metadata
license: mit
MLIP Stack Docker (CPU)
Ready-to-run Docker image bundling five machine-learning interatomic potential (MLIP) stacks in isolated conda environments. Built for CPU-only machines (no CUDA required). All environments use Python 3.11.
| Env name | Stack | Key packages |
|---|---|---|
grace |
GRACE (tensorpotential) | tensorflow |
nequip |
NequIP / Allegro | nequip, torch 2.12 (cpu) |
esen |
eSEN (fairchem) | fairchem-core, torch 2.4.1 (cpu), torch_scatter/sparse |
tace2 |
TACE | tace (git pin), torch 2.13 (cpu) |
mbd |
SevenNet and general MLIP tooling | sevenn, torch 2.8 (cpu), ase, pymatgen |
Quick start
# download and load (~7.6 GB compressed, ~28 GB on disk)
wget https://huggingface.co/datasets/Seanmonami/mlip-stack-docker/resolve/main/mlip-router-stack_2026-08-21.tar.gz
docker load < mlip-router-stack_2026-08-21.tar.gz
# interactive shell
docker run --rm -it mlip-router-stack:2026-08-21
# run something in a specific env
docker run --rm -v $PWD:/work -w /work mlip-router-stack:2026-08-21 \
micromamba run -n mbd python your_script.py
Inside the container, switch environments with micromamba run -n <env> ... or micromamba activate <env>.
Rebuilding from source
docker_build/ contains the Dockerfile and per-env requirement files (pip freezes normalized to CPU wheels). env_snapshots/ contains the original conda env export YAMLs and raw pip freezes captured from the source machine (Ubuntu 24.04, 2026-08-21).
cd docker_build && bash build.sh
Notes
- Model checkpoints are NOT included; download them via each package's own tooling (e.g.
sevenn,fairchem, GRACE model registry). - CUDA wheels were replaced with CPU wheels (
nvidia-*andtritondropped). For GPU use, rebuild with the original pins inenv_snapshots/.