RFdiffusion学习笔记
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2024-11-20
RFdiffusion学习笔记
RFdiffusion介绍
TODO
RFdiffusion安装
1. 安装准备工作
git下载:
git clone https://github.com/RosettaCommons/RFdiffusion.git
进入目录并下载模型的weights:
cd RFdiffusion
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
2. 通过docker执行
由于我安装的CUDA版本为12.1,只能采用docker版本,具体方法如下:
构建镜像
docker build -f docker/Dockerfile -t rfdiffusion .
由于numpy已经更新,需要对numpy进行降级,可以使用如下:
# 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 \
numpy==1.26.4 \
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"]
RFdiffusion使用
因为我安装的是docker版本,这里简单介绍docker版本的使用,如下:
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]'
使用过程可能会报如下错误:
could not select device driver “” with capabilities: [[gpu]].
主要是没有安装nvida-container,官方详细说明可以看此连接
简单安装如下:
- 加入包索引
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
- 安装
sudo apt-get update sudo apt-get install -y nvidia-container-toolkit
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