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#author("2026-06-13T14:49:03+00:00","default:sysosa","sysosa") #author("2026-06-13T15:46:45+00:00","default:sysosa","sysosa") 本家様 [[https://github.com/bytedance/Protenix>+https://github.com/bytedance/Protenix]] pipy [[https://pypi.org/project/protenix/>+https://pypi.org/project/protenix/]] Toward High-Accuracy Open-Source Biomolecular Structure Prediction. pipyのサイトからPython >=3.11みたい ***pyenv-anacondaの設置 [#y442193c] #code(nonumber){{ git clone https://github.com/yyuu/pyenv.git /apps/pyenv export PYENV_ROOT=/apps/pyenv export PATH=$PYENV_ROOT/bin:$PATH pyenv install miniforge3-26.3.2-1 source /apps/pyenv/versions/miniforge3-26.3.2-1/etc/profile.d/conda.sh conda update conda (既に環境があるなら) source /apps/pyenv/versions/miniforge3-26.3.2-1/etc/profile.d/conda.sh }} ***Protenix のインストール [#h2fb44b7] #code(nonumber){{ [root@r9 ~]# conda create -n protenix python=3.11 pip [root@r9 ~]# conda activate protenix (protenix) [root@r9 ~]# pip install protenix (protenix) [root@r9 ~]# conda list # packages in environment at /apps/pyenv/versions/miniforge3-26.3.2-1/envs/protenix: # # Name Version Build Channel _openmp_mutex 4.5 20_gnu conda-forge absl-py 2.4.0 pypi_0 pypi annotated-types 0.7.0 pypi_0 pypi asttokens 3.0.1 pypi_0 pypi biopython 1.85 pypi_0 pypi biotite 1.4.0 pypi_0 pypi biotraj 1.2.2 pypi_0 pypi bzip2 1.0.8 hda65f42_9 conda-forge ca-certificates 2026.5.20 hbd8a1cb_0 conda-forge certifi 2026.5.20 pypi_0 pypi charset-normalizer 3.4.7 pypi_0 pypi click 8.4.1 pypi_0 pypi colorama 0.4.6 pypi_0 pypi comm 0.2.3 pypi_0 pypi contourpy 1.3.3 pypi_0 pypi cuequivariance 0.10.0 pypi_0 pypi cuequivariance-ops-cu12 0.8.0 pypi_0 pypi cuequivariance-ops-torch-cu12 0.8.0 pypi_0 pypi cuequivariance-torch 0.8.0 pypi_0 pypi cycler 0.12.1 pypi_0 pypi decorator 5.3.1 pypi_0 pypi deepspeed 0.17.5 pypi_0 pypi einops 0.8.2 pypi_0 pypi executing 2.2.1 pypi_0 pypi fair-esm 2.0.0 pypi_0 pypi filelock 3.29.3 pypi_0 pypi fonttools 4.63.0 pypi_0 pypi fsspec 2026.4.0 pypi_0 pypi gemmi 0.6.7 pypi_0 pypi gitdb 4.0.12 pypi_0 pypi gitpython 3.1.50 pypi_0 pypi hjson 3.1.0 pypi_0 pypi icecream 2.1.7 pypi_0 pypi idna 3.18 pypi_0 pypi ihm 2.11 pypi_0 pypi ipdb 0.13.13 pypi_0 pypi ipython 9.14.1 pypi_0 pypi ipython-pygments-lexers 1.1.1 pypi_0 pypi ipywidgets 8.1.7 pypi_0 pypi jedi 0.20.0 pypi_0 pypi jinja2 3.1.6 pypi_0 pypi joblib 1.5.3 pypi_0 pypi jupyterlab-widgets 3.0.16 pypi_0 pypi kiwisolver 1.5.0 pypi_0 pypi ld_impl_linux-64 2.45.1 default_hbd61a6d_102 conda-forge libexpat 2.8.1 hecca717_1 conda-forge libffi 3.5.2 h3435931_0 conda-forge libgcc 15.2.0 he0feb66_19 conda-forge libgcc-ng 15.2.0 h69a702a_19 conda-forge libgomp 15.2.0 he0feb66_19 conda-forge liblzma 5.8.3 hb03c661_0 conda-forge libnsl 2.0.1 hb9d3cd8_1 conda-forge libsqlite 3.53.2 h0c1763c_0 conda-forge libuuid 2.42.1 h5347b49_0 conda-forge libxcrypt 4.4.36 hd590300_1 conda-forge libzlib 1.3.2 h25fd6f3_2 conda-forge markupsafe 3.0.3 pypi_0 pypi matplotlib 3.10.5 pypi_0 pypi matplotlib-inline 0.2.2 pypi_0 pypi