pytorch with cuda pytorch geometric SU2 solver: swig --> for python wrapper openmpi --> mpi pyvista --> geometry manipulation tqdm --> progress indicator in for loop
###########install pytorch and pytorch geometric######################################## #create conda enviroment conda create -n cfd-gcn python=3.7 conda activate cfd-gcn conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.1 -c pytorch conda install pyg -c pyg #Install torch_geometric env PATH=/usr/local/cuda/bin:$PATH env CPATH=/usr/local/cuda/include:$CPATH env LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH pip install torch-scatter==2.0.5 -f https://pytorch-geometric.com/whl/torch-1.5.0+cu101.html pip install torch-sparse==0.6.6 -f https://pytorch-geometric.com/whl/torch-1.5.0+cu101.html pip install torch-cluster==1.5.5 -f https://pytorch-geometric.com/whl/torch-1.5.0+cu101.html pip install torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-1.5.0+cu101.html pip install torch-geometric==1.6.0 -f https://pytorch-geometric.com/whl/torch-1.5.0+cu101.html
#assuming your in the current path = your-own-path apt-get update -y && apt-get install -y openmpi-bin libopenmpi-dev swig m4 env MPICC=/usr/bin/mpicc pip install mpi4py export PATH=/usr/local/cuda/bin:$PATH export CPATH=/usr/local/cuda/include:$CPATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export CXXFLAGS="-O3" git clone --branch feature_pytorch_communicator https://github.com/su2code/SU2 mkdir su2install cd SU2 ./preconfigure.py --enable-mpi --with-cc=/usr/bin/mpicc --with-cxx=/usr/bin/mpicxx --prefix=your-own-path/su2install/SU2 --enable-autodiff --enable-PY_WRAPPER --disable-tecio --update sudo make -j 16 Install #add this to ~/.bashrc export PATH=$PATH:your-own-path/su2install/SU2/bin export PYTHONPATH=$PYTHONPATH:your-own-path/su2install/SU2/bin export SU2_RUN=your-own-path/su2install/SU2/bin export SU2_HOME=your-own-path/SU2
#activate the conda enviroment cfd-gcn git clone https://github.com/shanjierenyidp/CFD_GCN.git cd CFD-GCN conda activate cfd-gcn #run this to convert simulation to graphs cd project-fold-path python data.py --root './data/cases' -d './data/dataset0' -fm './data/aorta3.su2' -ts 2000
#you need to download the data from google drive via this link, please download it and unzip it as data folder. https://drive.google.com/file/d/1VTnuxaQXppp2mKpLFD9KJ86RL1zCWYQf/view?usp=sharing
#then start runing the experiments: mpirun -np 5 python train.py > log #note that the np = 5 = batch_size + 1