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PyTorch is a Python package that provides two high-level features:

  • Tensor computation (like NumPy) with strong GPU acceleration
  • Deep neural networks built on a tape-based autograd system

PyTorch has a distant connection with Torch, but for all practical purposes you can treat them as separate packages.


Latest available wheels

To see the latest version of PyTorch that we have built:

[name@server ~]$ avail_wheels "torch*"

For more information on listing wheels, see listing available wheels.

Installing Compute Canada wheel

The preferred option is to install it using the Python wheel as follows:

1. Load a Python module, either python/2.7, python/3.5, python/3.6 or python/3.7
2. Create and start a virtual environment.
3. Install PyTorch in the virtual environment with pip install.


(venv) [name@server ~] pip install torch --no-index


In addition to torch, you can install torchvision, torchtext and torchaudio:

(venv) [name@server ~] pip install torch torchvision torchtext torchaudio --no-index

libtorch is included in the wheel. Once Pytorch is installed in a virtual environment, you can find it at: $VIRTUAL_ENV/lib/python3.6/site-packages/torch/lib/

Job submission

Here is an example of a job submission script using the python wheel, with a virtual environment inside a job:

File :

#SBATCH --gres=gpu:1       # Request GPU "generic resources"
#SBATCH --cpus-per-task=6  # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
#SBATCH --mem=32000M       # Memory proportional to GPUs: 32000 Cedar, 64000 Graham.
#SBATCH --time=0-03:00
#SBATCH --output=%N-%j.out

module load python/3.6
virtualenv --no-download $SLURM_TMPDIR/env
source $SLURM_TMPDIR/env/bin/activate
pip install torch --no-index


The Python script has the form

File :

import torch
x = torch.Tensor(5, 3)
y = torch.rand(5, 3)
# let us run the following only if CUDA is available
if torch.cuda.is_available():
    x = x.cuda()
    y = y.cuda()
    print(x + y)

You can then submit a PyTorch job with:

[name@server ~]$ sbatch


This section gives ResNet-18 benchmark results on different clusters with various configurations.

All numbers are images per second per GPU, using DistributedDataParallel and NCCL.

These results are provisional and there is a lot of variance in their measurement. Work is being done to get a clearer picture.

Graham[P100], images per second per GPU
Batch Size 1 Node, 1 GPU (baseline) 1 Node, 2 GPUs 2 * (1 Node, 2 GPUs) 3 * (1 Node, 2 GPUs)
32 542 134 103 82
64 620 190 149 134
128 646 241 197 180
256 587 263 184 368


Memory leak

On AVX512 hardware (Béluga, Skylake or V100 nodes), older versions of Pytorch (less than v1.0.1) using older libraries (cuDNN < v7.5 or MAGMA < v2.5) may considerably leak memory resulting in an out-of-memory exception and death of your tasks. Please upgrade to the latest torch version.