3 NVIDIA Grace-Hopper nodes (GH200 480) are now available. See Using Bede for more information.


TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

TensorFlow can be installed through a number of python package managers such as Conda or pip.

For use on Bede’s ppc64le nodes, the simplest method is to install TensorFlow using the Open-CE Conda distribution.

For the aarch64 nodes, using a NVIDIA provided NGC Tensorflow container is likely preferred.

Installing via Conda (Open-CE)#

With a working Conda installation (see Installing Miniconda) the following instructions can be used to create a Python 3.8 conda environment named tf-env with the latest Open-CE provided TensorFlow:


TensorFlow installations via conda can be relatively large. Consider installing your miniconda (and therfore your conda environments) to the /nobackup file store.

# Create a new conda environment named tf-env within your conda installation
conda create -y --name tf-env python=3.8

# Activate the conda environment
conda activate tf-env

# Add the OSU Open-CE conda channel to the current environment config
conda config --env --prepend channels https://ftp.osuosl.org/pub/open-ce/current/

# Also use strict channel priority
conda config --env --set channel_priority strict

# Install the latest available version of Tensorflow
conda install -y tensorflow

In subsequent interactive sessions, and when submitting batch jobs which use TensorFlow, you will then need to re-activate the conda environment.

For example, to verify that TensorFlow is available and print the version:

# Activate the conda environment
conda activate tf-env

# Invoke python
python3 -c "import tensorflow;print(tensorflow.__version__)"


The Open-CE distribution of TensorFlow does not include IBM technologies such as DDL or LMS, which were previously available via WMLCE. WMLCE is no longer supported.

Using NGC TensorFlow Containers#


NVIDIA do not provide ppc64le containers for TensorFlow through NGC. This method should only be used for aarch64 partitions.

Further Information#

For further information on TensorFlow features and usage, please refer to the TensorFlow Documentation.