Multithreaded embedded python and Tensorflow

I have a TensorFlow model written in python and I have a c++ application which acquires the training data and passes it to Tensorflow with embedded python Api.

The application has a GUI so it naturally should be multithreaded.

The problem is that although application runs without errors it seems that the model is not training (at least, not in the way I expect), i.e. the trained variable is always some random number close to the initial value. I tried to dump the data and feed it to the python class directly from python and it worked perfectly.

I assume I am doing something wrong with threading. Right?

The definition of the python class looks like this (for simplicity I will assume that we have only one variable):

class TrainingClass:
    def __init__(self, params):
        # assign params
        # build graph, loss etc...
        self.variable = tf.Variable(42, dtype=tf.float64)


        self.session = tf.Session()
        self.session.run([tf.global_variables_initializer()])

    def train(self, train_params):
        return self.session.run([list_of_optimizers, self.loss], feed_dict = {self.param_placeholders: params})[-1]

    def get_trained_variable(self):
        return self.session.run(self.variable)

On c++ side things look like this.

class MyCppClass {

    std::thread *m_training_thread;
    bool thread_is_canceled();

    void on_start_button_pressed() {
        PyObject * python_class_instance = PyObject_CallFunctionObjArgs(python_class_type, params..., NULL);

        m_training_thread = new std::thread(&MyCppClass::cpp_train_function, this, python_class_instance );
    }

    void cpp_train_function(PyObject * python_class_instance) {
        while(!thread_is_canceled()) {
             // Acquire lock
             PyGILState_STATE state = PyGILState_Ensure();

             PyObject * py_list = PyList_New(42);

             /* Long and boring code for assigning 
              * the items of the list 
             */

             // Pass the py_list to tensorflow
             PyObject * py_loss = PyObject_CallMethodObjArgs(python_class_instance, train_function_name, py_list, NULL );

             if( py_loss ) {
                // Display current loss value to the user
             }

             // Release lock
             PyGILState_Release(state);
        }
    }

    void show_trained_parameters(PyObject * python_class_instance)
    {
        // Acquire lock
        PyGILState_STATE state = PyGILState_Ensure();

        PyObject * trained_vars = PyObject_CallMethodObjArgs(python_class_instance, get_trained_variable_py_str , NULL);

        // Show the trained variable

        // Release lock
        PyGILState_Release(state);
    }
};

I verified that the py_list is correctly assigned by printing it from python.

OS: Ubuntu 18.04 C++ 11 Python 3.6