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 https://s2s-ai-challenge.github.io/.
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 reku project).
Fork this template renku project from https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/settings.

2. 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.
scorer
user to your repo with Reporter permissions
3. Add the The scorer is not yet ready, but will follow this verification notebook.
COMPETITION
and name S2S-AI
4. Add a gitlab variable with key 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
5. Start jupyter
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
git repo URLs - use git
to clone the project on whichever machine you want.
6. Train your Machine Learning model
using training data from https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge or renku datasets
7. Let the Machine Learning model perform subseasonal 2020 predictions
and save them as netcdf
files.
git commit
training pipeline and netcdf submission
8. For later verification of the organizers, reproducibility and scoring of submissions,
the training notebook/pipeline and submission file ML_prediction.nc with git lfs
.
scorer
bot
9. RPSS scoring by The scorer
will fetch your predictions, score them with RPSS against recalibrated ECMWF real-time forecasts.
Your score will be added to the leaderboard at https://s2s-ai-challenge.github.io/#leaderboard
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.