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https://github.com/ai2cm/shield-minimal

https://github.com/ai2cm/shield-minimal

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: ai2cm
  • Language: Python
  • Default Branch: main
  • Size: 23.4 KB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme

README.md

SHiELD-minimal

This repository contains a workflow that will build and test the latest public version of SHiELD in a docker image. To try things out, first make sure to download and extract the required input data for the tests from here on Google Drive into the top-level directory.

$ tar -xvf input_data.tar

Then initialize the submodules, build the docker image, and run the tests:

$ make update_submodules $ make build $ make test

The tests may or may not pass depending on the build. Try this out several times running

$ docker system prune --all

in between builds to clear out the docker cache and build fresh each time. One can observe that the tests sometimes pass and sometimes fail. For a particular docker image, however, the model always seems to produce the same result (as verified by the test that repeatedly runs the model 5 times to see that each time it produces the same result for a given build); it is only when we completely rebuild the image that results have the potential to change, suggesting this is a build-time issue.

This workflow is carried out in CircleCI and if we re-run the workflow from scratch several times, we can reproduce this behavior.

Owner

  • Name: ai2cm
  • Login: ai2cm
  • Kind: organization
  • Location: Seattle WA, USA

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Dependencies

docker/Dockerfile docker
  • ubuntu@sha256 b492494d8e0113c4ad3fe4528a4b5ff89faa5331f7d52c5c138196f69ce176a6 build
requirements.in pypi
  • fv3config *
  • pytest *
  • pytest-regtest *
requirements.txt pypi
  • aiohttp ==3.8.5
  • aiosignal ==1.3.1
  • appdirs ==1.4.4
  • async-timeout ==4.0.3
  • attrs ==23.1.0
  • cachetools ==5.3.1
  • certifi ==2023.7.22
  • charset-normalizer ==3.2.0
  • dacite ==1.8.1
  • decorator ==5.1.1
  • exceptiongroup ==1.1.3
  • f90nml ==1.4.3
  • frozenlist ==1.4.0
  • fsspec ==2023.9.1
  • fv3config ==0.9.0
  • gcsfs ==2023.9.1
  • google-api-core ==2.11.1
  • google-auth ==2.23.0
  • google-auth-oauthlib ==1.1.0
  • google-cloud-core ==2.3.3
  • google-cloud-storage ==2.10.0
  • google-crc32c ==1.5.0
  • google-resumable-media ==2.6.0
  • googleapis-common-protos ==1.60.0
  • idna ==3.4
  • iniconfig ==2.0.0
  • multidict ==6.0.4
  • oauthlib ==3.2.2
  • packaging ==23.1
  • pluggy ==1.3.0
  • protobuf ==4.24.3
  • pyasn1 ==0.5.0
  • pyasn1-modules ==0.3.0
  • pytest ==7.4.2
  • pytest-regtest ==1.5.1
  • pyyaml ==6.0.1
  • requests ==2.31.0
  • requests-oauthlib ==1.3.1
  • rsa ==4.9
  • tomli ==2.0.1
  • urllib3 ==1.26.16
  • yarl ==1.9.2