TensorFlow

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, the simplest method is to install TensorFlow using the Open-CE Conda distribution.

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:

Note

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__)"

Note

The Open-CE distribution of TensorFlow does not include IBM technologies such as DDL or LMS, which were previously available via WMLCE. WMLCE is not supported on RHEL 8.

Further Information

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