PyTorch is an open source machine learning framework with GPU acceleration and deep neural networks that is based on the automatic differentiation in the Torch library of tensors.

If you installed PyTorch-nightly on Linux via pip between December 25, 2022 and December 30, 2022, please uninstall it and torchtriton immediately, and use the latest nightly binaries (newer than Dec 30th 2022). See this post page from PyTorch for detailed information. 


OSC does not provide general access to PyTorch.  However, we are available to assist with the configuration of local individual/research-group installations on all our clusters.  If you have any questions, please contact OSC Help.

Installing PyTorch Locally

Here is a quick recipe that was used in February 2022 to install a GPU enabled version compatible with the CUDA drivers on the clusters at that time:

module load python/3.7-2019.10  cuda/11.1.1
; module list ;
conda create -n pytorch
; source activate pytorch
; pip install -t ~/local/pytorch torch==1.10.2+cu111 torchvision==0.11.3+cu111 -f

This uses a local install directory hierarchy described here and can be tested via:

module load python/3.7-2019.10 cuda/11.1.1
; module list ;
source activate pytorch
python <<EOF
import torch

x = torch.rand(5, 3)
print("torch.rand(5, 3) =", x)

print( "Is cuda available =", torch.cuda.is_available() )


Here is an older recipe with explanations.

Create a local python environment (e.g., pytorch-test) for Pytorch installation:

Load a python module:

module load python/3.6-conda5.2

Create a local python environment (e.g., pytorch-test) for Pytorch installation:

conda create -n pytorch-test python=3.6 anaconda

Activate the environment:

source activate pytorch-test

Install Pytorch:

pip install torch torchvision

Please refer here if you want a different version of the Pytorch.

Please follow this link for more information.

Feel free to contact OSC Help if you have any issues with installation.

Publisher/Vendor/Repository and License Type, Open source.

Usage on Owens

Usage on Owens

Batch Usage on Owens

Batch jobs can request multiple nodes/cores and compute time up to the limits of the OSC systems. Refer to Queues and Reservations for Owens, and Scheduling Policies and Limits for more info.  In particular, Pytorch should be run on a GPU-enabled compute node.

An Example Batch Script Templte

Below is an example batch script (job.pbs) for using PyTorch.

Contents of job.pbs

#PBS -N pytorch
#PBS -l nodes=1:ppn=28:gpus=1:default
#PBS -l walltime=30:00
#PBS -j oe
#PBS -S /bin/bash 
#PBS -A yourprojectID

module load python/3.6 cuda
source activate your-local-python-environment-name

In order to run it via the batch system, submit the job.pbs  file with the following command:

qsub job.pbs

Further Reading

PyTorch Homepage

Fields of Science: