diff --git a/tensorboard_profiling_keras.py b/tensorboard_profiling_keras.py
new file mode 100755
index 00000000..bdcd4cfb
--- /dev/null
+++ b/tensorboard_profiling_keras.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+
+#@title Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datetime import datetime
+import os
+import tensorflow as tf
+
+print("TensorFlow version: ", tf.__version__)
+
+device_name = tf.test.gpu_device_name()
+if not device_name:
+  raise SystemError('GPU device not found')
+print('Found GPU at: {}'.format(device_name))
+
+import tensorflow_datasets as tfds
+tfds.disable_progress_bar()
+
+
+# Limit to a short training time tfor demonstration purposes
+Epochs = 2
+
+
+(ds_train, ds_test), ds_info = tfds.load(
+    'mnist',
+    split=['train', 'test'],
+    shuffle_files=True,
+    as_supervised=True,
+    with_info=True,
+)
+
+def normalize_img(image, label):
+  """Normalizes images: `uint8` -> `float32`."""
+  return tf.cast(image, tf.float32) / 255., label
+
+ds_train = ds_train.map(normalize_img)
+ds_train = ds_train.batch(128)
+
+ds_test = ds_test.map(normalize_img)
+ds_test = ds_test.batch(128)
+
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
+  tf.keras.layers.Dense(128,activation='relu'),
+  tf.keras.layers.Dense(10, activation='softmax')
+])
+model.compile(
+    loss='sparse_categorical_crossentropy',
+    optimizer=tf.keras.optimizers.Adam(0.001),
+    metrics=['accuracy']
+)
+
+# Create a TensorBoard callback
+logs = "logs/" + datetime.now().strftime("%Y%m%d-%H%M%S")
+
+tboard_callback = tf.keras.callbacks.TensorBoard(log_dir = logs,
+                                                 histogram_freq = 1,
+                                                 profile_batch = (1, Epochs)) # Note: 1-indexed
+
+model.fit(ds_train,
+          epochs=Epochs,
+          validation_data=ds_test,
+          callbacks = [tboard_callback])
+
+
+(ds_train, ds_test), ds_info = tfds.load(
+    'mnist',
+    split=['train', 'test'],
+    shuffle_files=True,
+    as_supervised=True,
+    with_info=True,
+)
+
+ds_train = ds_train.map(normalize_img)
+ds_train = ds_train.batch(128)
+ds_train = ds_train.cache()
+ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
+
+ds_test = ds_test.map(normalize_img)
+ds_test = ds_test.batch(128)
+ds_test = ds_test.cache()
+ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)
+
+model.fit(ds_train,
+          epochs=Epochs,
+          validation_data=ds_test,
+          callbacks = [tboard_callback])
+
+