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aqlaboratory / openfold

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Apache License 2.0
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libtorch_cuda_cpp.so missing after installation (rtx4090 with cuda driver 12.1) #387

Open cifnik opened 10 months ago

cifnik commented 10 months ago

I am trying to use openfold on a machine with an rtx 4090 with cuda driver 12.1 I install the package using the environment file and I get this problem at the time of installing attn_cuda

(copying the last part, it looks like it has to do with the architecture of my GPU? I could install in older ones with cuda driver 10.2)

      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h: In instantiation of ‘constexpr const bool fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, 128, 128, 16, 16, 1, 0, 8, 1, true>::PARTIAL_STORE’:
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:98:41:   required from ‘constexpr const int fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, 128, 128, 16, 16, 1, 0, 8, 1, true>::STORING_THREADS’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:102:651:   required from ‘struct fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, 128, 128, 16, 16, 1, 0, 8, 1, true>’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:570:8:   required from ‘struct fmha::Smem_tile_col_b<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, 16, 1, 8>’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:705:8:   required from ‘struct fmha::Smem_tile_b<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, fmha::Col, 16, 1, true>’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:92:86:   required from ‘constexpr const int FMHA_kernel_traits<128, 128, 16, 1, 8, 256>::BYTES_PER_SMEM_QK’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:96:44:   required from ‘constexpr const int FMHA_kernel_traits<128, 128, 16, 1, 8, 256>::BYTES_PER_SMEM_QKV’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:101:58:   required from ‘constexpr const int FMHA_kernel_traits<128, 128, 16, 1, 8, 256>::BYTES_PER_SMEM’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:103:79:   required from ‘struct FMHA_kernel_traits<128, 128, 16, 1, 8, 256>’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha_dgrad_fp16_kernel_loop.sm80.cu:14:64:   required from ‘void run_fmha_dgrad_fp16_sm80_loop_(const FMHA_dgrad_params&, cudaStream_t) [with Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 8, 256>; cudaStream_t = CUstream_st*]’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha_dgrad_fp16_kernel_loop.sm80.cu:97:81:   required from here
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:97:53: warning: comparison between ‘enum fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, 128, 128, 16, 16, 1, 0, 8, 1, true>::<unnamed>’ and ‘enum fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 8, 1>, 128, 128, 16, 16, 1, 0, 8, 1, true>::<unnamed>’ [-Wenum-compare]
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h: In instantiation of ‘constexpr const bool fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 1, 8>, 128, 128, 16, 16, 1, 0, 8, 1, true>::PARTIAL_STORE’:
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:98:41:   required from ‘constexpr const int fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 1, 8>, 128, 128, 16, 16, 1, 0, 8, 1, true>::STORING_THREADS’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:102:651:   required from ‘struct fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 1, 8>, 128, 128, 16, 16, 1, 0, 8, 1, true>’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:935:8:   required from ‘struct fmha::Smem_tile_v<fmha::Cta_tile_<16, 128, 128, 1, 1, 8> >’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:94:85:   required from ‘constexpr const int FMHA_kernel_traits<128, 128, 16, 1, 8, 256>::BYTES_PER_SMEM_V’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:96:64:   required from ‘constexpr const int FMHA_kernel_traits<128, 128, 16, 1, 8, 256>::BYTES_PER_SMEM_QKV’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:101:58:   required from ‘constexpr const int FMHA_kernel_traits<128, 128, 16, 1, 8, 256>::BYTES_PER_SMEM’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/kernel_traits.h:103:79:   required from ‘struct FMHA_kernel_traits<128, 128, 16, 1, 8, 256>’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha_dgrad_fp16_kernel_loop.sm80.cu:14:64:   required from ‘void run_fmha_dgrad_fp16_sm80_loop_(const FMHA_dgrad_params&, cudaStream_t) [with Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 8, 256>; cudaStream_t = CUstream_st*]’
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha_dgrad_fp16_kernel_loop.sm80.cu:97:81:   required from here
      /tmp/pip-req-build-dqf5r6ka/csrc/flash_attn/src/fmha/smem_tile.h:97:53: warning: comparison between ‘enum fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 1, 8>, 128, 128, 16, 16, 1, 0, 8, 1, true>::<unnamed>’ and ‘enum fmha::Smem_tile_without_skews<fmha::Cta_tile_<16, 128, 128, 1, 1, 8>, 128, 128, 16, 16, 1, 0, 8, 1, true>::<unnamed>’ [-Wenum-compare]
      ninja: build stopped: subcommand failed.
      Traceback (most recent call last):
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 1798, in _run_ninja_build
          subprocess.run(
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/subprocess.py", line 528, in run
          raise CalledProcessError(retcode, process.args,
      subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

