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#author("2026-06-04T20:43:12+00:00","default:sysosa","sysosa") #author("2026-06-04T20:52:52+00:00","default:sysosa","sysosa") 本家様 [[https://biopipelines.readthedocs.io/en/latest/>+https://biopipelines.readthedocs.io/en/latest/]] github [[https://github.com/locbp-uzh/biopipelines>+https://github.com/locbp-uzh/biopipelines]] #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 }} #code(nonumber){{ root@ubuntu24:~# grep VERSION /etc/os-release VERSION_ID="24.04" VERSION="24.04.4 LTS (Noble Numbat)" VERSION_CODENAME=noble root@ubuntu24:~# root@ubuntu24:~# cat /proc/driver/nvidia/version NVRM version: NVIDIA UNIX Open Kernel Module for x86_64 595.71.05 Release Build (dvs-builder@U22-I3-G08-03-1) Fri Apr 24 06:42:30 UTC 2026 GCC version: gcc version 13.3.0 (Ubuntu 13.3.0-6ubuntu2~24.04.1) root@ubuntu24:~# nvidia-smi -L GPU 0: NVIDIA RTX PRO 2000 Blackwell (UUID: GPU-40660e37-0d35-d4f4-294a-eee3fe83049e) root@ubuntu24:~# root@ubuntu24:~# ls /usr/local/cuda-12.9/ bin extras gds-12.9 lib64 nsight-compute-2025.2.1 nsight-systems-2025.1.3 nvvm src version.json compute-sanitizer gds include libnvvp nsightee_plugins nvml share targets root@ubuntu24:~# }} #code(nonumber){{ root@ubuntu24:~# cd /apps/ root@ubuntu24:/apps# git clone https://github.com/locbp-uzh/biopipelines.git root@ubuntu24:/apps# cd biopipelines root@ubuntu24:/apps/biopipelines# ls biopipelines config.local.yaml environments ligands my_pipelines pipe_scripts renderers run tables config.cluster.yaml CONTRIBUTING.md example_pipelines llm outputs pyproject.toml requirements-docs.txt sequences tests config.colab.yaml docs LICENSE mkdocs.yml pdbs README.md resubmit submit versions root@ubuntu24:/apps/biopipelines# ls environments/ admet_ai.pip.txt dssp.yaml frame2seq.yaml placer.yaml ProteinEnv.colab.yaml admet_ai.yaml dynamicbind.colab.yaml gems.colab.yaml plip.colab.yaml rbs_designer.cluster.yaml af2bind.yaml dynamicbind.pip.1.txt gems.yaml plip.pip.1.txt rbs_designer.colab.yaml Aggrescan3D.colab.yaml dynamicbind.pip.2.txt gnina.yaml plip.pip.colab.1.txt reduce.colab.yaml Aggrescan3D.yaml dynamicbind.pip.colab.1.txt ligandmpnn_env.pip.cluster.txt plip.yaml reduce.yaml apbs.colab.yaml dynamicbind.pip.colab.2.txt ligandmpnn_env.pip.colab.txt plm_sol.pip.txt rfdiffusion_allatom.pip.txt apbs.yaml dynamicbind_relax.colab.yaml ligandmpnn_env.yaml plm_sol.yaml rtmscore.colab.yaml bioemu.yaml dynamicbind_relax.yaml MutationEnv.yaml pocketgen.colab.yaml rtmscore.yaml biopipelines.yaml dynamicbind.yaml neuralplexer.colab.yaml pocketgen.pip.1.txt SE3nv.cluster.yaml Boltz2Env.pip.txt esmfold.cluster.yaml neuralplexer.pip.1.txt pocketgen.pip.2.txt SE3nv.colab.yaml Boltz2Env.yaml esmfold.colab.yaml neuralplexer.pip.2.txt pocketgen.pip.colab.1.txt SE3nv.pip.cluster.txt boltzgen.pip.txt esmfold.pip.colab.1.txt neuralplexer.pip.3.txt pocketgen.pip.colab.2.txt SE3nv.pip.colab.txt boltzgen.yaml esmfold.pip.colab.2.txt neuralplexer.pip.colab.1.txt pocketgen.yaml thermompnn.colab.yaml CABSflex.colab.yaml foundry.pip.txt neuralplexer.pip.colab.2.txt posebusters.pip.txt thermompnn.pip.colab.txt CABSflex.yaml foundry.yaml neuralplexer.pip.colab.3.txt posebusters.yaml thermompnn.yaml diffdock.colab.yaml fpocket.colab.yaml neuralplexer.yaml prodigy.colab.yaml vespag.colab.yaml diffdock.pip.colab.txt fpocket.yaml openmm.colab.yaml prodigy.yaml vespag.pip.colab.txt diffdock.pip.txt frame2seq.colab.yaml openmm.yaml prolif.colab.yaml vespag.pip.txt diffdock.yaml frame2seq.pip.colab.txt p2rank.colab.yaml prolif.yaml vespag.yaml dssp.colab.yaml frame2seq.pip.txt p2rank.yaml ProteinEnv.cluster.yaml xtb.yaml root@ubuntu24:/apps/biopipelines# root@ubuntu24:/apps/biopipelines# conda env create -f environments/biopipelines.yaml root@ubuntu24:/apps/biopipelines# conda activate biopipelines (biopipelines) root@ubuntu24:/apps/biopipelines# (biopipelines) root@ubuntu24:/apps/biopipelines# conda list : ipython 9.14.0 pyh53cf698_0 conda-forge : numpy 2.4.6 py312h33ff503_0 conda-forge