Welcome to Fragile’s documentation#

Fragile#

Documentation Status Code coverage PyPI package Latest docker image Code style: black license: MIT

Fragile is a framework for developing optimization algorithms inspired by Fractal AI and running them at scale.

Features#

  • Provides classes and an API for easily developing planning algorithms

  • Provides an classes and an API for function optimization

  • Build in visualizations of the sampling process

  • Fully documented and tested (In progress)

  • Support for parallelization and distributed search processes (In progress)

About FractalAI#

FractalAI is based on the framework of non-equilibrium thermodynamics, and can be used to derive new mathematical tools for efficiently exploring state spaces.

The principles of our work are accessible online:

  • Arxiv manuscript describing the fundamental principles of our work.

  • Blog that describes our early research process.

  • Youtube channel with videos showing how different prototypes work.

  • GitHub repository containing a prototype that solves most Atari games.

Getting started#

Check out the getting started section of the docs, or the examples folder.

Running in docker#

The fragile docker container will execute a Jupyter notebook accessible on port 8080 with password: fragile

You can pull a docker image from Docker Hub running:

    docker pull fragiletech/fragile:version-tag

Where version-tag corresponds to the fragile version that will be installed in the pulled image.

Installation#

This framework has been tested in Ubuntu 18.04 and supports Python 3.8 and 3.9. If you find any problems running it in a different OS or Python version please open an issue.

It can be installed with pip install fragile["all"].

You can find the pinned versions of the minimum requirements to install the core module in requirements.txt, and the pinned versions of all the optional requirements in requirements-all.txt.

Detailed installation instructions can be found in the docs.

Documentation#

You can access the documentation on Read The Docs.

Roadmap#

Upcoming features: (not necessarily in order)

  • Fix documentation and add examples for the distributed module

  • Upload Montezuma solver

  • Add new algorithms to sample different state spaces.

  • Add a benchmarking module

  • Add deep learning API

Contributing#

Contribution are welcome. Please take a look at contributining and respect the code of conduct.

Cite us#

If you use this framework in your research please cite us as:

@misc{1803.05049,
    Author = {Sergio Hernández Cerezo and Guillem Duran Ballester},
    Title = {Fractal AI: A fragile theory of intelligence},
    Year = {2018},
    Eprint = {arXiv:1803.05049},
}

License#

This project is MIT licensed. See LICENSE.md for the complete text.

Getting started

API Reference

Search Page