      The above exception was the direct cause of the following exception:

      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-req-build-dqf5r6ka/setup.py", line 152, in <module>
          setup(
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/setuptools/__init__.py", line 153, in setup
          return distutils.core.setup(**attrs)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/core.py", line 148, in setup
          dist.run_commands()
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/dist.py", line 966, in run_commands
          self.run_command(cmd)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/dist.py", line 985, in run_command
          cmd_obj.run()
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/wheel/bdist_wheel.py", line 368, in run
          self.run_command("build")
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/cmd.py", line 313, in run_command
          self.distribution.run_command(command)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/dist.py", line 985, in run_command
          cmd_obj.run()
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/command/build.py", line 135, in run
          self.run_command(cmd_name)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/cmd.py", line 313, in run_command
          self.distribution.run_command(command)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/dist.py", line 985, in run_command
          cmd_obj.run()
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/setuptools/command/build_ext.py", line 79, in run
          _build_ext.run(self)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/command/build_ext.py", line 340, in run
          self.build_extensions()
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 755, in build_extensions
          build_ext.build_extensions(self)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/command/build_ext.py", line 449, in build_extensions
          self._build_extensions_serial()
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/command/build_ext.py", line 474, in _build_extensions_serial
          self.build_extension(ext)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/setuptools/command/build_ext.py", line 202, in build_extension
          _build_ext.build_extension(self, ext)
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/distutils/command/build_ext.py", line 529, in build_extension
          objects = self.compiler.compile(sources,
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 576, in unix_wrap_ninja_compile
          _write_ninja_file_and_compile_objects(
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 1477, in _write_ninja_file_and_compile_objects
          _run_ninja_build(
        File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 1814, in _run_ninja_build
          raise RuntimeError(message) from e
      RuntimeError: Error compiling objects for extension
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for flash-attn
  Running setup.py clean for flash-attn
Successfully built DLLogger
Failed to build flash-attn

Installing flash-attn alone I manage to get it but then runnging a small test: python run_pretrained_openfold.py fasta_dir data/pdb_mmcif/mmcif_files/ --uniref90_database_path data/uniref90/uniref90.fasta --mgnify_database_path data/mgnify/mgy_clusters_2018_12.fa --pdb70_database_path data/pdb70/pdb70 --uniclust30_database_path data/uniclust30/uniclust30_2018_08/uniclust30_2018_08 --output_dir ./ --bfd_database_path data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt --model_device "cuda:0" --jackhmmer_binary_path lib/conda/envs/openfold_venv/bin/jackhmmer --hhblits_binary_path lib/conda/envs/openfold_venv/bin/hhblits --hhsearch_binary_path lib/conda/envs/openfold_venv/bin/hhsearch --kalign_binary_path lib/conda/envs/openfold_venv/bin/kalign --config_preset "model_1_ptm" --openfold_checkpoint_path openfold/resources/openfold_params/finetuning_ptm_2.pt

I get the following error:

