diff --git a/Jenkinsfile b/Jenkinsfile
index 9cc72c98fcb0..809a9b03872a 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -81,6 +81,7 @@ stage('Build') {
         init_git()
         def flag = """ \
 USE_PROFILER=1                \
+USE_CPP_PACKAGE=1             \
 USE_BLAS=openblas             \
 -j\$(nproc)
 """
@@ -99,6 +100,7 @@ USE_BLAS=openblas             \
 USE_CUDA=1                    \
 USE_CUDA_PATH=/usr/local/cuda \
 USE_CUDNN=1                   \
+USE_CPP_PACKAGE=1             \
 -j\$(nproc)
 """
         make('gpu', flag)
@@ -126,6 +128,7 @@ USE_MKL2017_EXPERIMENTAL=1    \
 USE_CUDA=1                    \
 USE_CUDA_PATH=/usr/local/cuda \
 USE_CUDNN=1                   \
+USE_CPP_PACKAGE=1             \
 -j\$(nproc)
 """
         make('mklml_gpu', flag)
diff --git a/cpp-package/example/charRNN.cpp b/cpp-package/example/charRNN.cpp
index daf31a4fe69c..4dd1ce7a5a51 100644
--- a/cpp-package/example/charRNN.cpp
+++ b/cpp-package/example/charRNN.cpp
@@ -115,7 +115,7 @@ Symbol LSTMUnroll(int num_lstm_layer, int sequence_length, int input_dim,
 
   auto label = Symbol::Variable("softmax_label");
   label = transpose(label);
-  label = Reshape(label, Shape(), false, false, Shape(-1));  // -1: infer from graph
+  label = Reshape(label, Shape(), false, Shape(-1), false);  // -1: infer from graph
   auto sm = SoftmaxOutput("softmax", pred, label);
   if (isTrain)
     return sm;
@@ -141,7 +141,7 @@ Symbol LSTMWithBuiltInRNNOp(int num_lstm_layer, int sequence_length, int input_d
   auto label = Symbol::Variable("softmax_label");
   label = transpose(label);
   label = Reshape(label, Shape(), false,
-                  false, Shape(-1));  // FullyConnected requires one dimension
+                  Shape(-1), false);  // FullyConnected requires one dimension
   if (!TIME_MAJOR && isTrain)
     embed = SwapAxis(embed, 0, 1);  // Change to time-major as cuDNN requires
 
@@ -151,7 +151,7 @@ Symbol LSTMWithBuiltInRNNOp(int num_lstm_layer, int sequence_length, int input_d
   auto rnn_params = Symbol::Variable("LSTM_parameters");  // See explanations near RNNXavier class
   auto rnn = RNN(embed, rnn_params, rnn_h_init, rnn_c_init, num_hidden, num_lstm_layer,
       RNNMode::lstm, false, dropout, !isTrain);
-  auto hidden = Reshape(rnn[0], Shape(), false, false, Shape(-1, num_hidden));
+  auto hidden = Reshape(rnn[0], Shape(), false, Shape(-1, num_hidden), false);
 
   auto cls_weight = Symbol::Variable("cls_weight");
   auto cls_bias = Symbol::Variable("cls_bias");
@@ -194,7 +194,8 @@ class Shuffler {
 
 class BucketSentenceIter : public DataIter {
   Shuffler* random;
-  int batch, current, end, sequence_length;
+  int batch, current, end;
+  unsigned int sequence_length;
   Context device;
   vector<vector<mx_float>> sequences;
   vector<wchar_t> index2chars;
@@ -582,7 +583,7 @@ void predict(wstring* ptext, int sequence_length, const string param_file,
   LoadCheckpoint(param_file, exe);
 
   mx_float index;
-  wchar_t next;
+  wchar_t next = 0;
   vector<mx_float> softmax;
   softmax.resize(input_dim);
   for (auto c : *ptext) {
@@ -642,7 +643,7 @@ void predictWithBuiltInRNNOp(wstring* ptext, int sequence_length, const string p
   LoadCheckpoint(param_file, exe);
 
   mx_float index;
-  wchar_t next;
+  wchar_t next = 0;
   vector<mx_float> softmax;
   softmax.resize(input_dim);
   for (auto c : *ptext) {
diff --git a/cpp-package/include/mxnet-cpp/op_suppl.h b/cpp-package/include/mxnet-cpp/op_suppl.h
index 5eb86d8ef275..9381a1ecade9 100644
--- a/cpp-package/include/mxnet-cpp/op_suppl.h
+++ b/cpp-package/include/mxnet-cpp/op_suppl.h
@@ -119,48 +119,7 @@ inline Symbol Crop(const std::string& symbol_name,
 
 
 /*!
- * \breif Slice input equally along specified axis.
- * \param data input symbol.
- * \param num_outputs Number of outputs to be sliced.
- * \param axis Dimension along which to slice.
- * \param squeeze_axis If true AND the sliced dimension becomes 1, squeeze that dimension.
- * \return new symbol
- */
-inline Symbol SliceChannel(Symbol data,
-                           int num_outputs,
-                           int axis = 1,
-                           bool squeeze_axis = false) {
-  return Operator("SliceChannel")
-           .SetParam("num_outputs", num_outputs)
-           .SetParam("axis", axis)
-           .SetParam("squeeze_axis", squeeze_axis) (data)
-           .CreateSymbol();
-}
-
-
-/*!
- * \breif Slice input equally along specified axis.
- * \param symbol_name name of the resulting symbol.
- * \param data input symbol.
- * \param num_outputs Number of outputs to be sliced.
- * \param axis Dimension along which to slice.
- * \param squeeze_axis If true AND the sliced dimension becomes 1, squeeze that dimension.
- * \return new symbol
- */
-inline Symbol SliceChannel(const std::string& symbol_name,
-                           Symbol data,
-                           int num_outputs,
-                           int axis = 1,
-                           bool squeeze_axis = false) {
-  return Operator("SliceChannel")
-           .SetParam("num_outputs", num_outputs)
-           .SetParam("axis", axis)
-           .SetParam("squeeze_axis", squeeze_axis) (data)
-           .CreateSymbol(symbol_name);
-}
-
-/*!
- * \breif Apply activation function to input.
+ * \brief Apply activation function to input.
  *        Softmax Activation is only available with CUDNN on GPUand will be
  *        computed at each location across channel if input is 4D.
  * \param symbol_name name of the resulting symbol.