S2S AI Challenge Template
This is a template repository with running examples how to join and contribute to
the s2s-ai-challenge.
You were likely referred here from the public website.
The competition starts in June, so examples are still work in progress and joining the competition not possible until then, but you can already look around. If you fork this project before June, please rebase or fork again in June.
Find an overview of repositories and websites
Introduction
This is a Renku project - basically a git repository with some
bells and whistles. You'll find we have already created some
useful things like data and notebooks directories and
a Dockerfile.
Join the challenge
1. The simplest way to join the S2S AI Challenge is forking this renku project.
Ensure you do not fork the gitlab repository, but the renku project.
Fork this template renku project from https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/settings.
Name your fork s2s-ai-challenge-$TEAMNAME.
2. Fill our registration form.
3. Make the project private
Now check out the gitlab repository by clicking on "View in gitlab". Under "Settings" - "General" - "Visibility" you can set your project private.
Now other people cannot steal your idea/code.
Now please modify the README in your fork with team details and a description of your method.
4. Add the scorer user to your repo with Reporter permissions
The scorer is not yet ready, but will follow this verification notebook.
5. Add a gitlab variable with key COMPETITION and name S2S-AI
In the gitlab repository, under "Settings" -> "CI/CD" -> "Variables", add the
COMPETITION key with value S2S-AI, so the scorer bot knows where to search
for submissions.
Contribute
6. Start jupyter on renku or locally
The simplest way to contribute is right from the Renku platform -
just click on the Environments tab in your renku project and start a new session.
This will start an interactive environment right in your browser.
If the docker image fails initially, please re-build docker or touch the enviroment.yml file.
To work with the project anywhere outside the Renku platform,
click the Settings tab where you will find the
renku project URLs - use renku clone to clone the project on whichever machine you want.
Install renku first with pipx,
and then renku clone https://renkulab.io/gitlab/$YOURNAME/s2s-ai-challenge-$GROUPNAME.git
7. Train your Machine Learning model
Get training data via
Get corresponding observations/ground truth:
- climetlab
- IRIDL: temperature and accumulated precipitation
8. Let the Machine Learning model perform subseasonal 2020 predictions
and save them as netcdf files.
The submissions have to placed in the submissions folder with filename submission_your_choice.nc,
see example.
9. git commit training pipeline and netcdf submission
For later verification of the organizers, reproducibility and scoring of submissions,
the training notebook/pipeline and submission file ML_prediction.nc with git lfs.
After commiting, git tag submission-method_name-number
git lfs track "*.nc" # once, already done in template
git add submissions/submission_my_method.nc
git commit -m "commit submission for my_method"
git tag "submission-my_method-0.0.1" # if this is to be checked by scorer
git push --tags
10. RPSS scoring by scorer bot
The scorer will fetch your tagged submissions, score them with RPSS against recalibrated ECMWF real-time forecasts.
Your score will be added to the leaderboard.
The scorer is not active for the competition yet.
More information
in the wiki
Changing interactive environment dependencies
Initially we install a very minimal set of packages to keep the images small.
However, you can add python and conda packages in requirements.txt and
environment.yml to your heart's content. If you need more fine-grained
control over your environment, please see the documentation.