Traceback (most recent call last):
  File "/home/dcianferoni/openfold/run_pretrained_openfold.py", line 21, in <module>
    from openfold.utils.script_utils import load_models_from_command_line, parse_fasta, run_model, prep_output, \
  File "/home/dcianferoni/openfold/openfold/utils/script_utils.py", line 10, in <module>
    from openfold.model.model import AlphaFold
  File "/home/dcianferoni/openfold/openfold/model/model.py", line 21, in <module>
    from openfold.model.embedders import (
  File "/home/dcianferoni/openfold/openfold/model/embedders.py", line 20, in <module>
    from openfold.model.primitives import Linear, LayerNorm
  File "/home/dcianferoni/openfold/openfold/model/primitives.py", line 30, in <module>
    from flash_attn.bert_padding import unpad_input
  File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/flash_attn/__init__.py", line 3, in <module>
    from flash_attn.flash_attn_interface import (
  File "/home/dcianferoni/miniforge3/envs/openfold_env/lib/python3.9/site-packages/flash_attn/flash_attn_interface.py", line 10, in <module>
    import flash_attn_2_cuda as flash_attn_cuda
ImportError: libtorch_cuda_cpp.so: cannot open shared object file: No such file or directory
dthorburn commented 5 months ago

I had the same problem with an RTX4090. I couldn't get the main environment.yml to install a version of torch that supported the 4090's architecture.

I found installing ninja helped a little too, which i see is missing in your environment. I started playing around with some of the experimental branches and eventually got a stable conda environment with cuda 11.8, I've only just started testing, but it's going well so far. One key part was rolling back flash attention, unsure why, but it works when rolled back to v2.0 (commit hash 4f285b3). 

