Installation
============

`AMPLFI` can be installed with `pip` from PyPI 

```console
$ pip install amplfi
```

or directly from source


```console
$ git clone git@github.com:ML4GW/amplfi.git 
$ cd amplfi
$ pip install .  
```

It is highly recommended that you install `AMPLFI` in a virtual environment using tools
like `venv` or `uv`. `AMPLFI` is managed using `uv`, which is the tool
we recommend.

::::{tab-set}

:::{tab-item} uv
```console
$ git clone git@git.ligo.org:ml4gw/amplfi.git
$ cd amplfi
$ uv sync
```
:::

:::{tab-item} venv
```console
$ git clone git@git.ligo.org:ml4gw/amplfi.git
$ cd amplfi
$ python -m venv ./venv 
$ source ./venv/bin/activate
```
:::

::::
Supported python versions: 3.10-3.12.

## Data Generation
Currently, running the data generation workflow that queries strain data requires utilizing the `AMPLFI` container.
First, set the `$AMPLFI_CONTAINER_ROOT` where you would like the image stored.

```console
export AMPLFI_CONTAINER_ROOT=~/amplfi/images
```

Then you can either pull the container from the remote github repository

::::{tab-set}

:::{tab-item} apptainer
```console
$ apptainer pull ${AMPLFI_CONTAINER_ROOT}/amplfi.sif docker://ghcr.io/ml4gw/amplfi/amplfi:main
```
:::

:::{tab-item} docker
```console
$ docker pull ghcr.io/ml4gw/amplfi/amplfi:main
```
:::

::::
Supported python versions: 3.10-3.12.

Or build the container locally

```console
$ git clone git@git.ligo.org:ml4gw/amplfi.git
$ cd amplfi
$ apptainer build $AMPLFI_CONTAINER_ROOT/amplfi.sif apptainer.def
```

This container can be also be used for launching the training
and tuning pipelines, but is not strictly required. See the [container
documentation](./containers.md) for more information.