Updated the course material so that the examples comply with TF2.

course_material/examples/mnist_epoch_distributed.py

@@ -4,8 +4,6 @@
 # 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.
 
@@ -13,14 +11,14 @@
     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:
+    workstation/laptop:
 
         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
+    The code has been tested with Python 3.8.7, tensorflow 2.3.1, and
     horovod 0.16.2.
 
     Note: This code will NOT work on the supercomputers.
@@ -30,16 +28,17 @@
 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))
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
 
 # Reference to the MNIST dataset
 mnist = tf.keras.datasets.mnist

course_material/examples/mnist_single_gpu.py

@@ -4,17 +4,16 @@
 # 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.
+    This program is an adaptation of a previously available code sample
+    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:
+    workstation/laptop:
 
         python -u mnist.py
 
-    The code has been tested with Python 3.7.5 and tensorflow-gpu 1.13.1.
+    The code has been tested with Python 3.8.7 and tensorflow 2.3.1
 
     Note: This code will NOT work on the supercomputers.