TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

About TensorFlow on Bede


GPU must be requested in order to enable GPU acceleration by adding the flag e.g. --gpus=1 to the scheduler command or job script. See Running and Scheduling Tasks on Bede for more information.

Bede has a locally installed IBM Watson Machine Learning Community Edition (WML CE) Anaconda channel that provides versions of Tensorflow, PyTorch and their dependencies especially built for the POWER architecture.

Installation in Home Directory - GPU Version

First request an interactive session, e.g. see Running and Scheduling Tasks on Bede.

Then GPU version of TensorFlow can be installed by the following

# Load the conda module
module load Anaconda3/2020.02

# Adds the local WML CE channel to the conda search path
conda config --prepend channels file:///opt/software/apps/ibm_wmlce/wmlce-1.7.0-mirror/

# Create an conda virtual environment e.g. named 'tensorflow'
conda create -n tensorflow python=3.6

# Activate the 'tensorflow' environment
source activate tensorflow

# Install GPU version of TensorFlow
conda install tensorflow

To install a version of tensorflow other than the latest version you should specify a version number when running conda install i.e.

pip install tensorflow=<version_number>

To search for available versions use conda search i.e.

conda search tensorflow

Every Session Afterwards and in Your Job Scripts

Every time you use a new session or within your job scripts, the modules must be loaded and Conda must be activated again. Use the following command to activate the Conda environment with TensorFlow installed:

module load Anaconda3/2020.02
source activate tensorflow

Testing your TensorFlow installation

You can test that TensorFlow is running on the GPU with the following python code

import tensorflow as tf
# Creates a graph
#If using CPU, replace /device:GPU:0 with /cpu:0
with tf.device('/device:GPU:0'):
   a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
   b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
   c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.

Which should give the following results:

[[ 22.  28.]
 [ 49.  64.]]