Build from source.

GLUED is distributed as source. There are no pip or conda packages — by design. You'll need CMake, an OpenMM 8.x install (typically from conda-forge), and optionally a CUDA toolkit if you want the GPU platform.

Prerequisites

If you're using Miniforge, conda-forge is already the only channel and the install just works. If you're on plain Miniconda, the conda commands below include --override-channels so the solver doesn't trip over the Anaconda ToS prompt for the default channels.

Platform support

The Reference platform works everywhere and is fully functional for development and small systems.

Build & install

Pick your OS — the prerequisites and CMake invocation differ slightly.

conda create -n openmm_env --override-channels -c conda-forge \
    "python=3.12" "cuda-version=12.9" \
    openmm cmake ninja swig pytest \
    cuda-nvcc cuda-cudart-dev cuda-libraries-dev cxx-compiler
conda activate openmm_env

git clone https://github.com/MarvinTaterra/GluedMD.git
cd GluedMD

cmake -S . -B build -G Ninja
cmake --build build
cmake --install build

conda create -n openmm_env --override-channels -c conda-forge ^
    "python=3.12" "cuda-version=12.9" ^
    openmm cmake ninja swig pytest ^
    cuda-nvcc cuda-cudart-dev cxx-compiler
conda activate openmm_env

git clone https://github.com/MarvinTaterra/GluedMD.git
cd GluedMD

cmake -S . -B build -G Ninja -DOPENMM_DIR="%CONDA_PREFIX%\Library"
cmake --build build
cmake --install build

conda create -n openmm_env --override-channels -c conda-forge \
    "python=3.12" openmm cmake ninja swig pytest
conda activate openmm_env

git clone https://github.com/MarvinTaterra/GluedMD.git
cd GluedMD

cmake -S . -B build -G Ninja
cmake --build build
cmake --install build

cp -r /mnt/c/Users/<you>/Desktop/glued ~/glued
cd ~/glued
cmake -S . -B build -G Ninja && cmake --build build

Common CMake options

Build a single platform target

cmake --build build --target OpenMMGluedReference   # CPU reference, no CUDA required
cmake --build build --target OpenMMGluedCUDA        # CUDA platform only
cmake --build build --target OpenMMGluedOpenCL      # OpenCL platform only

Running the tests

# Smoke test — verifies the plugin loads on every available platform
python tests/test_api_smoke.py

# Full pytest suite
python -m pytest tests/ -q

# Single test by name
python -m pytest tests/test_md_enhanced_sampling.py::test_metad_deposits -v

Tests that require CUDA or OpenCL are automatically skipped when that platform is unavailable. A Reference-only build still passes all non-GPU tests.

Verifying the install

import glued
import openmm as mm

f = glued.Force()
print("Available platforms:", [mm.Platform.getPlatform(i).getName()
                                for i in range(mm.Platform.getNumPlatforms())])

Expected output on Linux + NVIDIA:

Available platforms: ['Reference', 'CPU', 'CUDA', 'OpenCL']

Troubleshooting: NVRTC version mismatch

Linux / WSL2 quick fix

export LD_PRELOAD=/usr/local/cuda/lib64/libnvrtc.so.XX:/usr/local/cuda/lib64/libnvrtc-builtins.so.XX

Windows quick fix

$env:PATH = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vXX.Y\bin;$env:PATH"

The permanent fix is to update your NVIDIA driver to one that supports the NVRTC version bundled with your OpenMM build (compare nvcc --version to the driver's CUDA version in nvidia-smi).

Troubleshooting: import openmm segfaults

OpenMM scans /etc/OpenCL/vendors/ at import time and dlopens every .so listed there. A broken third‑party entry (most commonly the pocl ICD on Ubuntu base images, pointing at a missing or version‑mismatched libpocl) takes the whole process down. The GLUED CUDA plugin is fine — OpenMM is just being dragged down while enumerating plugins.

Inspect and fix

ls /etc/OpenCL/vendors/
# For each .icd file, confirm the .so it points to exists at the right version.
# If you only need NVIDIA OpenCL or CUDA, removing the bad entry is safe:
sudo rm /etc/OpenCL/vendors/pocl.icd

Re‑run python -c "import openmm; print(openmm.__version__)" — it should print the version without crashing.