Gonito platform =============== [Gonito](https://gonito.net) (pronounced _ɡɔ̃ˈɲitɔ_) is a Kaggle-like platform for machine learning competitions (disclaimer: Gonito is neither affiliated with nor endorsed by [Kaggle](https://www.kaggle.com)). 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](https://gonito.net) is written in [Haskell](https://www.haskell.org) and uses [Yesod Web Framework](http://www.yesodweb.com/), but all you need is just [the Stack tool](https://github.com/commercialhaskell/stack). See https://github.com/commercialhaskell/stack for instruction how to install Stack on your computer. By default, Gonito uses [Postgresql](http://www.postgresql.org/), 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: ![Recommended commit structure](misc/commits.png) ### 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](https://git-annex.branchable.com/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" }