ml-peg 0.2.1

ML potential usability and performance guide

ML-PEG: ML potential usability and performance guide

🔗 See our live guide: https://ml-peg.stfc.ac.uk

[!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

Contents

• Getting started
• Features
• Docker/Podman images
• Development
• License

Getting started

Dependencies

All required and optional dependencies can be found in pyproject.toml.

Installation

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

Features

Coming soon!

Docker/Podman images

You can use Docker or Podman to build and/or run the ML-PEG app yourself.

[!TIP] The commands below will assume you are using Docker. To use Podman, replace docker with podman, e.g. podman pull, podman build, and podman run.

A Docker image with the latest changes can be pulled from the GitHub container registry, following the command that can be found under this repository's packages.

[!NOTE] Currently, this repository only contains images for the linux/amd64 platform. On MacOS with ARM silicon, this can often still be run by setting --platform linux/amd64 when using docker run.

Alternatively, to build the container yourself, you can use the Dockerfile provided. From the ml-peg directory, run:

docker build -t ml-peg-app -f containers/Dockerfile .

Once built, you can mount your current application data and start the app by running:

docker run --volume ./ml_peg/app/data:/app/ml_peg/app/data  --publish 8050:8050 ml-peg-app

[!TIP] Ensure ml_peg/app/data is populated with results before running the container.

A compressed zip file containing the current live data can be found at http://s3.echo.stfc.ac.uk/ml-peg-data/app/data/data.tar.gz.

This may also be downloaded through the command line using

ml_peg download --key app/data/data.tar.gz  --filename data.tar.gz

Alternatively, you can use the compose.yml file provided, via Docker Compose:

docker compose -f containers/compose.yml up -d

The app should now be accessible at http://localhost:8050.

Development

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:

1. Install uv
2. 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 tests

Please refer to the online documentation for information about contributing new benchmarks and models.

License

GNU General Public License version 3