cpp_integration

Integrating Skala in C++ code

This example demonstrates how to use the Skala machine learning functional in C++ CPU applications using LibTorch.

Setup environment

Set up the conda environment using the provided environment file:

cd examples/cpp/cpp_integration
conda env create -n skala_cpp_integration -f environment-cpu.yml
conda activate skala_cpp_integration

Build library

The example can be built using CMake. The provided environment is configured for CMake to find the required dependencies.

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

For any changes to the code, rebuild using the last command.

Run example

Download the Skala model, as well as a reference LDA functional from HuggingFace using the provided download script:

./download_model.py

Prepare the molecular features for a test molecule (H2) using the provided script:

python ./prepare_inputs.py --output-dir H2

Finally, run $E_\text{xc}$ and (partial) $V_\text{xc}$ computations with the C++ example:

./_build/skala_cpp_integration skala-1.0.fun H2

Note: You are expected to add D3 dispersion correction (using b3lyp settings) to the final energy of Skala.

Performance tuning

This guide from Intel provides useful tips on how to tune performance of PyTorch models on CPU.