diff --git a/tutorials/04-utils/tensorboard/logger_pytorch.py b/tutorials/04-utils/tensorboard/logger_pytorch.py
new file mode 100644
index 00000000..b958b0a2
--- /dev/null
+++ b/tutorials/04-utils/tensorboard/logger_pytorch.py
@@ -0,0 +1,51 @@
+from io import BytesIO         # Python 3.x
+import numpy as np
+from PIL import Image 
+import torch 
+import torch.utils.tensorboard as tb
+import torchvision
+
+class Logger(object):
+    def __init__(self, log_dir):
+        """ Create a summary writer object logging to log_dir."""
+        self.writer = tb.SummaryWriter(log_dir=log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        """Log a scalar variable."""
+        self.writer.add_scalar(tag,value, step)
+        self.writer.flush()
+
+    def image_summary(self, tag, image, step,dataformats='HWC'):
+        """Log image , Input image will be a numpy ndarray 
+        dataformats : can be changed as per requirement to HWC,CHW..
+        where H-Height W-Width and C-Channels of image"""
+        self.writer.add_image(tag,image,step,dataformats)
+        self.writer.flush()	
+        
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+        self.writer.add_histogram(tag,values, step)
+        self.writer.flush()
+   
+    def text_summary(self,tag,value,step):
+        """Log text with tag to it"""
+        self.writer.add_text(tag,value,step)
+        self.writer.flush()
+
+    def embedding_summary(self,embedding_matrix, metadata=None, label_img=None, 
+          global_step=None, tag='default', metadata_header=None):
+        """Log embedding matrix to tensorboard."""
+        self.writer.add_embedding(embedding_matrix, metadata, label_img,global_step, tag,
+                metadata_header)
+        self.writer.flush()
+
+    def plot_pr_summary(self,tag, labels, predictions, global_step=None,
+            num_thresholds=127, weights=None, walltime=None):
+        """Plot Precision/Recall curves with labels being actual labels 
+        and predictions being how accurarte(in tems of %)"""
+        self.writer.add_pr_curve(tag, labels, predictions, global_step, num_thresholds, weights, walltime)
+        self.writer.flush()
+   
+    def __del__(self):
+        """close the writer"""
+        self.writer.close()