Installation

Installation#

Install from PyPI#

PyPI

To install skala, you can use pip:

pip install skala

This will install the skala packages and all its dependencies, including

  • torch, e3nn, and opt_einsum_fx for running the exchange-correlation model

  • pyscf for running the self-consistent field (SCF) calculations and evaluating the density features

  • dftd3 for computing the D3 dispersion correction to the total energy

The default Pytorch installation is the GPU version, which the skala package in combination with PySCF doesn’t leverage. To install only the much smaller CPU version of Pytorch, run the following before installing the skala package:

pip install torch --index-url https://download.pytorch.org/whl/cpu

Install from conda-forge#

conda-forge

The skala package is available on conda-forge, to install it use

mamba install -c conda-forge skala

You can select between GPU and CPU version of pytorch by requesting the cuda* build or the cpu* build. For the CPU version use

mamba install -c conda-forge skala "pytorch=*=cpu*"

For the GPU version use (e.g. with Cuda 12)

mamba install -c conda-forge skala "pytorch=*=cuda12*"

Installing from source#

If you prefer to install Skala from the source code, you can clone the repository and install it in editable mode:

git clone https://github.com/microsoft/skala
cd skala
mamba env create -n skala -f environment-cpu.yml
mamba activate skala
pip install -e .

where environment-cpu.yml can be replaced with environment-gpu.yml for GPU support via GPU4PySCF. The GPU environment pins cuda-toolkit 12, cuda-version 12, cutensor, and installs gpu4pyscf-cuda12x 1.5 from PyPI as part of the environment file — no separate install step is required:

mamba env create -n skala -f environment-gpu.yml
mamba activate skala
pip install -e .

If you are building inside a container without a GPU attached (for example CI, or a Docker image built on a CPU-only host), set CONDA_OVERRIDE_CUDA so the solver proceeds without a device:

CONDA_OVERRIDE_CUDA=12.0 mamba env create -n skala -f environment-gpu.yml

For CUDA 11 or 13, adjust cuda-toolkit, cuda-version, and the gpu4pyscf-cuda{11,13}x pin in environment-gpu.yml accordingly. Check your driver’s maximum supported CUDA version with nvidia-smi.

To install the development dependencies, you can run:

pip install -e .[dev]

For development purposes, please initialize the pre-commit hooks via:

pre-commit install

To test your installation, you can run the tests:

pytest -v tests/

Model checkpoints#

The pre-trained Skala model checkpoints are hosted on Hugging Face and downloaded automatically by the Python package in this repository from there for running calculations.