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Add GitHub Actions workflow for PyPI publishing (#21)
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‎.github/workflows/pypi.yml‎

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name: Publish PyPI
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on: push
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jobs:
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build:
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name: Build
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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package: [skala, microsoft-skala]
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steps:
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- uses: actions/checkout@v4
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with:
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persist-credentials: false
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Install pypa/build
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run: >-
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python3 -m
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pip install
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build
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--user
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- name: Replace pyproject.toml
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if: ${{ matrix.package == 'microsoft-skala' }}
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run: |
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VERSION=$(python3 -c "from tomllib import load; print(load(open('pyproject.toml', 'rb'))['project']['version'])")
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rm -r src/skala
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mkdir -p src/microsoft_skala
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echo "from skala import *" > src/microsoft_skala/__init__.py
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touch src/microsoft_skala/py.typed
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sed -e "s/@VERSION@/$VERSION/g" .github/workflows/pypi/microsoft-skala.toml > pyproject.toml
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cat pyproject.toml
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- name: Build a binary wheel and a source tarball
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run: python3 -m build
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- name: Store the distribution packages
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uses: actions/upload-artifact@v4
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with:
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name: ${{ matrix.package }}-pkg
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path: dist/
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publish-to-pypi:
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name: Publish to PyPI
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if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes
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needs:
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- build
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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package: [skala, microsoft-skala]
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environment:
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name: pypi
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url: https://pypi.org/p/${{ matrix.package }}
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permissions:
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id-token: write # IMPORTANT: mandatory for trusted publishing
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steps:
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- name: Download all the dists
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uses: actions/download-artifact@v4
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with:
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name: ${{ matrix.package }}-pkg
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path: dist/
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- name: Publish distribution to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1

‎.github/workflows/pypi/microsoft-skala.toml‎

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[build-system]
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requires = ["setuptools"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "microsoft-skala"
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version = "@VERSION@"
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description = "Skala Exchange Correlation Functional"
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authors = []
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license-files = ["LICENSE.txt"]
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readme = "README.md"
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keywords = []
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classifiers = [
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"Programming Language :: Python :: 3",
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"Development Status :: 5 - Production/Stable",
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]
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requires-python = ">=3.10"
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dependencies = [
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"skala==@VERSION@",
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]
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urls.repository = "https://github.com/microsoft/skala"
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urls.documentation = "https://microsoft.github.io/skala"
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urls.homepage = "https://aka.ms/dft"

‎README.md‎

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[![Documentation](https://img.shields.io/badge/docs-microsoft.github.io%2Fskala-blue?logo=read-the-docs&logoColor=white)](https://microsoft.github.io/skala)
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[![Tests](https://img.shields.io/github/actions/workflow/status/microsoft/skala/test.yml?branch=main&logo=github&label=build)](https://github.com/microsoft/skala/actions/workflows/test.yml)
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[![PyPI](https://img.shields.io/pypi/v/microsoft-skala?logo=pypi&logoColor=white)](https://pypi.org/project/microsoft-skala/)
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[![PyPI](https://img.shields.io/pypi/v/skala?logo=pypi&logoColor=white)](https://pypi.org/project/skala/)
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[![Paper](https://img.shields.io/badge/arXiv-2506.14665-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2506.14665)
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Skala is a neural network-based exchange-correlation functional for density functional theory (DFT), developed by Microsoft Research AI for Science. It leverages deep learning to predict exchange-correlation energies from electron density features, achieving chemical accuracy for atomization energies and strong performance on broad thermochemistry and kinetics benchmarks, all at a computational cost similar to semi-local DFT.
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This repository contains three main components:
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1. The Python package `microsoft-skala`, which is also distributed [on PyPI](https://pypi.org/project/microsoft-skala/) and contains a Pytorch implementation of the Skala model, its hookups to quantum chemistry packages [PySCF](https://pyscf.org/) and [ASE](https://ase-lib.org/), and an independent client library for the Skala model served [in Azure AI Foundry](https://ai.azure.com/catalog/models/Skala).
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1. The Python package `skala`, which is also distributed [on PyPI](https://pypi.org/project/skala/) and contains a Pytorch implementation of the Skala model, its hookups to quantum chemistry packages [PySCF](https://pyscf.org/) and [ASE](https://ase-lib.org/), and an independent client library for the Skala model served [in Azure AI Foundry](https://ai.azure.com/catalog/models/Skala).
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2. A development version of the CPU/GPU C++ library for XC functionals [GauXC](https://github.com/wavefunction91/GauXC/tree/skala) with an add-on supporting Pytorch-based functionals like Skala. GauXC is part of the stack that serves Skala in Azure AI Foundry and can be used to integrate Skala into other third-party DFT codes.
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3. An example of using Skala in C++ CPU applications through LibTorch, see [`examples/cpp/cpp_integration`](examples/cpp/cpp_integration).
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```bash
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pip install torch --index-url https://download.pytorch.org/whl/cpu # unless you already have GPU Pytorch for something else
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pip install microsoft-skala
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pip install skala
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```
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Run an SCF calculation with Skala for a hydrogen molecule:
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cu_version=128 #or 126 or 130 depending on your CUDA version
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pip install torch cupy --extra-index-url "https://download.pytorch.org/whl/cu${cu_version}"
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pip install --no-deps "gpu4pyscf-cuda${cu_version:0:2}x>=1.0,<2" "gpu4pyscf-libxc-cuda${cu_version:0:2}x>=0.4,<1"
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pip install microsoft-skala
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pip install skala
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```
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Run an SCF calculation with Skala for a hydrogen molecule on GPU:
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‎pyproject.toml‎

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build-backend = "setuptools.build_meta"
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[project]
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name = "microsoft-skala"
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name = "skala"
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version = "1.1.1"
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description = "Skala Exchange Correlation Functional"
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authors = []
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]
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urls.repository = "https://github.com/microsoft/skala"
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urls.documentation = "https://microsoft.github.io/skala"
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urls.homepage = "https://aka.ms/dft"
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[tool.ruff]
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target-version = "py311"

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