gonito/README.md

Gonito platform

Gonito (pronounced ɡɔ̃ˈɲitɔ) is a Kaggle-like platform for machine learning competitions (disclaimer: Gonito is neither affiliated with nor endorsed by Kaggle).

What's so special about Gonito:

• free & open-source (GPL), you can use it your own, in your company, at your university, etc.
• git-based (challenges and solutions are submitted only with git).

See the home page (and an instance of Gonito) at https://gonito.net .

Installation

Gonito is written in Haskell and uses Yesod Web Framework, but all you need is just the Stack tool. See https://github.com/commercialhaskell/stack for instruction how to install Stack on your computer.

By default, Gonito uses Postgresql, so it needs to be installed and running at your computer.

After installing Stack:

createdb -E utf8 gonito
git clone git://gonito.net/geval
git clone git://gonito.net/gonito
cd gonito
stack setup
# before starting the build you might need some non-Haskell dependencies, e.g. in Ubuntu:
# sudo apt-get install libbz2-dev liblzma-dev libpcre3-dev libcairo-dev libfcgi-dev
stack build
stack exec yesod devel

The last command will start the Web server with Gonito (go to http://127.0.0.1:3000 in your browser).

Gonito & git

Gonito uses git in an inherent manner:

• challenges (data sets) are provided as git repositories,
• submissions are uploaded via git repositories, they are referred to with git commit hashes.

Advantages:

• great flexibility as far as where you want to keep your challenges and submissions (could be external, well-known services such as GitHub or GitLab, your local git server, let's say gitolite or Gogs, or just a disk accessible in a Gonito instance),
• even if Gonito ceases to exist, the challenges and submissions are still available in a standard manner, provided that git repositories (be it external or local) are accessible,
• data sets can be easily downloaded using the command line (e.g. git clone git://gonito.net/paranormal-or-skeptic), without even clicking anything in the Web browser,
• facilitates experiment repeatability and reproducibility (at worst the system output is easily available via git)
• tools that were used to generate the output could be linked as git subrepositories
• some challenge/submission metadata are tracked in a Gonito-independent way (within git commits),
• copying data can be avoided with git mechanisms (e.g. when the challenge is already cloned, downloading specific submissions should be much quicker),
• large data sets and models could be stored if needed using mechanisms such as git-annex (see below).

Commit structure

The following flow of git commits is recommended (though not required):

• the challenge without hidden data for main test sets (i.e. files such as test-A/expected.tsv) should be pushed to the master branch
• the hidden files (test-A/expected.tsv) should be added in a subsequent commit and pushed either to the dont-peek branch or a master branch of a separate repository (if access to the hidden data must be more strict),
• the submissions should be committed with the master branch as the parent (or at least ancestor) commit and pushed to the same repository as the challenge data (in some user-specific branch) or any other repository (could be user-owned repositories)
• any subsequent submissions could be derived in a natural way from other git commits (e.g. when a submission is improved, or even two approaches are merged)
• new versions of the challenge can be committed (a challenge can be updated at Gonito) to the master (and dont-peek) branches

See also the following picture:

git-annex

In some cases, you don't want to store challenge/submissions files simply in git:

• very large data files, textual files (e.g. train/in.tsv even if compressed as train/in.tsv.xz)
• binary training/testing data (PDF files, images, movies, recordings)
• data sensitive due to privacy/security concerns (a scenario where it's OK to store metadata and some files in a widely accessible repository, but some files require limited access)
• large ML models (note that Gonito does not require models for evaluation, but still it might be a good practice to commit them along with output files and scripts)

Such cases can be handled in a natural manner using git-annex, a git extension for handling files and their metadata without commiting their content to the repository. The contents can be stored at a wide range of special remotes, e.g. S3 buckets, WebDAV, rsync servers.

It's up to you which files are stored in git in a regular manner and which are added with git annex add, but note that if a challenge/submission file must be stored via git-annex and are required for evaluation (e.g. expected.tsv files for the challenge or out.tsv files for submissions), the git-annex special remote must be given when a challenge is created or a submission is done and the Gonito server must have access to such a special remote.

Authors

• Filip Graliński

References

@inproceedings{gralinski:2016:gonito,
  title="{Gonito.net - Open Platform for Research Competition, Cooperation and Reproducibility}",
  author={Grali{\'n}ski, Filip and Jaworski, Rafa{\l} and Borchmann, {\L}ukasz and Wierzcho{\'n}, Piotr},
  booktitle="{Branco, Ant{\'o}nio and Nicoletta Calzolari and Khalid Choukri (eds.), Proceedings of the 4REAL Workshop: Workshop on Research Results Reproducibility and Resources Citation in Science and Technology of Language}",
  pages={13--20},
  year=2016,
  url="http://4real.di.fc.ul.pt/wp-content/uploads/2016/04/4REALWorkshopProceedings.pdf"
}