ml-collections 1.1.0 pypi_0 pypi modelcif 1.4 pypi_0 pypi mpmath 1.3.0 pypi_0 pypi msgpack 1.2.0 pypi_0 pypi ncurses 6.6 hdb14827_0 conda-forge networkx 3.6.1 pypi_0 pypi ninja 1.13.0 pypi_0 pypi numpy 2.4.1 pypi_0 pypi nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi nvidia-curand-cu12 10.3.7.77 pypi_0 pypi nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi nvidia-ml-py 13.610.43 pypi_0 pypi nvidia-nccl-cu12 2.26.2 pypi_0 pypi nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi nvidia-nvtx-cu12 12.6.77 pypi_0 pypi openssl 3.6.3 h35e630c_0 conda-forge opt-einsum 3.4.0 pypi_0 pypi optree 0.17.0 pypi_0 pypi packaging 26.2 pyhc364b38_0 conda-forge pandas 2.3.1 pypi_0 pypi parso 0.8.7 pypi_0 pypi pdbeccdutils 1.0.0 pypi_0 pypi pexpect 4.9.0 pypi_0 pypi pillow 12.2.0 pypi_0 pypi pip 26.1.2 pyh8b19718_0 conda-forge platformdirs 4.10.0 pypi_0 pypi prompt-toolkit 3.0.52 pypi_0 pypi protenix 2.0.0 pypi_0 pypi protobuf 6.31.1 pypi_0 pypi psutil 7.2.2 pypi_0 pypi ptyprocess 0.7.0 pypi_0 pypi pure-eval 0.2.3 pypi_0 pypi py-cpuinfo 9.0.0 pypi_0 pypi py3dmol 2.5.2 pypi_0 pypi pydantic 2.13.4 pypi_0 pypi pydantic-core 2.46.4 pypi_0 pypi pygments 2.20.0 pypi_0 pypi pyparsing 3.3.2 pypi_0 pypi python 3.11.15 h7508c33_1_cpython conda-forge python-dateutil 2.9.0.post0 pypi_0 pypi pytz 2026.2 pypi_0 pypi pyyaml 6.0.2 pypi_0 pypi rdkit 2025.9.3 pypi_0 pypi readline 8.3 h853b02a_0 conda-forge requests 2.34.2 pypi_0 pypi scikit-learn 1.7.1 pypi_0 pypi scikit-learn-extra 0.3.0 pypi_0 pypi scipy 1.17.1 pypi_0 pypi sentry-sdk 2.62.0 pypi_0 pypi setuptools 82.0.1 pyh332efcf_0 conda-forge six 1.17.0 pypi_0 pypi smmap 5.0.3 pypi_0 pypi stack-data 0.6.3 pypi_0 pypi sympy 1.14.0 pypi_0 pypi threadpoolctl 3.6.0 pypi_0 pypi tk 8.6.13 noxft_h366c992_103 conda-forge torch 2.7.1 pypi_0 pypi torchaudio 2.7.1 pypi_0 pypi torchvision 0.22.1 pypi_0 pypi tqdm 4.67.1 pypi_0 pypi traitlets 5.15.1 pypi_0 pypi triton 3.3.1 pypi_0 pypi typing-extensions 4.15.0 pypi_0 pypi typing-inspection 0.4.2 pypi_0 pypi tzdata 2026.2 pypi_0 pypi urllib3 2.7.0 pypi_0 pypi wandb 0.21.1 pypi_0 pypi wcwidth 0.8.1 pypi_0 pypi wheel 0.47.0 pyhd8ed1ab_0 conda-forge widgetsnbextension 4.0.15 pypi_0 pypi zstd 1.5.7 hb78ec9c_6 conda-forge (protenix) [root@r9 ~]# }} 確認 #code(nonumber){{ (protenix) [root@r9 ~]# python Python 3.11.15 | packaged by conda-forge | (main, Jun 11 2026, 03:34:02) [GCC 14.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import torch >>> print(torch.cuda.is_available()) True >>> print(torch.cuda.get_device_name()) /apps/pyenv/versions/miniforge3-26.3.2-1/envs/protenix/lib/python3.11/site-packages/torch/cuda/__init__.py:287: UserWarning: NVIDIA RTX PRO 2000 Blackwell with CUDA capability sm_120 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_90. If you want to use the NVIDIA RTX PRO 2000 Blackwell GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/ warnings.warn( NVIDIA RTX PRO 2000 Blackwell >>> quit(); (protenix) [root@r9 ~]# }} っと今入っている torch 2.7.1 ではblackwellは無理みたい なので差し替え #code(nonumber){{ (protenix) [root@r9 ~]# python -m pip uninstall -y torch torchvision torchaudio triton (protenix) [root@r9 ~]# python -m pip cache purge (protenix) [root@r9 ~]# python -m pip install --pre torch==2.7.1 torchaudio==2.7.1 torchvision==0.22.1 --index-url https://download.pytorch.org/whl/cu128 (protenix) [root@r9 ~]# conda list : numpy 2.4.1 pypi_0 pypi nvidia-cublas-cu12 12.8.3.14 pypi_0 pypi nvidia-cuda-cupti-cu12 12.8.57 pypi_0 pypi nvidia-cuda-nvrtc-cu12 12.8.61 pypi_0 pypi nvidia-cuda-runtime-cu12 12.8.57 pypi_0 pypi nvidia-cudnn-cu12 9.7.1.26 pypi_0 pypi nvidia-cufft-cu12 11.3.3.41 pypi_0 pypi nvidia-cufile-cu12 1.13.0.11 pypi_0 pypi nvidia-curand-cu12 10.3.9.55 pypi_0 pypi nvidia-cusolver-cu12 11.7.2.55 pypi_0 pypi nvidia-cusparse-cu12 12.5.7.53 pypi_0 pypi nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi nvidia-ml-py 13.610.43 pypi_0 pypi nvidia-nccl-cu12 2.26.2 pypi_0 pypi nvidia-nvjitlink-cu12 12.8.61 pypi_0 pypi nvidia-nvtx-cu12 12.8.55 pypi_0 pypi openssl 3.6.3 h35e630c_0 conda-forge : python 3.11.15 h7508c33_1_cpython conda-forge : torch 2.7.1+cu128 pypi_0 pypi torchaudio 2.7.1+cu128 pypi_0 pypi torchvision 0.22.1+cu128 pypi_0 pypi : (protenix) [root@r9 ~]# (protenix) [root@r9 ~]# python Python 3.11.15 | packaged by conda-forge | (main, Jun 11 2026, 03:34:02) [GCC 14.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import torch >>> print(torch.cuda.is_available()) True >>> print(torch.cuda.get_device_name()) NVIDIA RTX PRO 2000 Blackwell >>> quit(); (protenix) [root@r9 ~]# }} ***environment-modules [#fed55038] 「/apps/modulefiles/protenix」 #code(nonumber){{ #%Module set root /apps/pyenv/versions/miniforge3-26.3.2-1/envs/protenix prepend-path PATH $root/bin prepend-path LD_LIBRARY_PATH $root/lib }} ***つかってみる [#w347da37] #code(nonumber){{ [saber@r9 ~]$ module load protenix [saber@r9 ~]$ cd test [saber@r9 test]$ git clone https://github.com/bytedance/Protenix Cloning into 'Protenix'... remote: Enumerating objects: 2642, done. remote: Total 2642 (delta 0), reused 0 (delta 0), pack-reused 2642 (from 1) Receiving objects: 100% (2642/2642), 105.22 MiB | 27.46 MiB/s, done. Resolving deltas: 100% (1644/1644), done. [saber@r9 test]$ cd Protenix/ [saber@r9 Protenix]$ [saber@r9 Protenix]$ protenix --version protenix, version 2.0.0 [saber@r9 Protenix]$ protenix --help Usage: protenix [OPTIONS] COMMAND [ARGS]... Protenix: A trainable reproduction of AlphaFold 3. This CLI provides tools for structure prediction, data conversion, and MSA/template searching. Options: --version Show the version and exit. -h, --help Show this message and exit. Commands: json Convert PDB or CIF files to JSON files for Protenix inference. msa Perform MSA search using MMseqs2. mt Perform MSA search followed by template search. pred Run predictions with Protenix using various input formats. prep Perform MSA search, template search, and RNA MSA search... [saber@r9 Protenix]$ [saber@r9 Protenix]$ [saber@r9 Protenix]$ protenix pred -i examples/input.json -o ./output -n protenix_base_default_v1.0.0 }}
