pytorch

Link to section 'Description' of 'pytorch' Description

PyTorch is a GPU accelerated tensor computational framework with a Python front end. Functionality can be easily extended with common Python libraries such as NumPy, SciPy, and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility and speed as a deep learning framework and provides accelerated NumPy-like functionality.

Link to section 'Versions' of 'pytorch' Versions

Scholar: 20.02-py3, 20.03-py3, 20.06-py3, 20.11-py3, 20.12-py3, 21.06-py3, 21.09-py3
Gilbreth: 20.02-py3, 20.03-py3, 20.06-py3, 20.11-py3, 20.12-py3, 21.06-py3, 21.09-py3
Anvil: 20.02-py3, 20.03-py3, 20.06-py3, 20.11-py3, 20.12-py3, 21.06-py3, 21.09-py3

Link to section 'Module' of 'pytorch' Module

You can load the modules by:

module load ngc
module load pytorch

Link to section 'Example job' of 'pytorch' Example job

Using #!/bin/sh -l as shebang in the slurm job script will cause the failure of some biocontainer modules. Please use #!/bin/bash instead.

To run pytorch on our clusters:

#!/bin/bash
#SBATCH -A myallocation     # Allocation name
#SBATCH -t 1:00:00
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -c 8
#SBATCH --gpus-per-node=1
#SBATCH --job-name=pytorch
#SBATCH --mail-type=FAIL,BEGIN,END
#SBATCH --error=%x-%J-%u.err
#SBATCH --output=%x-%J-%u.out

module --force purge
ml ngc pytorch