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Notes & FAQ

A grab-bag of tips collected from user issues and questions.

GPU / DGL

  • Install a DGL build that matches your CUDA runtime, e.g. pip install dgl-cu111 for CUDA 11.1. Mismatched builds are the most common install failure.
  • If you see errors involving libcudart.so or libtorch_cuda.so, reinstall DGL from the wheel index that matches your PyTorch CUDA version (see Installation).

Structure file parsing

  • Simple .cif and .pdb files are handled by jarvis-tools directly.
  • For more complex CIFs, install cif2cell==2.0.0a3.
  • For complex PDBs, install pytraj via conda install -c ambermd pytraj.

Training hyperparameters

  • The example config_example.json ships with batch_size: 2 so the test suite runs fast. Use batch_size: 32 or 64 for real trainings — otherwise training will be very slow and under-performing.
  • pandas >= 1.2.3 is required.
  • Starting March 2024, pytorch-ignite is no longer a dependency (removed for conda-forge build compatibility).

CLIs are importable scripts

train_alignn.py, pretrained.py, and run_alignn_ff.py are installed as executables in your environment's bin/ directory. You do not need the absolute path — just run them.

Known dataset issues

  • QM9 results: see issue #54 for details on a data-split discrepancy that affects reproducibility.

Getting help