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.
Publisher/Vendor/Repository and License Type
https://pytorch.org, Open source.
Loading PyTorch from Module
A basic conda environment with PyTorch is available through the module system:
module load pytorch/2.4.0 module unload pytorch/2.4.0
The basic environment includes: pytorch, transformers, flash attention, accelerate, lightning, deepspeed, diffusers, and megatron.
Cloning PyTorch Environment
For extending the basic conda with project- or lab-specific packages, we encourage users to clone the basic environment to their project space:
module load miniconda3/24.1.2-py310 conda create --prefix /fs/project/your_project_code/your_username/your_project_name --clone /apps/pytorch/2.4.0
Then, users can install packages in the new cloned conda environment. See HOWTO: Create and Manage Python Environments.
Installing PyTorch Locally
For alternative versions of PyTorch, users are able to create their own conda environments and install locally. We are also 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.
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 miniconda3/24.1.2-py310 cuda/12.3.0 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 torch torchvision
PyTorch is now installed into your $HOME/local directory using the local install directory hierarchy described here and can be tested via:
module load miniconda3/24.1.2-py310 cuda/12.3.0 ; 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() )
exit
EOF
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.
AN EXAMPLE BATCH SCRIPT TEMPLTE
Below is an example batch script (job.sh) for using PyTorch (Slurm syntax).
Contents of job.sh
#!/bin/bash #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 cd $SLURM_SUBMIT_DIR module load miniconda3/24.1.2-py310 source activate your-local-python-environment-name python your-pytorch-script.py
In order to run it via the batch system, submit the job.sh file with the following command:
sbatch job.sh
GPU Usage
- GPU Usage: PyTorch can be ran on a GPU for signifcant performace improvements. See HOWTO: Use GPU with Tensorflow and PyTorch
- Distributed Training: torch.distributed includes data and model parallelization to increase training speed and overcome GPU memory limitations. See HOWTO: Pytorch Distributed Data Parallel (DDP) and HOWTO: Fully Sharded Data Parallel (FSDP).
- 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.
Jupyter Usage
PyTorch is available to be loaded as a kernel in a Jupyter notebook when running on Pitzer, Cardinal, and Ascend clusters. See HOWTO: Use Jupyter on OnDemand for details. Be sure to request GPU resources when starting your Jupyter session if you want GPU acceleration.