name: openfold_plup118
channels:
  - pytorch
  - bioconda
  - nvidia
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_kmp_llvm
  - absl-py=2.1.0=pyhd8ed1ab_0
  - appdirs=1.4.4=pyh9f0ad1d_0
  - aria2=1.37.0=h347180d_1
  - aws-c-auth=0.7.8=h538f98c_2
  - aws-c-cal=0.6.9=h5d48c4d_2
  - aws-c-common=0.9.10=hd590300_0
  - aws-c-compression=0.2.17=h7f92143_7
  - aws-c-event-stream=0.3.2=h0bcb0bb_8
  - aws-c-http=0.7.14=hd268abd_3
  - aws-c-io=0.13.36=he0cd244_2
  - aws-c-mqtt=0.9.10=h35285c7_2
  - aws-c-s3=0.4.4=h0448019_0
  - aws-c-sdkutils=0.1.13=h7f92143_0
  - aws-checksums=0.1.17=h7f92143_6
  - awscli=2.15.45=py39hf3d152e_0
  - awscrt=0.19.19=py39hf0530f4_2
  - biopython=1.79=py39hb9d737c_3
  - blas=2.116=mkl
  - blas-devel=3.9.0=16_linux64_mkl
  - brotli-python=1.1.0=py39h3d6467e_1
  - bzip2=1.0.8=hd590300_5
  - c-ares=1.28.1=hd590300_0
  - ca-certificates=2024.2.2=hbcca054_0
  - certifi=2024.2.2=pyhd8ed1ab_0
  - cffi=1.16.0=py39h7a31438_0
  - charset-normalizer=3.3.2=pyhd8ed1ab_0
  - click=8.1.7=unix_pyh707e725_0
  - colorama=0.4.6=pyhd8ed1ab_0
  - contextlib2=21.6.0=pyhd8ed1ab_0
  - cryptography=40.0.2=py39h079d5ae_0
  - cuda-cudart=11.8.89=0
  - cuda-cupti=11.8.87=0
  - cuda-libraries=11.8.0=0
  - cuda-nvrtc=11.8.89=0
  - cuda-nvtx=11.8.86=0
  - cuda-runtime=11.8.0=0
  - cudatoolkit=11.8.0=h4ba93d1_13
  - distro=1.8.0=pyhd8ed1ab_0
  - docker-pycreds=0.4.0=py_0
  - docutils=0.19=py39hf3d152e_1
  - fftw=3.3.10=nompi_hc118613_108
  - filelock=3.14.0=pyhd8ed1ab_0
  - fsspec=2024.3.1=pyhca7485f_0
  - git=2.45.0=pl5321hef9f9f3_1
  - gitdb=4.0.11=pyhd8ed1ab_0
  - gitpython=3.1.43=pyhd8ed1ab_0
  - gmp=6.3.0=h59595ed_1
  - gmpy2=2.1.5=py39h03b5d36_0
  - hhsuite=3.3.0=py39pl5321he10ea66_10
  - hmmer=3.3.2=hdbdd923_4
  - icu=73.2=h59595ed_0
  - idna=3.7=pyhd8ed1ab_0
  - ihm=1.0=py39hd1e30aa_0
  - jinja2=3.1.3=pyhd8ed1ab_0
  - jmespath=1.0.1=pyhd8ed1ab_0
  - kalign2=2.04=h031d066_6
  - keyutils=1.6.1=h166bdaf_0
  - krb5=1.21.2=h659d440_0
  - ld_impl_linux-64=2.40=h55db66e_0
  - libabseil=20240116.2=cxx17_h59595ed_0
  - libblas=3.9.0=16_linux64_mkl
  - libcblas=3.9.0=16_linux64_mkl
  - libcublas=11.11.3.6=0
  - libcufft=10.9.0.58=0
  - libcufile=1.9.1.3=0
  - libcurand=10.3.5.147=0
  - libcurl=8.7.1=hca28451_0
  - libcusolver=11.4.1.48=0
  - libcusparse=11.7.5.86=0
  - libedit=3.1.20191231=he28a2e2_2
  - libev=4.33=hd590300_2
  - libexpat=2.6.2=h59595ed_0
  - libffi=3.4.2=h7f98852_5
  - libgcc=7.2.0=h69d50b8_2
  - libgcc-ng=13.2.0=h77fa898_7
  - libgfortran-ng=13.2.0=h69a702a_7
  - libgfortran5=13.2.0=hca663fb_7
  - libhwloc=2.10.0=default_h2fb2949_1000
  - libiconv=1.17=hd590300_2
  - liblapack=3.9.0=16_linux64_mkl
  - liblapacke=3.9.0=16_linux64_mkl
  - libnghttp2=1.58.0=h47da74e_1
  - libnpp=11.8.0.86=0
  - libnsl=2.0.1=hd590300_0
  - libnvjpeg=11.9.0.86=0
  - libprotobuf=4.25.3=h08a7969_0
  - libsqlite=3.45.3=h2797004_0
  - libssh2=1.11.0=h0841786_0
