structures25/environment_cpu.yaml at main · ToKa96/structures25 · GitHub

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# reasons you might want to use `environment.yaml` instead of `requirements.txt`:
# - pip installs packages in a loop, without ensuring dependencies across all packages
#   are fulfilled simultaneously, but conda achieves proper dependency control across
#   all packages
# - conda allows for installing packages without requiring certain compilers or
#   libraries to be available in the system, since it installs precompiled binaries

name: mldft

channels:
  - pytorch
  - pyscf
  - conda-forge
  - defaults

# it is strongly recommended to specify versions of packages installed through conda
# to avoid situation when version-unspecified packages install their latest major
# versions which can sometimes break things

# current approach below keeps the dependencies in the same major versions across all
# users, but allows for different minor and patch versions of packages where backwards
# compatibility is usually guaranteed

dependencies:
  - python=3.11
  - pyscf=2.4.0
  - jupyter
  - pytorch=2.4.1
  - torchvision=0.*
  - cpuonly
  - lightning=2.5.0
  - torchmetrics=0.*
  - hydra-core=1.*
  - numpy
  - rdkit
  - rich=13.*
  - pre-commit=3.*
  - pytest=7.*
  - pytest-cov
  - h5py
  - tensorboard
  - e3nn
  - opt_einsum
  - tqdm
  - nodejs # just for yaml formatting pre-commit
  - sphinx
  - sphinx-rtd-theme
  - zarr=2.18.4
  - ipytree
  - sqlalchemy=1.4.46
  - mysqlclient
  - umap-learn
  - pypdf
  - seaborn
  - python-lmdb
  - geometric

  - pip>=23
  - pip:
      - pyvista[all]
      - hydra-optuna-sweeper
      - hydra-colorlog
      - rootutils
      - ase>=3.19
      - polars
      - loguru
      - basis_set_exchange
      - torch_scatter
      - torch_cluster
      - torch_sparse
      - torch_geometric==2.6.1
      - gitpython
      - huggingface_hub==0.*
      - git+https://github.com/sciai-lab/tensor_frames.git@cd1addfd3c82a47095c9961ab999dcabfab4c21d