Logging hyperparams and metrics
My question is how do I log both hyperparams and metrics so that tensorboard works "properly". I've copied pytorch_lightning.loggers.TensorBoardLogger into a catboost/hyperopt project, and using the code below after each iteration I get the result I'm after, on the tensorboard HPARAMS page both the hyperparameters and the metrics appear and I can view the Parallel Coords View etc.
    self.logger.log_hyperparams(
        params=dict(n_estimators=n_estimators, max_leaves=max_leaves, l2_leaf_reg=l2_leaf_reg, min_data_in_leaf=min_data_in_leaf),
        metrics=dict(val_loss=val_loss, train_loss=train_loss))

However when I follow the lightning tutorials, only the hyperparams are being logged, there's no metrics so the charts don't display.

Is there something additional I should be doing to ensure that log_hyperparams passes the metrics on_train_end (or wherever is appropriate, since on_train_end also doesn't appear to pass outputs)
Edit:
I can see in run_pretrain_routine that log_hyperparams is invoked with no metrics
    # log hyper-parameters
    if self.logger is not None:
        # save exp to get started
        self.logger.log_hyperparams(ref_model.hparams)

        self.logger.save()

This seems to be the wrong place to call log_hyperparams, for tensorboard at least it should really be post training with the validation loss.
Edit 2:
And my example which does work, only sort of works. Looking closer it seems to treat all the hyperparameters as categorical (i,e, they're not ordered). Examples of use with keras callbacks have the hyperparameters and metrics defined first (i.e. type and range).