This is a template repository with running examples how to join and contribute to
the s2s-ai-challenge. You were likely refered here from https://s2s-ai-challenge.github.io/.
## Introduction
## Introduction
...
@@ -9,32 +10,38 @@ bells and whistles. You'll find we have already created some
...
@@ -9,32 +10,38 @@ bells and whistles. You'll find we have already created some
useful things like `data` and `notebooks` directories and
useful things like `data` and `notebooks` directories and
a `Dockerfile`.
a `Dockerfile`.
## Working with the project
## Join the challenge
The simplest way to start your project is right from the Renku
1. The simplest way to join the S2S AI Challenge is forking this renku project.
platform - just click on the `Environments` tab and start a new session.
(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.
<imgsrc="docs/fork_renku.png"width="300">
2. Make the project private (so that other people do not steal your idea/code)
3. Add the `scorer` user to your repo with Reporter permissions (this is the lowest view-only permission so that the scorer can check results)
4. Add a gitlab variable with key `COMPETITION` and name `S2S-AI` (we can set whatever name we want here)
## Contribute
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.
This will start an interactive environment right in your browser.
To work with the project anywhere outside the Renku platform,
To work with the project anywhere outside the Renku platform,
click the `Settings` tab where you will find the
click the `Settings` tab where you will find the
git repo URLs - use `git` to clone the project on whichever machine you want.
git repo URLs - use `git` to clone the project on whichever machine you want.
### Changing interactive environment dependencies
5. Train your Machine Learning model, using training data from https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge or renku datasets
6. Let the Machine Learning model perform subseasonal 2020 predictions as netcdf files
7. Commit training notebook/pipeline and ML_prediction.nc with `git lfs`.
8. The `scorer` will fetch your predictions, score them with RPSS against recalibrated ECMWF real-time forecasts and add your score to the leaderboard at https://s2s-ai-challenge.github.io.
## Changing interactive environment dependencies
Initially we install a very minimal set of packages to keep the images small.
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
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
`environment.yml` to your heart's content. If you need more fine-grained
control over your environment, please see [the documentation](https://renku.readthedocs.io/en/latest/user/advanced_interfaces.html#dockerfile-modifications).
control over your environment, please see [the documentation](https://renku.readthedocs.io/en/latest/user/advanced_interfaces.html#dockerfile-modifications).
## Project configuration
Project options can be found in `.renku/renku.ini`. In this
project there is currently only one option, which specifies
the default type of environment to open, in this case `/lab` for
JupyterLab. You may also choose `/tree` to get to the "classic" Jupyter
interface.
## Moving forward
Once you feel at home with your project, we recommend that you replace
this README file with your own project documentation! Happy data wrangling!
