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Sam Reeve edited this page Oct 19, 2021 · 14 revisions

Detailed PyTorch installation instructions

Simple dependency installation instructions in the README

For installing a specific PyTorch version on a machine that has only CPU:

pip3 install torch==1.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
pip3 install torch-scatter==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip3 install torch-sparse==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip3 install torch-cluster==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip3 install torch-spline-conv==latest+cpu -f https://pytorch-geometric.com/whl/torch-1.6.0.html
pip3 install torch-geometric

For installing on a machine that has GPUs and PyTorch already installed:

pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric

where ${CUDA} and ${TORCH} should be replaced by your specific CUDA version (cpu, cu92, cu101, cu102, cu110) and PyTorch version (1.4.0, 1.5.0, 1.6.0, 1.7.0)

ORNL CADES

  1. Get an interactive job from a queue with GPUs, e.g., gpu_p100 
salloc -A ccsd -p gpu_p100 -N 1 -n1 -c1 -G1 --mem=0G -t 01:00:00 /bin/bash
  1. Set up a python virtual environment called name-env and save it in directory subdir/ 
python3 -m venv subdir/name-env
  1. Activate the environment name-env 
source subdir/name-env/bin/activate

2.1 load PE-gnu for MPI

module load PE-gnu/3.0
  1. Install torch packages with CUDA support (you can customize your own versions) 
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html

Installation done! Next, time to run the code.

ORNL SUMMIT

Installation has been done already (non-trivial for IBM/NVIDIA machine) - skip to run instructions.

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