  - libstdcxx-ng=13.2.0=hc0a3c3a_7
  - libuuid=2.38.1=h0b41bf4_0
  - libxcrypt=4.4.36=hd590300_1
  - libxml2=2.12.6=h232c23b_2
  - libzlib=1.2.13=hd590300_5
  - lightning-utilities=0.11.2=pyhd8ed1ab_0
  - llvm-openmp=15.0.7=h0cdce71_0
  - markupsafe=2.1.5=py39hd1e30aa_0
  - mkl=2022.1.0=h84fe81f_915
  - mkl-devel=2022.1.0=ha770c72_916
  - mkl-include=2022.1.0=h84fe81f_915
  - ml-collections=0.1.1=pyhd8ed1ab_0
  - modelcif=0.7=pyhd8ed1ab_0
  - mpc=1.3.1=hfe3b2da_0
  - mpfr=4.2.1=h9458935_1
  - mpmath=1.3.0=pyhd8ed1ab_0
  - msgpack-python=1.0.7=py39h7633fee_0
  - ncurses=6.4.20240210=h59595ed_0
  - networkx=3.2.1=pyhd8ed1ab_0
  - numpy=1.26.4=py39h474f0d3_0
  - ocl-icd=2.3.2=hd590300_1
  - ocl-icd-system=1.0.0=1
  - openmm=7.7.0=py39h15fbce5_1
  - openssl=3.3.0=hd590300_0
  - packaging=24.0=pyhd8ed1ab_0
  - pandas=2.2.2=py39hddac248_0
  - pathtools=0.1.2=py_1
  - pcre2=10.43=hcad00b1_0
  - pdbfixer=1.8.1=pyh6c4a22f_0
  - perl=5.32.1=7_hd590300_perl5
  - pip=24.0=pyhd8ed1ab_0
  - pretty_errors=1.2.25=pyhd8ed1ab_0
  - prompt-toolkit=3.0.38=pyha770c72_0
  - prompt_toolkit=3.0.38=hd8ed1ab_0
  - protobuf=4.25.3=py39h1be52a0_0
  - psutil=5.9.8=py39hd1e30aa_0
  - pycparser=2.22=pyhd8ed1ab_0
  - pyopenssl=23.1.1=pyhd8ed1ab_0
  - pysocks=1.7.1=pyha2e5f31_6
  - python=3.9.19=h0755675_0_cpython
  - python-dateutil=2.8.2=pyhd8ed1ab_0
  - python-tzdata=2024.1=pyhd8ed1ab_0
  - python_abi=3.9=4_cp39
  - pytorch=2.1.2=py3.9_cuda11.8_cudnn8.7.0_0
  - pytorch-cuda=11.8=h7e8668a_5
  - pytorch-lightning=2.2.2=pyhd8ed1ab_0
  - pytorch-mutex=1.0=cuda
  - pytz=2024.1=pyhd8ed1ab_0
  - pyyaml=5.4.1=py39hb9d737c_4
  - readline=8.2=h8228510_1
  - requests=2.31.0=pyhd8ed1ab_0
  - ruamel.yaml=0.17.21=py39h72bdee0_3
  - ruamel.yaml.clib=0.2.7=py39hd1e30aa_2
  - s2n=1.4.0=h06160fa_0
  - scipy=1.13.0=py39haf93ffa_1
  - sentry-sdk=2.1.1=pyhd8ed1ab_0
  - setproctitle=1.3.3=py39hd1e30aa_0
  - setuptools=59.5.0=py39hf3d152e_0
  - six=1.16.0=pyh6c4a22f_0
  - smmap=5.0.0=pyhd8ed1ab_0
  - sympy=1.12=pypyh9d50eac_103
  - tbb=2021.12.0=h00ab1b0_0
  - tk=8.6.13=noxft_h4845f30_101
  - torchmetrics=1.4.0=pyhd8ed1ab_0
  - torchtriton=2.1.0=py39
  - tqdm=4.62.2=pyhd8ed1ab_0
  - typing-extensions=4.11.0=hd8ed1ab_0
  - typing_extensions=4.11.0=pyha770c72_0
  - tzdata=2024a=h0c530f3_0
  - urllib3=1.26.18=pyhd8ed1ab_0
  - wandb=0.16.5=pyhd8ed1ab_0
  - wcwidth=0.2.13=pyhd8ed1ab_0
  - wheel=0.43.0=pyhd8ed1ab_1
  - xz=5.2.6=h166bdaf_0
  - yaml=0.2.5=h7f98852_2
  - zstd=1.5.6=ha6fb4c9_0
  - pip:
      - annotated-types==0.6.0
      - deepspeed==0.12.4
      - dllogger==1.0.0
      - dm-tree==0.1.6
      - einops==0.8.0
      - flash-attn==2.0.0.post1
      - hjson==3.1.0
      - ninja==1.11.1.1
      - openfold==2.0.0
      - py-cpuinfo==9.0.0
      - pydantic==2.7.1
      - pydantic-core==2.18.2
      - pynvml==11.5.0
variables:
  CUTLASS_PATH: /mnt/nvme1n1p1/openfold_install/pl_upgrades/cutlass
  KMP_AFFINITY: none