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 that 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
.
Your fork will inherit the tags from the template repo. The tag s2s-ai-challenge
is needed for the scorer
bot to find your repo.
registration form.
2. Fill ourRegistrations are not required before October 31st 2021, but highly appreciated for the flow of information.
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.
Please use different branches if you try out different methods. The scorer finds branches from all branches.
scorer
user to your repo with Reporter permissions
4. Add the The scorer is not yet ready, but will follow this verification notebook.
Todo: How to add scorer to repo
Make Predictions
5. 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
6. Train your Machine Learning model
Get training data via
Get corresponding observations/ground truth:
- climetlab
- IRIDL: temperature and accumulated precipitation
7. 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.
git commit
training pipeline and netcdf submission
8. For later verification of the organizers and reviewers, reproducibility and scoring of submissions,
the training notebook/pipeline and submission file ML_prediction_2020.nc
must be committed with git lfs
:
# run your training and create file ../submissions/ML_prediction_2020.nc
git lfs track "*.nc" # do once, already done in template
git add ../submissions/ML_prediction_2020.nc
git commit -m "commit submission for my_method_name" # whatever message you want
git tag "submission-my_method_name-0.0.1" # if this is to be checked by scorer, only the last submitted==tagged version will be considered
git push --tags
scorer
bot
9. RPSS scoring by The scorer
will fetch your tagged submissions, score them with RPSS against recalibrated ECMWF real-time forecasts.
Your score will be added to the private leaderboard, which will be made public in early November 2021.
The scorer
is not active for the competition yet.
More information
- in the
s2s-ai-challenge
wiki - all different resources for this competition
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.