PyTorch

Machine learning framework for Python.

News

17.11.2023 PyTorch 2.1 added to Puhti, Mahti and LUMI. Horovod has been removed, we recommend using PyTorch DDP instead. PyTorch Geometric was added to the Puhti and Mahti version.

5.10.2022 Due to Puhti's update to Red Hat Enterprise Linux 8 (RHEL8), the number of fully supported PyTorch versions has been reduced. Previously deprecated conda-based versions have been removed. Please contact our servicedesk if you really need access to older versions.

5.5.2022 Due to Mahti's update to Red Hat Enterprise Linux 8 (RHEL8), the number of fully supported PyTorch versions has been reduced. Please contact our servicedesk if you really need access to other versions.

4.2.2022 All old PyTorch versions which were based on direct Conda installations have been deprecated, and we encourage users to move to newer versions. Read more on our separate Conda deprecation page.

Available

Currently supported PyTorch versions:

Version	Module	Puhti	Mahti	LUMI	Notes
2.1.1	`pytorch/2.1`	-	-	X	default version
2.1.0	`pytorch/2.1	X	X	-	default version
2.0.1	`pytorch/2.0`	-	-	X
2.0.0	`pytorch/2.0`	X	X	-
1.13.1	`pytorch/1.13`	-	-	X	limited multi-node support
1.13.0	`pytorch/1.13`	X	X	-
1.12.0	`pytorch/1.12`	X	X	-
1.11.0	`pytorch/1.11`	X	X	-
1.10.0	`pytorch/1.10`	(x)	(x)	-
1.9.0	`pytorch/1.9`	(x)	(x)	-
1.8.1	`pytorch/1.8`	(x)	(x)	-
1.7.1	`pytorch/1.7`	(x)	-	-

Includes PyTorch and related libraries with GPU support via CUDA/ROCm.

Versions marked with "(x)" are based on old Red Hat Enterprise Linux 7 (RHEL7) images, and are no longer fully supported. In particular MPI and Horovod are not expected to work anymore with these modules. If you still wish to access these versions, you need to enable old RHEL7 modules by module use /appl/soft/ai/rhel7/modulefiles/.

If you find that some package is missing, you can often install it yourself with pip install --user. See our Python documentation for more information on how to install packages yourself. If you think that some important PyTorch-related package should be included in the module provided by CSC, please contact our servicedesk.

It is also possible to use Python virtual environments. To create a virtual environment use the command python3 -m venv --system-site-packages venv.

All modules are based on containers using Apptainer (previously known as Singularity). Wrapper scripts have been provided so that common commands such as python, python3, pip and pip3 should work as normal. For other commands, you need to prefix them with apptainer_wrapper exec, for example apptainer_wrapper exec huggingface-cli. For more information, see CSC's general instructions on how to run Apptainer containers.

New users

If you are new to using machine learning on CSC's supercomputers, please read our new tutorial Getting started with machine learning at CSC, which covers how to run a simple PyTorch project on Puhti using the web user interface.

License

PyTorch is BSD-style licensed, as found in the LICENSE file.

Usage

To use the default version of PyTorch on Puhti or Mahti, initialize it with:

module load pytorch

To access PyTorch on LUMI:

module use /appl/local/csc/modulefiles/
module load pytorch

Note that LUMI versions are still considered experimental with limited support. They are still subject to change at any time without notice, and for example multi-node jobs are know not to work properly yet.

If you wish to have a specific version (see above for available versions), use:

module load pytorch/1.13

Please note that the module already includes CUDA and cuDNN libraries, so there is no need to load cuda and cudnn modules separately!

This command will also show all available versions:

module avail pytorch

To check the exact packages and versions included in the loaded module you can run:

list-packages

Warning

Note that login nodes are not intended for heavy computing, please use slurm batch jobs instead. See our instructions on how to use the batch job system.

Example batch script

Example batch script for reserving one GPU and a corresponding proportion of the available CPU cores in a single node:

PuhtiMahtiLUMI

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpu
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --mem=80G
#SBATCH --time=1:00:00
#SBATCH --gres=gpu:v100:1

module load pytorch/2.0
srun python3 myprog.py <options>

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpusmall
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --time=1:00:00
#SBATCH --gres=gpu:a100:1

module load pytorch/2.0
srun python3 myprog.py <options>

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small-g
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=7
#SBATCH --gpus-per-node=1
#SBATCH --mem=60G
#SBATCH --time=1:00:00

module use /appl/local/csc/modulefiles/
module load pytorch/2.0
srun python3 myprog.py <options>

Please read the section on Efficient GPU utilization in our Machine learning guide to learn how to use the GPU efficiently.

Big datasets, multi-GPU and multi-node jobs

If you are working with big datasets, or datasets that contain a lot of files, please read the data section of our Machine learning guide. In particular, please do not read a huge number of files from the shared file system, use fast local disk or package your data into larger files instead!

For multi-GPU and multi-node jobs we recommend using the PyTorch Distributed Data-Parallel framework. You can read more about this and find examples of how to use PyTorch DDP on CSC's supercomputers in the Multi-GPU and multi-node section of our Machine learning guide