diff --git a/README.md b/README.md
index 5af3ac4bacc3d563dab2dd9133dcc3ea5aadf1f9..a1e6ecb62ef15ba36a668a85a9248d621bd84fc9 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,9 @@ visit [this](https://gitlab.version.fz-juelich.de/MLDL_FZJ/MLDL_FZJ_Wiki/wikis/E
### Announcements
+* **November 28, 2019:** Slides and code samples for the "Deep Learning on Supercomputers" talk given
+as part of the [Introduction to the programming and usage of the supercomputer resources at Jülich](https://www.fz-juelich.de/SharedDocs/Termine/IAS/JSC/EN/courses/2019/supercomputer-2019-11.html?nn=944302)
+course are now available in the `course_material` directory.
* **November 22, 2019:** Samples for Caffe are no longer supported on JURECA due to system-wide
MVAPICH2 module changes.
* **November 18, 2019:** The `horovod_data_distributed` directory has been added that contains code
diff --git a/course_material/README.md b/course_material/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e8bb64436ffaa398a40f6db8b549bbcaedec90a4
--- /dev/null
+++ b/course_material/README.md
@@ -0,0 +1,9 @@
+# Slides and code samples
+
+The slides and code samples in the sub-directories correspond to the introductory examples presented during the
+"Deep Learning on Supercomputers" talk, which is given as
+part of the [Introduction to the programming and usage of the supercomputer resources at Jülich](https://www.fz-juelich.de/SharedDocs/Termine/IAS/JSC/EN/courses/2019/supercomputer-2019-11.html?nn=944302)
+course.
+
+**Note:** These code samples are NOT designed to work on our supercomputers. To see why, read `datasets/README.md`.
+To run code samples on the supercomputers, please follow the main tutorial.
\ No newline at end of file
diff --git a/course_material/examples/mnist_epoch_distributed.py b/course_material/examples/mnist_epoch_distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c9080e63af23eabeb6a6b47c8e89edf26e7190f
--- /dev/null
+++ b/course_material/examples/mnist_epoch_distributed.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2019 Forschungszentrum Juelich GmbH.
+# This code is licensed under MIT license (see the LICENSE file for details).
+# This code is derived from Horovod, which is licensed under the Apache License,
+# Version 2.0 (see the NOTICE file for details).
+
+"""
+ This program is an adaptation of the following code sample:
+ https://github.com/horovod/horovod/blob/master/examples/keras_mnist.py.
+ The program creates and trains a shallow ANN for handwritten digit
+ classification using the MNIST dataset.
+
+ The Horovod framework is used for seamless distributed training. In this
+ example epochs are distributed across the Horovod ranks, not data.
+
+ To run this sample use the following command on your
+ workstation/laptop equipped with a GPU:
+
+ mpirun -np 1 python -u mnist_epoch_distributed.py
+
+ If you have more than one GPU on your system, you can increase the
+ number of ranks accordingly.
+
+ The code has been tested with Python 3.7.5, tensorflow-gpu 1.13.1, and
+ horovod 0.16.2.
+
+ Note: This code will NOT work on the supercomputers.
+
+"""
+
+import math
+import tensorflow as tf
+import horovod.tensorflow.keras as hvd
+from tensorflow.python.keras import backend as K
+
+
+# Horovod: initialize Horovod.
+hvd.init()
+
+# Horovod: pin GPU to be used to process local rank (one GPU per process)
+config = tf.ConfigProto()
+config.gpu_options.visible_device_list = str(hvd.local_rank())
+K.set_session(tf.Session(config=config))
+
+# Reference to the MNIST dataset
+mnist = tf.keras.datasets.mnist
+
+# Load the MNIST dataset, split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# Normalize input samples
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# Define the model, i.e., the network
+model = tf.keras.models.Sequential([
+ tf.keras.layers.Flatten(),
+ tf.keras.layers.Dense(512, activation=tf.nn.relu),
+ tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+])
+
+# Optimizer
+optimizer = tf.keras.optimizers.Adam()
+
+# Decorate the optimizer with Horovod's distributed optimizer
+optimizer = hvd.DistributedOptimizer(optimizer)
+
+# Horovod: adjust number of epochs based on number of GPUs.
