diff --git a/build-python.sh b/build-python.sh
index 8892ca2e936f..4eefcbeaf6c7 100755
--- a/build-python.sh
+++ b/build-python.sh
@@ -186,7 +186,7 @@ while [ $# -gt 0 ]; do
   shift
 done
 
-pip install --prefer-binary 'build>=0.10.0'
+venv/bin/pip install --prefer-binary 'build>=0.10.0'
 
 # create a new directory that just contains the files needed
 # to build the Python package
@@ -362,12 +362,12 @@ if test "${INSTALL}" = true; then
     echo "--- installing lightgbm ---"
     # ref for use of '--find-links': https://stackoverflow.com/a/52481267/3986677
     cd ../dist
-    pip install \
+    ../venv/bin/pip install \
         ${PIP_INSTALL_ARGS} \
         --force-reinstall \
         --no-cache-dir \
         --find-links=. \
-        lightgbm
+        lightgbm-*.whl
     cd ../
 fi
 
diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index 3dfa583a62bb..752570aaba48 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -3661,6 +3661,7 @@ def update(
             is_the_same_train_set = False
         else:
             is_the_same_train_set = train_set is self.train_set and self.train_set_version == train_set.version
+        print("t1")
         if train_set is not None and not is_the_same_train_set:
             if not isinstance(train_set, Dataset):
                 raise TypeError(f'Training data should be Dataset instance, met {type(train_set).__name__}')
@@ -3673,6 +3674,7 @@ def update(
                 self.train_set.construct()._handle))
             self.__inner_predict_buffer[0] = None
             self.train_set_version = self.train_set.version
+        print("t2")
         is_finished = ctypes.c_int(0)
         if fobj is None:
             if self.__set_objective_to_none:
@@ -3682,10 +3684,12 @@ def update(
                 ctypes.byref(is_finished)))
             self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]
             return is_finished.value == 1
+            print("t3")
         else:
             if not self.__set_objective_to_none:
                 self.reset_parameter({"objective": "none"}).__set_objective_to_none = True
             grad, hess = fobj(self.__inner_predict(0), self.train_set)
+            print("t4")
             return self.__boost(grad, hess)
 
     def __boost(
diff --git a/python-package/lightgbm/engine.py b/python-package/lightgbm/engine.py
index 822aa3b35017..31241f2fb4b2 100644
--- a/python-package/lightgbm/engine.py
+++ b/python-package/lightgbm/engine.py
@@ -264,6 +264,7 @@ def train(
     booster.best_iteration = 0
 
     # start training
+    print("t0")
     for i in range(init_iteration, init_iteration + num_boost_round):
         for cb in callbacks_before_iter:
             cb(callback.CallbackEnv(model=booster,
diff --git a/src/boosting/gbdt_model_text.cpp b/src/boosting/gbdt_model_text.cpp
index 73c3ea98d3f6..b6cb40d876f5 100644
--- a/src/boosting/gbdt_model_text.cpp
+++ b/src/boosting/gbdt_model_text.cpp
@@ -23,6 +23,7 @@ std::string GBDT::DumpModel(int start_iteration, int num_iteration, int feature_
   Common::C_stringstream(str_buf);
 
   str_buf << "{";
+  str_buf << "\"TESTING\":\"" << "HAHA_v2" << "\"," << '\n';
   str_buf << "\"name\":\"" << SubModelName() << "\"," << '\n';
   str_buf << "\"version\":\"" << kModelVersion << "\"," << '\n';
   str_buf << "\"num_class\":" << num_class_ << "," << '\n';