Installation#
The easiest way to install presto is from the GitHub repo using pixi. There is no conda-forge release yet (as we have a mix of dependencies which are only available on either conda-forge or PyPI).
Prerequisites#
- pixi — see pixi installation docs.
- NVIDIA driver compatible with CUDA 12.9 or newer for the GPU environment. Check with
nvidia-smi. Older driver versions are not usable becauseprestorequires OpenMM 8.5 (forPythonForce), and OpenMM 8.5 requires CUDA 12.9. - A CPU-only environment is available for development and docs builds but is very slow for real fits.
Install with pixi#
This creates the default GPU environment (gpu-py313-cuda129) and activates a shell in it. For more on activating pixi environments, see the pixi shell docs.
Verify the install#
If presto version prints a version string, you're ready to move on to Quickstart.
Optional environments#
The default GPU environment includes every supported MLP feature. Defined in pyproject.toml [tool.pixi.environments]:
| Environment | Use when |
|---|---|
gpu-py313-cuda129 (default) |
Normal GPU workflow on Python 3.13. |
gpu-py312-cuda129 |
GPU workflow on Python 3.12. |
cpu-py312, cpu-py313 |
Tests, docs, or environments without a CUDA-capable GPU. |
base* (No MLP features) |
No MLP deps desired |
Activate a non-default environment with:
MLP licensing notes#
The pixi presto install from GitHub brings in several reference MLPs by default. Most have permissive licences but a few do not:
- AIMNet2 (MIT) — current default; permits commercial use.
- Orb-v3 (OMol25 variants) (Apache-2.0) — permits commercial use.
- Egret-1 (MIT) — permits commercial use.
- AceFF-2.0 — permits commercial use, but is currently broken upstream.
- MACE-OFF — released under the Academic Software License. Not for commercial use.
For more, see Concepts → MLPs in presto and How-to → Choose an MLP.
Troubleshooting#
If anything went wrong during install or first run, see Reference → Troubleshooting.