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LEAP Template Feedstock

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

Setup

Every dataset that is part of the LEAP-Pangeo Data Library is represented by a repository. You can easily create one by following the instructions below.

Use this template

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

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.
  • Optional but encouraged: Give the leap-stc/data-and-compute team admin access (so we can make changes without bothering you). To do so go to Settings > Collaborators and Teams > Add Teams and add leap-stc/data-and-compute with admin role.

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.

Are you linking or ingesting data?

If the data you want to work with is already available as ARCO format in a publically accessible cloud bucket, you can simply link it and add it to the LEAP catalog.

If you want to transform your dataset from e.g. a bunch of netcdf files into a zarr store, you have to build a Pangeo-Forge recipe.

Linking existing ARCO datasets

To link an existing dataset all you need to do is to modify 'feedstock/meta.yaml' and 'feedstock/catalog.yaml'. Enter the information about the dataset in 'feedstock/meta.yaml' and then add corresponding entries (the 'id' parameter has to match) in 'feedstock/catalog.yaml', where the url can point to any publically available cloud storage.

Example from the [`arco-era5_feedstock](https://github.com/leap-stc/arco-era5_feedstock):

meta.yaml

title: "ARCO ERA5"
description: >
   Analysis-Ready, Cloud Optimized ERA5 data ingested by Google Research
recipes:
  - id: "0_25_deg_pressure_surface_levels"
  - id: "0_25_deg_model_levels"
provenance:
  providers:
    - name: "Google Research"
      description: >
      Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
      Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
      Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G.,
      Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G.,
      Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J.,
      Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S.,
      Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P.,
      Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F.,
      Villaume, S., Thépaut, J-N. (2017): Complete ERA5: Fifth generation of
      ECMWF atmospheric reanalyses of the global climate. Copernicus Climate
      Change Service (C3S) Data Store (CDS).

      Hersbach et al, (2017) was downloaded from the Copernicus Climate Change
      Service (C3S) Climate Data Store. We thank C3S for allowing us to
      redistribute the data.

      The results contain modified Copernicus Climate Change Service
      information 2022. Neither the European Commission nor ECMWF is
      responsible for any use that may be made of the Copernicus information
      or data it contains.
      roles:
        - producer
        - licensor
  license: "Apache Version 2.0"
maintainers:
  - name: "Julius Busecke"
    orcid: "0000-0001-8571-865X"
    github: jbusecke

catalog.yaml

# All the information important to cataloging.
"ncviewjs:meta_yaml_url": "https://github.com/leap-stc/arco-era5_feedstock/blob/main/feedstock/meta.yaml" # !!! Make sure to change this to YOUR feedstock!!!
tags:
  - atmosphere
  - reanalysis
  - zarr
stores:
  - id: "0_25_deg_pressure_surface_levels"
    name: "This dataset contains most pressure-level fields and all surface-level field regridded to a uniform 0.25° resolution. It is a superset of the data used to train GraphCast and NeuralGCM"
    url: "gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3"

  - id: "0_25_deg_model_levels"
    name: "This dataset contains 3D fields at 0.25° resolution with ERA5's native vertical coordinates (hybrid pressure/sigma coordinates)."
    url: "'gs://gcp-public-data-arco-era5/ar/model-level-1h-0p25deg.zarr-v1'"

Build a Pangeo-Forge Recipe
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
  • 🚨 You should not have to modify any of the files outside the feedstock folder (and this README)! If you run into a situation where you think changes are needed, please open an issue and tag @leap-stc/data-and-compute.

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.

  1. 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
  1. 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.

  1. 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 and choose the appropriate branch.

[!WARNING] We recently ran into problems with PRs based on forked repositories (example), which cannot be run via the Actions "Run Workflow" button/trigger. Until we find a workable solution here we recommend to not fork the feedstock repo and work only with branches on the main feedstock

  • 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. Open a PR to our catalog input file and add a link to this repos 'catalog.yaml' there. See here for an example PR for the `arco-era5_feedstock.

Clean up

  • Replace the instructions in this README.