don't duplicate logging for validation loss each epoch

src/ml_downscaling_emulator/score_sde_pytorch/run_lib.py

@@ -218,9 +218,7 @@ def train(config, workdir):
       val_set_loss = val_loss(config, eval_dl, eval_step_fn, state)
       epoch_metrics = {"epoch/train/loss": train_set_loss, "epoch/val/loss": val_set_loss}
 
-      logging.info("epoch: %d, val_loss: %.5e" % (state['epoch'], val_set_loss))
-      writer.add_scalar("epoch/val/loss", val_set_loss, global_step=state['epoch'])
-      log_epoch(state['epoch'], epoch_metrics, wandb_run,writer)
+      log_epoch(state['epoch'], epoch_metrics, wandb_run, writer)
 
       if (state['epoch'] != 0 and state['epoch'] % config.training.snapshot_freq == 0) or state['epoch'] == num_train_epochs:
         # Save the checkpoint.