diff --git a/pts/trainer.py b/pts/trainer.py
index e224c74..19c6453 100644
--- a/pts/trainer.py
+++ b/pts/trainer.py
@@ -1,7 +1,7 @@
 import time
 from typing import List, Optional, Union
 
-from tqdm import tqdm
+from tqdm.auto import tqdm
 import wandb
 
 import torch
@@ -61,11 +61,11 @@ class Trainer:
             # mark epoch start time
             tic = time.time()
             cumm_epoch_loss = 0.0
+            total = self.num_batches_per_epoch - 1
 
             # training loop
-            with tqdm(train_iter) as it:
+            with tqdm(train_iter, total=total) as it:
                 for batch_no, data_entry in enumerate(it, start=1):
-                    it.update(1)
                     optimizer.zero_grad()
 
                     inputs = [v.to(self.device) for v in data_entry.values()]
@@ -83,7 +83,7 @@ class Trainer:
                             "epoch": f"{epoch_no + 1}/{self.epochs}",
                             "avg_loss": avg_epoch_loss,
                         },
-                        refresh=False
+                        refresh=False,
                     )
 
                     wandb.log({"loss": loss.item()})
@@ -97,13 +97,14 @@ class Trainer:
 
                     if self.num_batches_per_epoch == batch_no:
                         break
+                it.close()
 
-                # validation loop
-                if validation_iter is not None:
-                    cumm_epoch_loss_val = 0.0
+            # validation loop
+            if validation_iter is not None:
+                cumm_epoch_loss_val = 0.0
+                with tqdm(validation_iter, total=total, colour="green") as it:
 
-                    for batch_no, data_entry in enumerate(validation_iter, start=1):
-                        it.update(1)
+                    for batch_no, data_entry in enumerate(it, start=1):
                         inputs = [v.to(self.device) for v in data_entry.values()]
                         with torch.no_grad():
                             output = net(*inputs)
@@ -120,13 +121,13 @@ class Trainer:
                                 "avg_loss": avg_epoch_loss,
                                 "avg_val_loss": avg_epoch_loss_val,
                             },
+                            refresh=False,
                         )
 
                         if self.num_batches_per_epoch == batch_no:
                             break
 
                     wandb.log({"avg_val_loss": avg_epoch_loss_val})
-                    print(it) # TODO fix this
                 it.close()
 
             # mark epoch end time and log time cost of current epoch