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


PyTorch is an end-to-end machine learning framework. PyTorch enables fast, flexible experimentation and efficient production through a user-friendly front-end, distributed training, and ecosystem of tools and libraries.

The main method of distribution for PyTorch for ppc64le is via Conda, with Open-CE providing a simple method for installing multiple machine learning frameworks into a single conda environment.

The upstream Conda and pip distributions do not provide ppc64le pytorch packages at this time.

Installing via Conda#

With a working Conda installation (see Installing Miniconda) the following instructions can be used to create a Python 3.9 conda environment named torch with the latest Open-CE provided PyTorch:


Pytorch 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 torch within your conda installation
conda create -y --name torch python=3.9

# Activate the conda environment
conda activate torch

# 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 PyTorch
conda install -y pytorch

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

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

# Activate the conda environment
conda activate torch

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

Installation via the upstream Conda channel is not currently possible, due to the lack of ppc64le or noarch distributions.


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

Using NGC PyTorch Containers#


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

Further Information#

For more information on the usage of PyTorch, see the Online Documentation.