DeepClean

Single source for training, performing inference with, then analyzing the performance of DeepClean, a neural network for performing low-frequency noise subtraction in gravitational-wave observatories.

Project organization

Project is divided into libs, modular source libraries for performing the relevant signal processing and deep learning tasks, and projects, pipelines for training and inference built on top of these libraries. The gw-iaas project is included as a submodule for building a production-ready inference pipeline.

Installation

Setting up the repository

Before you do anything, be sure that the gw-iaas submodule has been initialized after cloning this repo

git submodule update
git submodule init

Environment setup

1. The Easy Way - `pinto`

The simplest way to interact with the code in this respository is to install the ML4GW Pinto command line utility, which contains all the same prerequisites (namely Conda and Poetry) that this repo does. The only difference is that rather than having to keep track of which projects require Conda and which only need Poetry separately, pinto exposes commands which build and execute scripts inside of virtual environments automatically, dynamically detecting how and where to install each project. For more information, consult the Pinto documentation linked to above.

2. The Hard Way - Conda + Poetry

Otherwise, make sure you have Conda and Poetry installed in the manner outlined in Pinto's documentation. Then create the base Conda environment on which all projects are based

conda env create -f environment.yaml

Projects that requires Conda will have a poetry.toml file in them containing in part

[virtualenvs]
create = false

For these projects, you can build the necessary virtual environment by running

conda create -n <environment name> --clone deepclean-base

then using Poetry to install additional dependencies (called from the project's root directory, not the repository's)

poetry install

Otherwise, you should just need to run poetry install from the project's directory, and Poetry will take care of creating a virtual environment automatically.

Running projects

Consult each project's documentation for additional installation steps and available commands. If you installed the pinto utility, commands can be run in each project's virtual environment by

pinto run path/to/project my-command --arg 1

Otherwise, for Conda projects, you'll have to activate the appropriate Conda environment first then execute the code inside of it. For Poetry environments, it should be enough to run poetry run my-command --arg 1 from the projects directory (one downside of Poetry is that everything has to be done locally).

ML4GW/DeepClean