cc092eed1bbf96eeb3..7fc09cb9e539a65a8cd3cceb1543bc7d111c22b3 100644
--- a/paddle/inference/inference.h
+++ b/paddle/inference/inference.h
@@ -28,6 +28,7 @@ public:
     delete load_program_;
   }
 
+  void LoadInferenceModel(const std::string& dirname);
   void LoadInferenceModel(const std::string& dirname,
                           const std::vector<std::string>& feed_var_names,
                           const std::vector<std::string>& fetch_var_names);
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 499df05e592855f63f41ec8ceb939edf0e4d435c..e7a06a07145758335986368fbe111de2bb930246 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -15,6 +15,7 @@ import os
 import cPickle as pickle
 
 from paddle.v2.fluid.framework import Program, Parameter, default_main_program, Variable
+from . import core
 
 __all__ = [
     'save_vars',
@@ -191,6 +192,33 @@ def get_inference_program(target_vars, main_program=None):
     return inference_program
 
 
+def prepend_feed_ops(inference_program, feeded_var_names):
+    global_block = inference_program.global_block()
+    feed_var = global_block.create_var(
+        name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
+
+    for i, name in enumerate(feeded_var_names):
+        out = global_block.var(name)
+        global_block.prepend_op(
+            type='feed',
+            inputs={'X': [feed_var]},
+            outputs={'Out': [out]},
+            attrs={'col': i})
+
+
+def append_fetch_ops(inference_program, fetch_var_names):
+    global_block = inference_program.global_block()
+    fetch_var = global_block.create_var(
+        name='fetch', type=core.VarDesc.VarType.FETCH_LIST, persistable=True)
+
+    for i, name in enumerate(fetch_var_names):
+        global_block.append_op(
+            type='fetch',
+            inputs={'X': [name]},
+            outputs={'Out': [fetch_var]},
+            attrs={'col': i})
+
+
 def save_inference_model(dirname,
                          feeded_var_names,
                          target_vars,
@@ -241,6 +269,9 @@ def save_inference_model(dirname,
             "fetch_var_names": fetch_var_names
         }, f, -1)
 
+    prepend_feed_ops(inference_program, feeded_var_names)
+    append_fetch_ops(inference_program, fetch_var_names)
+
     # Save only programDesc of inference_program in binary format
     # in another file: __model__.dat
     with open(model_file_name + ".dat", "wb") as fp: