Fix linter error from 2.1.0 (due to noarch: python)
#174
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The linter does not like having `noarch: python` at the top-level with selectors. Rewriting the recipe without selectors is a substantial lift and not worth it given the move towards rattler-build. However if we don't have `noarch` or a `skip` at the top-level, a full matrix of Python versions is built out for `xgboost` (despite these packages being redundant). To clean this up, we skip any Python version except the minimum one that XGBoost supports. This ensure we build for exactly one Python. To get the syntax right we need to use Jinja. Unfortunately that means we need to consolidate our other `skip`s into this one, which includes the Windows CUDA 11.8 skip. So we do so and try to make the syntax as pleasing as we can within the constraints.
…nda-forge-pinning 2024.07.25.23.42.20
jakirkham
requested review from
aldanor,
fhoehle,
hcho3,
ksangeek,
mfansler and
xhochy
as code owners
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Hi! This is the friendly automated conda-forge-linting service. I just wanted to let you know that I linted all conda-recipes in your PR ( |
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@conda-forge-admin, please re-render |
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Hi! This is the friendly automated conda-forge-webservice. I tried to rerender for you, but it looks like there was nothing to do. This message was generated by GitHub actions workflow run https://github.com/conda-forge/xgboost-feedstock/actions/runs/10105806851. |
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Hi! This is the friendly conda-forge automerge bot! I considered the following status checks when analyzing this PR:
Thus the PR was passing and merged! Have a great day! |
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The linter does not like having
noarch: pythonat the top-level with selectors ( #167 (comment) ) ( conda-forge/conda-smithy#1887 (comment) ). Rewriting the recipe without selectors is a substantial lift and not worth it given the move towards rattler-build.However if we don't have
noarchor askipat the top-level, a full matrix of Python versions is built out for all Python XGBoost packages (despite these packages being redundant). Example below taken from this CI run (attached log for posterity):Packages generated by Python matrix in past CI run:
{ "_py-xgboost-mutex-2.0-cpu_0.conda": { "recipe": { "channel_targets": "conda-forge main", "target_platform": "linux-64" } }, "_r-xgboost-mutex-2.0-cpu_0.conda": { "recipe": { "channel_targets": "conda-forge main", "target_platform": "linux-64" } }, "libxgboost-2.0.3-cpu_hce603c2_3.conda": { "recipe": { "__glibc": "__glibc >=2.17,<3.0.a0", "c_compiler": "gcc", "c_compiler_version": "12", "channel_targets": "conda-forge main", "cuda_compiler": "None", "cuda_compiler_version": "None", "cxx_compiler": "gxx", "cxx_compiler_version": "12", "target_platform": "linux-64" } }, "py-xgboost-2.0.3-cpu_pyh0a621ce_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.11.* *_cpython", "target_platform": "linux-64" } }, "py-xgboost-2.0.3-cpu_pyh78d450f_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.10.* *_cpython", "target_platform": "linux-64" } }, "py-xgboost-2.0.3-cpu_pyh995e691_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.8.* *_cpython", "target_platform": "linux-64" } }, "py-xgboost-2.0.3-cpu_pyhbc962bd_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.12.* *_cpython", "target_platform": "linux-64" } }, "py-xgboost-2.0.3-cpu_pyhc68e8b7_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.9.* *_cpython", "target_platform": "linux-64" } }, "py-xgboost-cpu-2.0.3-pyh099c497_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.12.* *_cpython" } }, "py-xgboost-cpu-2.0.3-pyh4f80c01_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.10.* *_cpython" } }, "py-xgboost-cpu-2.0.3-pyhac85b48_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.9.* *_cpython" } }, "py-xgboost-cpu-2.0.3-pyhb06c54e_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.11.* *_cpython" } }, "py-xgboost-cpu-2.0.3-pyhb8f9a19_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.8.* *_cpython" } }, "r-xgboost-2.0.3-cpu_r42h566b21a_3.conda": { "recipe": { "__glibc": "__glibc >=2.17,<3.0.a0", "c_compiler": "gcc", "c_compiler_version": "12", "channel_targets": "conda-forge main", "cuda_compiler": "None", "cxx_compiler": "gxx", "cxx_compiler_version": "12", "target_platform": "linux-64" } }, "r-xgboost-2.0.3-cpu_r43h566b21a_3.conda": { "recipe": { "__glibc": "__glibc >=2.17,<3.0.a0", "c_compiler": "gcc", "c_compiler_version": "12", "channel_targets": "conda-forge main", "cuda_compiler": "None", "cxx_compiler": "gxx", "cxx_compiler_version": "12", "target_platform": "linux-64" } }, "r-xgboost-cpu-2.0.3-r42hf5c67e3_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "target_platform": "linux-64" } }, "r-xgboost-cpu-2.0.3-r43hf5c67e3_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "target_platform": "linux-64" } }, "xgboost-2.0.3-cpu_pyh099c497_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.12.* *_cpython" } }, "xgboost-2.0.3-cpu_pyh4f80c01_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.10.* *_cpython" } }, "xgboost-2.0.3-cpu_pyhac85b48_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.9.* *_cpython" } }, "xgboost-2.0.3-cpu_pyhb06c54e_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.11.* *_cpython" } }, "xgboost-2.0.3-cpu_pyhb8f9a19_3.conda": { "recipe": { "channel_targets": "conda-forge main", "cuda_compiler": "None", "python": "3.8.* *_cpython" } } }To clean this up, we skip any Python version except the minimum one that XGBoost supports. This ensures we build for exactly one Python. To get the syntax right we need to use Jinja.
Unfortunately that means we need to consolidate our other
skips into this one, which includes the Windows CUDA 11.8 skip. So we do so and try to make the syntax as pleasing as we can within the constraints.That all being said, this does keep the linter happy and it re-renders without generated unneeded Python builds.
Checklist
0(if the version changed)conda-smithy(Use the phrase@conda-forge-admin, please rerenderin a comment in this PR for automated rerendering)