Checkpoints based on validation_step or validation_epoch_end
Somewhere I found an example for
def validation_step(self, batch, batch_idx):
       ....
        return {'val_loss': loss, ....}

def validation_epoch_end(self, batch):
        avg_val_loss               = torch.tensor([ x['val_loss'] for x in batch] ).mean()
        .....
        return {'val_loss': avg_val_loss,....}

What does the automatic checkpoint use for deciding if it got a better checkpoint?
My average val loss is getting better, but I do not have a checkpoint ( green line is run 292 ).



To avoid ambiguity, it would be nice to change the name.  Where are all the places I would have to change the name 'val_loss' if I were to make it 'avg_val_loss'?
btw, lighting is amazing!  I made excellent progress on a monster transformer model and I never worried about figuring out checkpoint, ddp, multi gpu, etc, etc.