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Installation

This page covers installing remedi and its dependencies.

Prerequisites

  • Python ≥ 3.12
  • A CUDA 12.6 capable GPU for training and for computing descriptors at scale (CPU works for small runs).
  • uv is recommended, since it resolves the pinned torch/CUDA and git dependencies for you.
git clone https://github.com/molsuit/Rem3Di.git
cd Rem3Di
uv sync                 # core install
uv sync --extra eval    # + downstream learners (LightGBM, scikit-learn)

uv sync uses the pins in pyproject.toml: the CUDA-12.6 torch wheels (pytorch-cu126 index) and the torch-sim MACE backend from git (torch-sim-atomistic).

Install with pip

pip install -e .            # core
pip install -e ".[eval]"    # + downstream learners

With pip you must install the git-sourced dependencies yourself:

pip install \
    "torch-sim-atomistic @ git+https://github.com/TorchSim/torch-sim.git@main"

Verify

python - <<'PY'
from remedi.evaluation.evaluation_utils import (
    evaluate_molecular_descriptor_on_dataset,
)
print("ok")
PY

Gotchas

  • zarr v3 only. Datasets use zarr>=3.2. polaris-lib pins zarr<3 and cannot share this environment; ingest Polaris data out-of-venv via scripts/dataset_download/dump_polaris.py.
  • numba. Pinned to numba>=0.61 (older versions fail to build on Python 3.12).
  • MACE weights. The model uses a MACE foundation model as its frontend. Point the checkpoint's mace_config.model_path at a MACE model file on your machine (see Concepts).
  • torch.load weights-only error on import (e3nn constants.pt, PyTorch ≥ 2.6): export TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1.

Next steps