ML potential usability and performance guide
Note
Migration in progress! The live benchmarks are currently run and analysed using mlipx nodes defined in this repository: https://github.com/joehart2001/mlipx.
New benchmarks are expected to be added following the format defined in this repository, and work is ongoing to migrate all existing benchmarks to this format.
Our original interactive analysis suite is currently hosted at: http://mlip-testing.stfc.ac.uk:8050
All required and optional dependencies can be found in pyproject.toml.
The latest stable release of ML-PEG, including its dependencies, will be installable from PyPI by running:
python3 -m pip install ml-peg
To get all the latest changes, ML-PEG can be installed from GitHub:
python3 -m pip install git+https://github.com/ddmms/ml-peg.git
Coming soon!
Please ensure you have consulted our contribution guidelines and coding style before proceeding.
We recommend installing uv for dependency management when developing for ML-PEG:
- Install uv
- Install ML-PEG with dependencies in a virtual environment:
git clone https://github.com/ddmms/ml-peg
cd ml-peg
uv sync # Create a virtual environment and install dependencies
source .venv/bin/activate
pre-commit install # Install pre-commit hooks
pytest -v # Discover and run all testsPlease refer to the online documentation for information about contributing new benchmarks and models.