ml-peg 0.3.0

ML Performance and Extrapolation Guide

ML-PEG: ML Performance and Extrapolation Guide

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

Contents

• Getting started
• Features
• Development
• Docker/Podman images
• 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, can be installed 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!

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.

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.

License

GNU General Public License version 3