on_training_epoch_end is never called

[bavShehata]

I'm training a CNN and I wanted to log some metrics at the end of each training epoch. However, I've noticed that on_training_epoch_end is never called while on_validation_epoch_end works just fine Here's an excerpt of the model containing those two:

def training_step(self, batch):
          images, labels = batch 
          pred = self(images)
          train_loss = F.cross_entropy(pred, labels)
          correct=pred.argmax(dim=1).eq(labels).sum().item()
          total=len(labels)
          batch_dictionary={
                "loss": train_loss,
                "correct": correct,
                "total": total
          }
          self.training_step_outputs.append(batch_dictionary)
          return batch_dictionary
          

      def validation_step(self, batch, batch_idx):
          images, labels = batch 
          pred = self(images)
          val_loss = F.cross_entropy(pred, labels)
          correct=pred.argmax(dim=1).eq(labels).sum().item()
          total=len(labels)
          val_acc = correct/total
          batch_dictionary={
                "loss": val_loss,
                "acc": val_acc,
                "correct": correct,
                "total": total
          }
          self.validation_step_outputs.append(batch_dictionary)
          return batch_dictionary
          
      def on_training_epoch_end(self):
          print('training epoch')
          outputs = self.training_step_outputs;
          batch_losses = [x['loss'] for x in outputs]
          epoch_loss = torch.stack(batch_losses).mean()   # Combine losses
          epoch_acc = sum([x['acc'] for x in outputs])/len(outputs)
          print("Training accuracy : ", epoch_acc)
          print("Training loss : ", epoch_loss)
          self.training_step_outputs.clear()  # free memory
          
      def on_validation_epoch_end(self):
          outputs = self.validation_step_outputs;
          batch_losses = [x['loss'] for x in outputs]
          epoch_loss = torch.stack(batch_losses).mean()   # Combine losses
          epoch_acc = sum([x['acc'] for x in outputs])/len(outputs)
          print("\nValidation accuracy : ", epoch_acc)
          print("Validation loss : ", epoch_loss)
          val_acc.append(epoch_acc)
          self.validation_step_outputs.clear()  # free memory

I've looked around and couldn't find any solutions to as to why this is happening or how to fix it.

[bavShehata]

If any one finds themselves here, the correct hook is on_train_epoch_end and not on_training_epoch_end as that was a mistake in the docs as answered here on their forums