From efc3bd4a90deb407af59a951e44a83f56993f6ed Mon Sep 17 00:00:00 2001
From: Fahad Khalid <f.khalid@fz-juelich.de>
Date: Tue, 4 May 2021 08:32:43 +0200
Subject: [PATCH] Updated the course material so that the examples comply with
TF2.
---
.../examples/mnist_epoch_distributed.py | 17 ++++++++---------
course_material/examples/mnist_single_gpu.py | 13 ++++++-------
2 files changed, 14 insertions(+), 16 deletions(-)
diff --git a/course_material/examples/mnist_epoch_distributed.py b/course_material/examples/mnist_epoch_distributed.py
index 7c9080e..504b2a8 100644
--- a/course_material/examples/mnist_epoch_distributed.py
+++ b/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
+ 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
diff --git a/course_material/examples/mnist_single_gpu.py b/course_material/examples/mnist_single_gpu.py
index 794150f..2918cd0 100644
--- a/course_material/examples/mnist_single_gpu.py
+++ b/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
+ 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.
--
GitLab