diff --git a/dalle2_pytorch/utils.py b/dalle2_pytorch/utils.py
new file mode 100644
index 0000000..f7cbf86
--- /dev/null
+++ b/dalle2_pytorch/utils.py
@@ -0,0 +1,11 @@
+import time
+
+class Timer:
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.last_time = time.time()
+
+    def elapsed(self):
+        return time.time() - self.last_time
diff --git a/train_decoder.py b/train_decoder.py
index 3c91c36..e3daa5e 100644
--- a/train_decoder.py
+++ b/train_decoder.py
@@ -2,8 +2,9 @@ from dalle2_pytorch import Unet, Decoder
 from dalle2_pytorch.trainer import DecoderTrainer, print_ribbon
 from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
 from dalle2_pytorch.trackers import WandbTracker, ConsoleTracker
+from dalle2_pytorch.utils import Timer
+
 from configs.decoder_defaults import default_config, ConfigField
-import time
 import json
 import torchvision
 from torchvision import transforms as T
@@ -260,14 +261,17 @@ def train(
     
     send_to_device = lambda arr: [x.to(device=inference_device, dtype=torch.float) for x in arr]
     step = start_step
+
     for epoch in range(start_epoch, epochs):
         print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
         trainer.train()
 
+        timer = Timer()
+
         sample = 0
         last_sample = 0
         last_snapshot = 0
-        last_time = time.time()
+
         losses = []
         for i, (img, emb) in enumerate(dataloaders["train"]):
             step += 1
@@ -281,8 +285,9 @@ def train(
                     trainer.update(unet_number=unet)
                     losses.append(loss)
 
-            samples_per_sec = (sample - last_sample) / (time.time() - last_time)
-            last_time = time.time()
+            samples_per_sec = (sample - last_sample) / timer.elapsed()
+
+            timer.reset()
             last_sample = sample
 
             if i % 10 == 0:
@@ -320,7 +325,7 @@ def train(
         with torch.no_grad():
             sample = 0
             average_loss = 0
-            start_time = time.time()
+            timer = Timer()
             for i, (img, emb, txt) in enumerate(dataloaders["val"]):
                 sample += img.shape[0]
                 img, emb = send_to_device((img, emb))
@@ -330,12 +335,13 @@ def train(
                     average_loss += loss
 
                 if i % 10 == 0:
-                    print(f"Epoch {epoch}/{epochs} - {sample / (time.time() - start_time):.2f} samples/sec")
+                    print(f"Epoch {epoch}/{epochs} - {sample / timer.elapsed():.2f} samples/sec")
                     print(f"Loss: {average_loss / (i+1)}")
                     print("")
 
                 if validation_samples is not None and sample >= validation_samples:
                     break
+
             average_loss /= i+1
             log_data = {
                 "Validation loss": average_loss
@@ -497,4 +503,4 @@ def main(config_file):
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/train_diffusion_prior.py b/train_diffusion_prior.py
index bb07a69..c7eb1a1 100644
--- a/train_diffusion_prior.py
+++ b/train_diffusion_prior.py
@@ -1,7 +1,6 @@
 from pathlib import Path
 import click
 import math
-import time
 import numpy as np
 
 import torch
@@ -13,6 +12,7 @@ from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork, OpenAIClipAdap
 from dalle2_pytorch.trainer import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model, print_ribbon
 
 from dalle2_pytorch.trackers import ConsoleTracker, WandbTracker
+from dalle2_pytorch.utils import Timer
 
 from embedding_reader import EmbeddingReader
 
@@ -29,16 +29,6 @@ tracker = WandbTracker()
 def exists(val):
     val is not None
 
-class Timer:
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.last_time = time.time()
-
-    def elapsed(self):
-        return time.time() - self.last_time
-
 # functions
 
 def eval_model(model, dataloader, text_conditioned, loss_type, phase="Validation"):