diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py
index fb0b8d7..7fc5781 100644
--- a/recbole/trainer/trainer.py
+++ b/recbole/trainer/trainer.py
@@ -218,7 +218,7 @@ class Trainer(AbstractTrainer):
             train_loss_output += "train loss: %.4f" % losses
         return train_loss_output + ']'
 
-    def fit(self, train_data, valid_data=None, verbose=True, saved=True):
+    def fit(self, train_data, valid_data=None, verbose=True, saved=True, callback_fn=None):
         r"""Train the model based on the train data and the valid data.
 
         Args:
@@ -227,6 +227,8 @@ class Trainer(AbstractTrainer):
                                                If it's None, the early_stopping is invalid.
             verbose (bool, optional): whether to write training and evaluation information to logger, default: True
             saved (bool, optional): whether to save the model parameters, default: True
+            callback_fn (callable): Optional callback function executed at end of epoch. Includes
+                                    (epoch_idx, valid_score) input arguments
 
         Returns:
              (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None)
@@ -273,6 +275,9 @@ class Trainer(AbstractTrainer):
                             self.logger.info(update_output)
                     self.best_valid_result = valid_result
 
+                if callback_fn:
+                    callback_fn(epoch_idx, valid_score)
+
                 if stop_flag:
                     stop_output = 'Finished training, best eval result in epoch %d' % \
                                   (epoch_idx - self.cur_step * self.eval_step)
@@ -647,12 +652,12 @@ class MKRTrainer(Trainer):
             interaction = interaction.to(self.device)
             self.optimizer.zero_grad()
             loss_rs = self.model.calculate_rs_loss(interaction)
-            
+
             self._check_nan(loss_rs)
             loss_rs.backward()
             self.optimizer.step()
             rs_total_loss += loss_rs
-            
+
         # train kg
         if epoch_idx % self.kge_interval == 0:
             print('Train KG')
@@ -661,7 +666,7 @@ class MKRTrainer(Trainer):
                 interaction = interaction.to(self.device)
                 self.optimizer.zero_grad()
                 loss_kge = self.model.calculate_kg_loss(interaction)
-                
+
                 self._check_nan(loss_kge)
                 loss_kge.backward()
                 self.optimizer.step()