+epochs = int(math.ceil(4.0 / hvd.size()))
+
+# Compile the model
+model.compile(
+ optimizer=optimizer,
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy']
+)
+
+# Training callbacks
+callbacks = [
+ # Horovod: broadcast initial variable states from rank 0 to all other processes.
+ # This is necessary to ensure consistent initialization of all workers when
+ # training is started with random weights or restored from a checkpoint.
+ hvd.callbacks.BroadcastGlobalVariablesCallback(0)
+]
+
+# Train the model using the training set
+model.fit(
+ x=x_train,
+ y=y_train,
+ batch_size=32,
+ epochs=epochs,
+ verbose=1 if hvd.rank() == 0 else 0,
+ callbacks=callbacks
+)
+
+# Run the test on the root rank only
+if hvd.rank() == 0:
+ # Test the model on the test set
+ score = model.evaluate(x=x_test, y=y_test, verbose=0)
+ print(f'Test loss: {score[0]}')
+ print(f'Test accuracy: {score[1]}')
diff --git a/course_material/examples/mnist_single_gpu.py b/course_material/examples/mnist_single_gpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..794150fe230348b0001d86158d32a9a9e5e52cbd
--- /dev/null
+++ b/course_material/examples/mnist_single_gpu.py
@@ -0,0 +1,67 @@
+# Copyright (c) 2019 Forschungszentrum Juelich GmbH.
+# This code is licensed under MIT license (see the LICENSE file for details).
+# This code is derived from Tensorflow tutorials, which is licensed under the Apache License,
+# Version 2.0 (see the NOTICE file for details).
+
+"""
+ This program is an adaptation of the code sample available at
+ https://www.tensorflow.org/tutorials/. The program creates
+ and trains a shallow ANN for handwritten digit classification
+ using the MNIST dataset.
+
+ To run this sample use the following command on your
+ workstation/laptop equipped with a GPU:
+
+ python -u mnist.py
+
+ The code has been tested with Python 3.7.5 and tensorflow-gpu 1.13.1.
+
+ Note: This code will NOT work on the supercomputers.
+
+"""
+
+import tensorflow as tf
+
+
+# Reference to the MNIST data object
+mnist = tf.keras.datasets.mnist
+
+# Load the MNIST dataset, split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# Normalize input samples
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# Define the model, i.e., the network
+model = tf.keras.models.Sequential([
+ tf.keras.layers.Flatten(),
+ tf.keras.layers.Dense(512, activation=tf.nn.relu),
+ tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+])
+
+# Optimizer
+optimizer = tf.keras.optimizers.Adam()
+
+# No. of epochs
+epochs = 4
+
+# Compile the model
+model.compile(
+ optimizer=optimizer,
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy']
+)
+
+# Train the model using the training set
+model.fit(
+ x=x_train,
+ y=y_train,
+ batch_size=32,
+ epochs=epochs,
+ verbose=1
+)
+
+# Test the model using the test set
+score = model.evaluate(x=x_test, y=y_test, verbose=0)
+print(f'Test loss: {score[0]}')
+print(f'Test accuracy: {score[1]}')
diff --git a/course_material/slides/.gitkeep b/course_material/slides/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/course_material/slides/fahad_DL_on_SCs_November_2019.pdf b/course_material/slides/fahad_DL_on_SCs_November_2019.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d0ddcb130c4bda3a571935530cf2ce7b19488bab
Binary files /dev/null and b/course_material/slides/fahad_DL_on_SCs_November_2019.pdf differ
diff --git a/utils/data_utils.py b/utils/data_utils.py
index bab6e035ea82e22108998da561519a70eea94eac..f2d10e4111fe70fdc465287a3b5f18bff8d6981f 100644
--- a/utils/data_utils.py
+++ b/utils/data_utils.py
@@ -22,9 +22,6 @@ class DataValidator:
"""
- def __init__(self):
- """ No-op constructor. """
-
@staticmethod
def validated_data_dir(filename):
"""
@@ -32,15 +29,9 @@ class DataValidator:
recognized input data directory locations. If the check is passed,
returns the fully qualified path to the input data directory.
- Parameters
- ----------
- filename:
- Name of the data file to be checked
+ :param filename: Name of the data file to be checked.
- Returns
- -------
- string:
- Fully qualified path to the input data directory
+ :return: str. Fully qualified path to the input data directory.
"""