139 lines
6.0 KiB
Markdown
139 lines
6.0 KiB
Markdown
Gonito platform
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===============
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[Gonito](https://gonito.net) (pronounced _ɡɔ̃ˈɲitɔ_) is a Kaggle-like
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platform for machine learning competitions (disclaimer: Gonito is
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neither affiliated with nor endorsed by [Kaggle](https://www.kaggle.com)).
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What's so special about Gonito:
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* free & open-source (GPL), you can use it your own, in your
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company, at your university, etc.
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* git-based (challenges and solutions are submitted only with git).
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See the home page (and an instance of Gonito) at https://gonito.net .
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Installation
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------------
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[Gonito](https://gonito.net) is written in [Haskell](https://www.haskell.org) and uses
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[Yesod Web Framework](http://www.yesodweb.com/), but all you need is
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just [the Stack tool](https://github.com/commercialhaskell/stack). See https://github.com/commercialhaskell/stack
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for instruction how to install Stack on your computer.
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By default, Gonito uses [Postgresql](http://www.postgresql.org/), so it needs to be installed and running at your computer.
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After installing Stack:
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createdb -E utf8 gonito
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git clone git://gonito.net/geval
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git clone git://gonito.net/gonito
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cd gonito
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stack setup
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# before starting the build you might need some non-Haskell dependencies, e.g. in Ubuntu:
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# sudo apt-get install libbz2-dev liblzma-dev libpcre3-dev libcairo-dev libfcgi-dev
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stack build
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stack exec yesod devel
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The last command will start the Web server with Gonito (go to
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http://127.0.0.1:3000 in your browser).
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Gonito & git
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------------
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Gonito uses git in an inherent manner:
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* challenges (data sets) are provided as git repositories,
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* submissions are uploaded via git repositories, they are referred to with
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git commit hashes.
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Advantages:
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* great flexibility as far as where you want to keep your challenges
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and submissions (could be external, well-known services such as
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GitHub or GitLab, your local git server, let's say gitolite or Gogs, or
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just a disk accessible in a Gonito instance),
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* even if Gonito ceases to exist, the challenges and submissions are still available
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in a standard manner, provided that git repositories (be it external or local) are
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accessible,
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* data sets can be easily downloaded using the command line
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(e.g. `git clone git://gonito.net/paranormal-or-skeptic`), without
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even clicking anything in the Web browser,
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* facilitates experiment repeatability and reproducibility (at worst
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the system output is easily available via git)
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* tools that were used to generate the output could be linked as git subrepositories
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* some challenge/submission metadata are tracked in a Gonito-independent way
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(within git commits),
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* copying data can be avoided with git mechanisms (e.g. when the challenge is already
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cloned, downloading specific submissions should be much quicker),
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* large data sets and models could be stored if needed using mechanisms such as git-annex (see below).
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### Commit structure
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The following flow of git commits is recommended (though not required):
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* the challenge without hidden data for main test sets (i.e. files such as `test-A/expected.tsv`)
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should be pushed to the `master` branch
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* the hidden files (`test-A/expected.tsv`) should be added in a
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subsequent commit and pushed either to the `dont-peek` branch or a
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`master` branch of a separate repository (if access to the hidden
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data must be more strict),
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* the submissions should be committed with the `master` branch as the
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parent (or at least ancestor) commit and pushed to the same
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repository as the challenge data (in some user-specific branch) or any other
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repository (could be user-owned repositories)
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* any subsequent submissions could be derived in a natural way from other git commits
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(e.g. when a submission is improved, or even two approaches are merged)
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* new versions of the challenge can be committed (a challenge can be updated at Gonito)
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to the `master` (and `dont-peek`) branches
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See also the following picture:
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### git-annex
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In some cases, you don't want to store challenge/submissions files simply in git:
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* very large data files, textual files (e.g. `train/in.tsv` even if
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compressed as `train/in.tsv.xz`)
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* binary training/testing data (PDF files, images, movies, recordings)
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* data sensitive due to privacy/security concerns (a scenario where it's OK to store
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metadata and some files in a widely accessible repository, but some files require
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limited access)
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* large ML models (note that Gonito does not require models for evaluation, but still
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it might be a good practice to commit them along with output files and scripts)
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Such cases can be handled in a natural manner using git-annex, a git
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extension for handling files and their metadata without commiting
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their content to the repository. The contents can be stored at a wide
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range of [special
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remotes](https://git-annex.branchable.com/special_remotes/), e.g. S3
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buckets, WebDAV, rsync servers.
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It's up to you which files are stored in git in a regular manner and
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which are added with `git annex add`, but note that if a
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challenge/submission file must be stored via git-annex and are required
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for evaluation (e.g. `expected.tsv` files for the challenge or
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`out.tsv` files for submissions), the git-annex special remote must be
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given when a challenge is created or a submission is done and the
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Gonito server must have access to such a special remote.
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Authors
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-------
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* Filip Graliński
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References
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----------
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@inproceedings{gralinski:2016:gonito,
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title="{Gonito.net - Open Platform for Research Competition, Cooperation and Reproducibility}",
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author={Grali{\'n}ski, Filip and Jaworski, Rafa{\l} and Borchmann, {\L}ukasz and Wierzcho{\'n}, Piotr},
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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}",
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pages={13--20},
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year=2016,
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url="http://4real.di.fc.ul.pt/wp-content/uploads/2016/04/4REALWorkshopProceedings.pdf"
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}
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