Normalising Flows for Inverse Problems (NF4IP) is a Python framework that provides all means for usage of normalising flows in solving inverse problems. The framework allows low-key usage of recent advances in machine learning for analysis of experimental data based on Surrogate Models as well as one-step solution to inverse problems.

The framework is being collaboratively developed by

• Photon AI Research
• HZDR's AI Consultant Team for MATTER research
• Helm & Walther IT Solutions

Maintainer:

• Nico Hoffmann of PAIR

The aim of this project is to make it easier to solve inverse problems. NF4IP is based on FrEIA from Heidelberg University, though while FrEIA provides a toolbox for building invertible networks, NF4IP uses this toolbox to offer a selection of ready-to-use network architectures tailored to inverse problems typically encountered in physics.

In addition, NF4IP already offers useful features that simplify the development of your machine learning application:

• powerful configuration options with defaults, config files and commandline arguments.
• a code generator to give you a quick start with your project
• checkpointing: automatically save your learning progress and restore from it.
• Filter/Hook system to customize the application and plug your code into the library
• Plugin/Extension System to extend the functionality
• These extensions are currently available:
  • NF implementation
  • Tensorboard logging
  • Variational Autoencoder

To install NF4IP, clone this repository, enter the cloned directory and install it locally using pip:

git clone https://github.com/Photon-AI-Research/NF4IP.git
cd NF4IP
pip install -e setup.py

The -e will make an editable install, so you can edit the NF4IP library any time and see the effect instantly.

You can start generating your first project by executing:

NF4IP generate project <targetDir>

where is the directory where the new project will be generated. This tutorial assumes that you used the default "myapp" label name.

After executing this command and answering all questions, you can open the targetDir in your favorite IDE. If you have no preference, we recommend the free pyCharm community edition.

The directory structure created is basicly a Cement 3.0-Project. The NF4IP documentation will already give you everything you need to know to start your first project. Additionally, cement got its own documentation which should be consulted if there are open questions concerning the generation of CLI applications based on NF4IP.

These instructions are for anyone interested in improving NF4IP itself. The following steps are not necessary if you want to develop an application based on NF4IP. This project includes a number of helpers in the Makefile to streamline common development tasks.

The following demonstrates setting up and working with a development environment:

### create a virtualenv for development

$ make virtualenv

$ source env/bin/activate


### run NF4IP cli application

$ NF4IP --help


### run pytest / coverage

$ make test

Before releasing to PyPi, you must configure your login credentials:

~/.pypirc:

[pypi]
username = YOUR_USERNAME
password = YOUR_PASSWORD

Then use the included helper function via the Makefile:

$ make dist

$ make dist-upload

Included is a basic Dockerfile for building and distributing NF4IP, and can be built with the included make helper:

$ make docker

$ docker run -it NF4IP --help