本家様 https://github.com/dauparas/ProteinMPNN
"ディープラーニングに基づくタンパク質配列設計法"

pyenv-anacondaの設置

[root@rockylinux9 ~]# git clone https://github.com/yyuu/pyenv.git /apps/pyenv
[root@rockylinux9 ~]# export PYENV_ROOT=/apps/pyenv
[root@rockylinux9 ~]# export PATH=$PYENV_ROOT/bin:$PATH
[root@rockylinux9 ~]# pyenv install anaconda3-2023.03
[root@rockylinux9 ~]# pyenv global anaconda3-2023.03
[root@rockylinux9 ~]# pyenv versions
  system
* anaconda3-2023.03 (set by /apps/pyenv/version)
 
[root@rockylinux9 ~]#
[root@rockylinux9 ~]# export PATH=$PYENV_ROOT/versions/anaconda3-2023.03/bin/:$PATH
 
[root@rockylinux9 ~]# conda update conda

既にpyenv-anaconda環境があるのなら

export PYENV_ROOT=/apps/pyenv
export PATH=$PYENV_ROOT/bin:$PATH
export PATH=$PYENV_ROOT/versions/anaconda3-2023.03/bin/:$PATH

ProteinMPNN の実行環境を作成

ProteinMPNNのアプリ構築ではなく、その実行環境の整備のみです.

[root@rockylinux9 ~]# conda create -n mlfold
 
[root@rockylinux9 ~]# source activate mlfold
(mlfold) [root@rockylinux9 ~]# conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
 
(mlfold) [root@rockylinux9 ~]# conda list
 :
cuda-runtime              11.7.1                        0    nvidia
 :
python                    3.10.10              h7a1cb2a_2
pytorch                   2.0.0           py3.10_cuda11.7_cudnn8.5.0_0    pytorch
pytorch-cuda              11.7                 h778d358_3    pytorch
 :
(mlfold) [root@rockylinux9 ~]# conda deactivate
 
[root@rockylinux9 ~]#

Environment Modules

[root@rockylinux9 ~]# vi /apps/modulefiles/mlfold
#%Module
set          root /apps/pyenv/versions/anaconda3-2023.03/envs/mlfold
prepend-path PATH $root/bin
 
[root@rockylinux9 ~]#

テスト

[saber@rockylinux9 ~]$ module load mlfold
 
[saber@rockylinux9 ~]$ mkdir test && cd test
 
[saber@rockylinux9 test]$ git clone https://github.com/dauparas/ProteinMPNN
[saber@rockylinux9 test]$ cd ProteinMPNN
 
 
[saber@rockylinux9 ProteinMPNN]$ cd examples/
[saber@rockylinux9 examples]$ ls -l
total 48
-rw-r--r-- 1 osanai em  690 Apr 17 03:17 submit_example_1.sh
-rw-r--r-- 1 osanai em  987 Apr 17 03:17 submit_example_2.sh
-rw-r--r-- 1 osanai em  772 Apr 17 03:17 submit_example_3_score_only_from_fasta.sh
-rw-r--r-- 1 osanai em  614 Apr 17 03:17 submit_example_3_score_only.sh
-rw-r--r-- 1 osanai em  577 Apr 17 03:17 submit_example_3.sh
-rw-r--r-- 1 osanai em 1552 Apr 17 03:17 submit_example_4_non_fixed.sh
-rw-r--r-- 1 osanai em 1558 Apr 17 03:17 submit_example_4.sh
-rw-r--r-- 1 osanai em 1826 Apr 17 03:17 submit_example_5.sh
-rw-r--r-- 1 osanai em 1006 Apr 17 03:17 submit_example_6.sh
-rw-r--r-- 1 osanai em  729 Apr 17 03:17 submit_example_7.sh
-rw-r--r-- 1 osanai em 1042 Apr 17 03:17 submit_example_8.sh
-rw-r--r-- 1 osanai em 1917 Apr 17 03:17 submit_example_pssm.sh
 
[saber@rockylinux9 examples]$ cat submit_example_1.sh
#!/bin/bash
#SBATCH -p gpu
#SBATCH --mem=32g
#SBATCH --gres=gpu:rtx2080:1
#SBATCH -c 2
#SBATCH --output=example_1.out
 
# source activate mlfold     <-- ここを無効化
 
folder_with_pdbs="../inputs/PDB_monomers/pdbs/"
 
output_dir="../outputs/example_1_outputs"
if [ ! -d $output_dir ]
then
    mkdir -p $output_dir
fi
 
path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
 
python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
 
python ../protein_mpnn_run.py \
        --jsonl_path $path_for_parsed_chains \
        --out_folder $output_dir \
        --num_seq_per_target 2 \
        --sampling_temp "0.1" \
        --seed 37 \
        --batch_size 1
 
[saber@rockylinux9 examples]$
 
[saber@rockylinux9 examples]$ bash ./submit_example_1.sh
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Last-modified: 2023-04-17 (月) 03:25:56