LEAP Template Feedstock

This repository serves as template/documentation/testing ground for Leap-Pangeo Data Library Feedstocks.

Setup

Use this template

Click on the button on the top left to use this repository as a template for your new feedstock

[!IMPORTANT]

Make the repo public

Make sure to create the repo under the leap-stc github organization, not your personal account!

Name your feedstock according to your data <your_data>_feedstock.

If you made a mistake here it is not a huge problem. All these settings can be changed after you created the repo.

Now you can locally check out the repository.

[!NOTE] The instructions below are specific for testing recipes locally but downloading and producing data on GCS cloud buckets. If you are running the recipes locally you have to minimally modify some of the steps as noted below.

Build and test your recipe locally on the LEAP-Pangeo Jupyterhub

Edit the feedstock/recipe.py to build your pangeo-forge recipe. If you are new to pangeo-forge, the docs are a great starting point
Make sure to also edit the other files in the /feedstock/ directory. More info on feedstock structure can be found here

Test your recipe locally

Before we run your recipe on LEAPs Dataflow runner you should test your recipe locally.

You can do that on the LEAP-Pangeo Jupyterhub or your own computer.

Set up an environment with mamba or conda:

mamba create -n runner0102 python=3.11 -y
conda activate runner0102
pip install pangeo-forge-runner==0.10.2 --no-cache-dir

You can now use pangeo-forge-runner from the root directory of a checked out version of this repository in the shell

pangeo-forge-runner bake \
  --repo=./ \
  --Bake.recipe_id=<recipe_id>\
  -f configs/config_local_hub.py

[!NOTE] Make sure to replace the 'recipe_id' with the one defined in your feedstock/meta.yaml file.

If you created multiple recipes you have to run a call like above for each one.

To run this fully local (e.g. on your laptop) you have to replace config_local_hub.py with config_local.py.

⚠️ This will save the cache and output to a subfolder of the location you are executing this from.. Make sure do delete them once you are done with testing.

Check the output! If something looks off edit your recipe.

[!TIP] The above command will by default 'prune' the recipe, meaning it will only use two of the input files you provided to avoid creating too large output. Keep that in mind when you check the output for correctness.

Once you are happy with the output it is time to commit your work to git, push to github and get this recipe set up for ingestion using Google Dataflow

Activate the linting CI and clean up your repo

Pre-Commit linting is already pre-configured in this repository. To run the checks locally simply do:

pip install pre-commit
pre-commit install
pre-commit run --all-files

Then create a new branch and add those fixes (and others that were not able to auto-fix). From now on pre-commit will run checks after every commit.

Alternatively (or additionally) you can use the pre-commit CI Github App to run these checks as part of every PR. To proceed with this step you will need assistance a memeber of the LEAP Data and Computation Team. Please open an issue on this repository and tag @leap-stc/data-and-compute and ask for this repository to be added to the pre-commit.ci app.

Deploy your recipe to LEAPs Google Dataflow

[!WARNING] To proceed with this step you will need to have certain repository secrets set up. For security reasons this should be done by a memeber of the LEAP Data and Computation Team. Please open an issue on this repository and tag @leap-stc/data-and-compute to get assistance.

To deploy a recipe to Google Dataflow you have to trigger the "Deploy Recipes to Google Dataflow" with a single recipe_id as input.
Once your recipe is run from a github workflow we assume that it is deployed to Google Dataflow and activate the final copy stage. This happens automatically, but you have to make sure to edit the 'feedstock/catalog.yaml' url entries for each recipe_id. This location will be the 'final' location of the data, and this is what gets passed to the the catalog in the next step!

[!NOTE] By default the 'prune' option is set to true. To build the final dataset you need to change that value here. Particularly for large datasets make sure that you have finalized the entries in 'feedstock/catalog.yaml', since the full build of the dataset can be slow and expensive - you want to avoid doing that again 😁

Add your dataset to the LEAP-Pangeo Catalog

Now that your awesome dataset is available as an ARCO zarr store, you should make sure that everyone else at LEAP can check this dataset out easily.

leap-stc / eNATL_feedstock

readme