diff --git a/train.py b/train.py
index 1041ec30c257..093a6197ff06 100644
--- a/train.py
+++ b/train.py
@@ -4,6 +4,7 @@
 import os
 import random
 import time
+import warnings
 from copy import deepcopy
 from pathlib import Path
 from threading import Thread
@@ -323,18 +324,19 @@ def train(hyp, opt, device, tb_writer=None):
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                 mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
                 s = ('%10s' * 2 + '%10.4g' * 6) % (
-                    '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
+                    f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])
                 pbar.set_description(s)
 
                 # Plot
                 if plots and ni < 3:
                     f = save_dir / f'train_batch{ni}.jpg'  # filename
                     Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
-                    if tb_writer:
-                        tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs, strict=False), [])  # model graph
-                        # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
+                    if tb_writer and ni == 0:
+                        with warnings.catch_warnings():
+                            warnings.simplefilter('ignore')  # suppress jit trace warning
+                            tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs, strict=False), [])  # graph
                 elif plots and ni == 10 and wandb_logger.wandb:
-                    wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
+                    wandb_logger.log({'Mosaics': [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
                                                   save_dir.glob('train*.jpg') if x.exists()]})
 
             # end batch ------------------------------------------------------------------------------------------------