Tenorboard, logs either don't appear or have prepended 'epoch_' names
I have two kinds of problems with Tensorboard.

Either logs don't appear when I create them inside training_step.
Code:

def training_step(self, batch, batch_idx):
        type = "train"
        loss, acc, y_true, y_pred, name = self.step(batch)

        result = pl.TrainResult(minimize=loss)
        result.log(type + "_loss", loss, prog_bar=True, on_step=True, on_epoch=False)
        result.log(type + "_acc", acc, prog_bar=True, on_step=True, on_epoch=False)

        return result

Screen from Tensorflow:

2. Or... 'epoch_' text is prepended to the logs name for an unknown reason although the same code is done for validation.
Code:
    def training_step(self, batch, batch_idx):
        type = "train"
        loss, acc, y_true, y_pred, name = self.step(batch)

        result = pl.TrainResult(minimize=loss)
        result.log(type + "_loss", loss, logger=False, prog_bar=True, on_step=True, on_epoch=False)
        result.log(type + "_acc", acc, logger=False, prog_bar=True, on_step=True, on_epoch=False)

        return result

    def training_epoch_end(self, outputs):
        type = "train"

        avg_loss = torch.stack([x for x in outputs[type + "_loss"]]).mean()
        avg_acc = torch.stack([x for x in outputs[type + "_acc"]]).mean()

        result = pl.TrainResult()
        result.log(type + "_loss", avg_loss, prog_bar=False, on_epoch=True)
        result.log(type + "_acc", avg_acc, prog_bar=False, on_epoch=True)

        return result

    def validation_step(self, batch, batch_idx):
        type = "val"
        loss, acc, y_true, y_pred, name = self.step(batch)

        result = pl.EvalResult()
        result.log(type + "_loss", loss, logger=False, prog_bar=True, on_step=True, on_epoch=False)
        result.log(type + "_acc", acc, logger=False, prog_bar=True, on_step=True, on_epoch=False)

        return result

    def validation_epoch_end(self, outputs):
        type = "val"

        avg_loss = torch.stack([x for x in outputs[type + "_loss"]]).mean()
        avg_acc = torch.stack([x for x in outputs[type + "_acc"]]).mean()

        result = pl.EvalResult(checkpoint_on=avg_loss, early_stop_on=avg_loss)
        result.log(type + "_loss", avg_loss, prog_bar=False, on_epoch=True)
        result.log(type + "_acc", avg_acc, prog_bar=False, on_epoch=True)

        return result

Screen from Tensorflow:

Why is that happening? Why validation logs do not have epoch_ beginning? The only difference is in using TrainReport vs EvalReport.