Stampede2 setup
Published:
Stampede2 is a supercomputers cluster at the Texas Advanced Computing Center (TACC). Our group has resource allocation to use the supercomputing resource.
Set up RFdiffusion to run on Stampede2
git clone repo
git clone https://github.com/RosettaCommons/RFdiffusion.git
install models
mkdir models && cd models
wget http://files.ipd.uw.edu/pub/RFdiffusion/6f5902ac237024bdd0c176cb93063dc4/Base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/e29311f6f1bf1af907f9ef9f44b8328b/Complex_base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/60f09a193fb5e5ccdc4980417708dbab/Complex_Fold_base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/74f51cfb8b440f50d70878e05361d8f0/InpaintSeq_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/76d00716416567174cdb7ca96e208296/InpaintSeq_Fold_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/5532d2e1f3a4738decd58b19d633b3c3/ActiveSite_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/12fc204edeae5b57713c5ad7dcb97d39/Base_epoch8_ckpt.pt
# Optional:
wget http://files.ipd.uw.edu/pub/RFdiffusion/f572d396fae9206628714fb2ce00f72e/Complex_beta_ckpt.pt
# original structure prediction weights
wget http://files.ipd.uw.edu/pub/RFdiffusion/1befcb9b28e2f778f53d47f18b7597fa/RF_structure_prediction_weights.pt
install miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
# install to work2 directory instead of home
# yes to init, which modifies bashrc, but stampede2 sources bash_profile so that doesn't really do anything
install SE3Transformer environment
conda env create -f env/SE3nv.yml
conda activate SE3nv
cd env/SE3Transformer
pip install --no-cache-dir -r requirements.txt
python3 setup.py install
cd ../.. # change into the root directory of the repository
pip install -e . # install the rfdiffusion module from the root of the repository
running RFdifussion jobs
#!/bin/bash
#SBATCH -J rfdiffusion
#SBATCH -p skx-dev
#SBATCH -N 1
#SBATCH -n 4
#SBATCH -t 02:00:00
#SBATCH -A Allocation
#SBATCH --mail-user=email
#SBATCH --mail-type=all
cd $SLURM_SUBMIT_DIR
conda activate SE3nv
./scripts/run_inference.py \
inference.output_prefix=temp/example_design_enzyme \
inference.input_pdb=examples/input_pdbs/5an7.pdb \
'contigmap.contigs=[10-100/A1083-1083/10-100/A1051-1051/10-100/A1180-1180/10-100]' \
potentials.guide_scale=1 \
'potentials.guiding_potentials=["type:substrate_contacts,s:1,r_0:8,rep_r_0:5.0,rep_s:2,rep_r_min:1"]' \
potentials.substrate=LLK inference.ckpt_override_path=./models/ActiveSite_ckpt.pt \
>& log_example_enzyme_design.txt < /dev/null
Note: tried 4 MPI and 48 MPI tasks, same speed