How to use ReduceLROnPlateau methon in matster branch version?
What is your question?
I find the log design has changed a lot between version 0.8.5 and master branch 0c26468
I got the error message when I follow the docs logging-from-a-lightningmodule to modify the log code.
error message:
MisconfigurationException: ReduceLROnPlateau conditioned on metric val_loss which is not available. Available metrics are: val_early_stop_on,val_checkpoint_on,epoch,checkpoint_on. Condition can be set using `monitor` key in lr scheduler dict

Code
def configure_optimizers(self):
    optimizer = torch.optim.Adam(self.parameters(), lr = 0.01)
    scheduler = ReduceLROnPlateau(optimizer, patience=10)
    return [optimizer], [scheduler]

def validation_step(self, batch, batch_nb):
    x, y = batch
    
    y_hat = self(x)    
    loss = F.l1_loss(y, y_hat)
    result = pl.EvalResult()
    result.log('val_step_loss', loss)
    return result

def validation_epoch_end(self, outputs):
    avg_loss = outputs.val_step_loss.mean()
    result = pl.EvalResult()
    result.log('val_loss', avg_loss)
    return result
I wonder if I should defined validation_epoch_end like above, if there are any example about how to use ReduceLROnPlateau in a right way?
What's your environment?

OS: Ubuntu 18.04
Packaging pip
Version 0.9.0 rc 12 master branch 0c26468