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ML Performance and Extrapolation Guide (reference deployment https://ml-peg.stfc.ac.uk)

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# ML-PEG: ML Performance and Extrapolation Guide

[![PyPI version][pypi-badge]][pypi-link]
[![Python versions][python-badge]][python-link]
[![Build Status][ci-badge]][ci-link]
[![Docs status][docs-badge]][docs-link]
[![License][license-badge]][license-link]
[![DOI][doi-badge]][doi-link]
[![Ask DeepWiki][deepwiki-badge]][deepwiki-link]

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

## Contents
- [Getting started](#getting-started)
- [Features](#features)
- [Development](#development)
- [Docker/Podman images](#dockerpodman-images)
- [License](#license)

## Getting started

### Dependencies

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

More details coming soon!

## Development

Please ensure you have consulted our [contribution guidelines](contributing.md) and
[coding style](coding_style.md) before proceeding.

We recommend installing `uv` for dependency management when developing for ML-PEG:

1. Install [uv](https://docs.astral.sh/uv/getting-started/installation)
2. Install ML-PEG with dependencies in a virtual environment:

```shell
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](https://ddmms.github.io/ml-peg/developer_guide/index.html)
for information about contributing new benchmarks and models.

### Command-line interface

To help run calculations, analysis, and the application, we provide the `ml_peg`
command line tool, which is installed with the package. This provides the following
commands:

```shell
ml_peg app
ml_peg calc
ml_peg analyse
ml_peg download
ml_peg list
```

For example, to run the X23 test with mace-mp-0a and orb-v3-consv-inf-omat, you can run:

```shell
ml_peg calc --test X23 --models mace-mp-0a,orb-v3-consv-inf-omat
```

A description of each subcommand, as well as valid options, can be listed using the
`--help` option. For example:

```shell
ml_peg calc --help
```

The `ml_peg list` command provides a further set of subcommands:

```shell
ml_peg list calcs
ml_peg list analysis
ml_peg list app
ml_peg list models
```

which list the available tests and categories that may be run for `ml_peg calc`,
`ml_peg analyse` and `ml_peg app`, and the MLIPs that these can be run for.

### Tutorials

We encourage developers new to the ML-PEG framework to work through the detailed
step-by-step guides provided by our Jupyter Notebook tutorials:
- [Adding a New Benchmark](docs/source/tutorials/python/adding_benchmark.ipynb) [![badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ddmms/ml-peg/blob/main/docs/source/tutorials/python/adding_benchmark.ipynb)

## Docker/Podman images

You can use [Docker](https://www.docker.com) or [Podman](https://podman.io/) 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](https://github.com/ddmms/ml-peg/pkgs/container/ml-peg-app).

> [!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](containers/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](containers/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](LICENSE)

[pypi-badge]: https://badge.fury.io/py/ml-peg.svg
[pypi-link]: https://pypi.org/project/ml-peg/
[python-badge]: https://img.shields.io/pypi/pyversions/ml-peg.svg
[python-link]: https://pypi.org/project/ml-peg/
[ci-badge]: https://github.com/ddmms/ml-peg/actions/workflows/ci.yml/badge.svg?branch=main
[ci-link]: https://github.com/ddmms/ml-peg/actions
[cov-badge]: https://coveralls.io/repos/github/ddmms/ml-peg/badge.svg?branch=main
[cov-link]: https://coveralls.io/github/ddmms/ml-peg?branch=main
[docs-badge]: https://github.com/ddmms/ml-peg/actions/workflows/docs.yml/badge.svg
[docs-link]: https://ddmms.github.io/ml-peg/
[license-badge]: https://img.shields.io/badge/License-GPLv3-blue.svg
[license-link]: https://opensource.org/license/gpl-3-0
[doi-link]: https://doi.org/10.5281/zenodo.16904444
[doi-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.16904444.svg
[deepwiki-link]: https://deepwiki.com/ddmms/ml-peg
[deepwiki-badge]: https://deepwiki.com/badge.svg

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