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# Usage:
# git clone https://github.com/RosettaCommons/RFdiffusion.git
# cd RFdiffusion
# docker build -f docker/Dockerfile -t rfdiffusion .
# mkdir $HOME/inputs $HOME/outputs $HOME/models
# bash scripts/download_models.sh $HOME/models
# wget -P $HOME/inputs https://files.rcsb.org/view/5TPN.pdb
# docker run -it --rm --gpus all \
# -v $HOME/models:$HOME/models \
# -v $HOME/inputs:$HOME/inputs \
# -v $HOME/outputs:$HOME/outputs \
# rfdiffusion \
# inference.output_prefix=$HOME/outputs/motifscaffolding \
# inference.model_directory_path=$HOME/models \
# inference.input_pdb=$HOME/inputs/5TPN.pdb \
# inference.num_designs=3 \
# 'contigmap.contigs=[10-40/A163-181/10-40]'
FROM nvcr.io/nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04
COPY . /app/RFdiffusion/
RUN apt-get -q update \
&& DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
git \
python3.9 \
python3-pip \
&& python3.9 -m pip install -q -U --no-cache-dir pip \
&& rm -rf /var/lib/apt/lists/* \
&& apt-get autoremove -y \
&& apt-get clean \
&& pip install -q --no-cache-dir \
dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html \
torch==1.12.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 \
e3nn==0.3.3 \
wandb==0.12.0 \
pynvml==11.0.0 \
git+https://github.com/NVIDIA/dllogger#egg=dllogger \
decorator==5.1.0 \
hydra-core==1.3.2 \
pyrsistent==0.19.3 \
/app/RFdiffusion/env/SE3Transformer \
&& pip install --no-cache-dir /app/RFdiffusion --no-deps
WORKDIR /app/RFdiffusion
ENV DGLBACKEND="pytorch"
ENTRYPOINT ["python3.9", "scripts/run_inference.py"]
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