openbabel 3.1.1 py312hbfe4552_9 conda-forge : python 3.12.13 hd63d673_0_cpython conda-forge : rdkit 2025.09.5 py312h3ecb6ed_0 conda-forge : (biopipelines) root@ubuntu24:/apps/biopipelines# (biopipelines) root@ubuntu24:/apps/biopipelines# conda search "pytorch=2.11=*cuda129*" -c pytorch Loading channels: done # Name Version Build Channel pytorch 2.11.0 cuda129_generic_py310_h331eed0_200 conda-forge pytorch 2.11.0 cuda129_generic_py311_h49a0b6d_200 conda-forge pytorch 2.11.0 cuda129_generic_py312_ha5c3ddd_200 conda-forge pytorch 2.11.0 cuda129_generic_py313_h7c95e44_200 conda-forge pytorch 2.11.0 cuda129_generic_py314_h117e3a7_200 conda-forge pytorch 2.11.0 cuda129_mkl_py310_h518f2e7_300 conda-forge pytorch 2.11.0 cuda129_mkl_py311_h6b7d41d_300 conda-forge pytorch 2.11.0 cuda129_mkl_py312_hb9da02c_300 conda-forge pytorch 2.11.0 cuda129_mkl_py313_hd2d7542_300 conda-forge pytorch 2.11.0 cuda129_mkl_py314_hfa65069_300 conda-forge (biopipelines) root@ubuntu24:/apps/biopipelines# (biopipelines) root@ubuntu24:/apps/biopipelines# conda install pytorch=2.11.0=cuda129_generic_py312_ha5c3ddd_200 -c conda-forge (biopipelines) root@ubuntu24:/apps/biopipelines# python -c "import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))" True NVIDIA RTX PRO 2000 Blackwell (biopipelines) root@ubuntu24:/apps/biopipelines# }} #code(nonumber){{ (biopipelines) root@ubuntu24:/apps/biopipelines# conda install pytest (biopipelines) root@ubuntu24:/apps/biopipelines# (biopipelines) root@ubuntu24:/apps/biopipelines# pytest tests/ ================================================================== test session starts =================================================================== platform linux -- Python 3.12.13, pytest-9.0.3, pluggy-1.6.0 rootdir: /apps/biopipelines configfile: pyproject.toml collected 650 items / 146 deselected / 504 selected tests/test_bayesian_adjuster.py .... [ 0%] tests/test_cabsflex_colab.py ...... [ 1%] tests/test_combinatorics.py ........................ [ 6%] tests/test_consensus.py .... [ 7%] tests/test_datastream.py ................................... [ 14%] tests/test_folders.py .. [ 14%] tests/test_id_map_utils.py ................................. [ 21%] tests/test_id_patterns.py ......................................................... [ 32%] tests/test_internal_and_folder.py .............. [ 35%] tests/test_ligand.py .............................. [ 41%] tests/test_load_multiple_folder.py ... [ 42%] tests/test_missing_propagation.py .......................... [ 47%] tests/test_mock.py ................................. [ 53%] tests/test_openbabel.py ..... [ 54%] tests/test_panda.py .................... [ 58%] tests/test_parallel.py ....... [ 60%] tests/test_pdb.py ................................. [ 66%] tests/test_pipeline_generation.py ..... [ 67%] tests/test_pocket_validation_wiring.py ....... [ 69%] tests/test_pool.py .............. [ 71%] tests/test_provenance.py ................ [ 75%] tests/test_remap.py .......... [ 76%] tests/test_resolve_selection_in_sequence.py ..... [ 77%] tests/test_sequence.py .............................. [ 83%] tests/test_shell_safety.py ............................................ [ 92%] tests/test_stream_slicers.py ................. [ 96%] tests/test_table.py .................... [100%] ===================================================== 504 passed, 146 deselected in 63.13s (0:01:03) ===================================================== (biopipelines) root@ubuntu24:/apps/biopipelines# }} 使いたいアプリがあるなら #code(nonumber){{ conda env update -n biopipelines -f environments/dssp.yaml }} とかで作った biopipelines 仮想実行環境に組み込むことはできるけど、environments/ にあるものは互いに調整してなくて、組み合わせによっては依存環境で実行できないこともあるそうな....&size(10){意味ねぇー}; 実際の計算というかジョブの流し方は #code(nonumber){{ pipeline: - module: dssp input_pdb: ./pdbs/1abc.pdb output_dir: ./outputs/dssp - module: plip input_pdb: ./pdbs/1abc.pdb output_dir: ./outputs/plip }} とか作って ./run に渡す jupyterlab とか使った方がいいかも。でも複数のアプリをまとめたconda環境を作る必要がある。機能、アプリ毎にconda環境を作ってもjupyterlab は１つの環境から別の環境に行く際に変数とか消えるので..
