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.

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

Installing PyTorch Locally

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

Load the correct python and cuda modules:

module load miniconda3/4.10.3-py37  cuda/11.8.0
module list
Create a python environment to install pytorch into:
conda create -n pytorch
Activate the conda environment:
source activate pytorch
Install the specific version of pytorch:
pip3 install -t ~/local/pytorch torch torchvision torchaudio --index-url

PyTorch is now installed into your $HOME/local directory using the local install directory hierarchy described here and can be tested via:

module load miniconda3/4.10.3-py37 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() )

If testing for a GPU you will need to submit the above script as a batch job (make sure to request a GPU for the job, see Job Scripts for more info on requesting GPU)

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

Batch Usage

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.


Below is an example batch script ( for using PyTorch (Slurm syntax).

Contents of

#SBATCH --job-name=pytorch
#SBATCH --nodes=1 --ntasks-per-node=28 --gpus_per_node=1 --gpu_cmode=shared
#SBATCH --time=30:00
#SBATCH --account=yourprojectID


module load miniconda3

source activate your-local-python-environment-name


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


GPU Usage

  • GPU Usage: PyTorch can be ran on a GPU for signifcant performace improvements. See HOWTO: Use GPU with Tensorflow and PyTorch
  • Horovod: If you are using PyTorch with a GPU you may want to also consider using Horovod. Horovod will take single-GPU training scripts and scale it to train across many GPUs in parallel.


Further Reading

PyTorch Homepage

Fields of Science: