GravyFlow

TensorFlow tools to facilitate machine learning for gravitational-wave data analysis.

Installation Guide

1. Clone the Repository

GravyFlow can be installed by cloning the Git repository:

git clone https://github.com/mrknorman/gravyflow

2. Install Gravyflow

It is recommended to install GravyFlow within a new conda environment. GravyFlow requires Python 3.11:

conda create --name gravyflow python=3.13
conda activate gravyflow

Next, ensure pip is installed by running:

conda install pip 

Then, install GravyFlow and its requirements into your conda environment:

pip install -e .[cuda]

Note that GravyFlow is under active development, and you may encounter issues during installation. Ensure TensorFlow can recognize GPUs in your environment, as GravyFlow is optimized for GPU use and relies on vectorized GPU functions.

4. Setup permissions:

Follow these guides for setting up permissions to access real data:

https://computing.docs.ligo.org/guide/auth/scitokens/ https://computing.docs.ligo.org/guide/auth/kerberos/

5. Setup Gravity Spy Permissions:

Access Gravity Spy credentials by logging in with your LIGO credentials at:

https://secrets.ligo.org/secrets/144/

Then, export the obtained username and password:

export GRAVITYSPY_DATABASE_USER=<user>
export GRAVITYSPY_DATABASE_PASSWD=<password>

6. Test Gravyflow (optional)

GravyFlow includes PyTest for testing its functionality. To run tests:

pytest gravyflow

Note: Tests may fail due to unavailable GPU memory if GPUs are currently under heavy use.