Commit d5a3d882 authored by Rene Ladan's avatar Rene Ladan
Browse files

misc/py-torch-geometric: Remove expired port 

2026-09-15 misc/py-torch-geometric: Depends on expiring math/py-dgl
parent d55bab21

MOVED

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@@ -5602,3 +5602,4 @@ math/libformfactor|math/formfactor|2026-09-12|Project was renamed upstream from 
math/py-pygslodeiv2||2026-09-15|Has expired: Depends on expired lang/cython0

devel/py-python-dtrace||2026-09-15|Has expired: Depends on expired lang/cython0

graphics/py-djvulibre||2026-09-15|Has expired: Depends on expired lang/cython0

misc/py-torch-geometric||2026-09-15|Has expired: Depends on expiring math/py-dgl

misc/Makefile

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@@ -697,7 +697,6 @@ 
    SUBDIR += py-timm

    SUBDIR += py-timm-vis

    SUBDIR += py-toil

    SUBDIR += py-torch-geometric

    SUBDIR += py-torchao

    SUBDIR += py-torchdata

    SUBDIR += py-torchmetrics

misc/py-torch-geometric/Makefile

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PORTNAME=	torch-geometric

DISTVERSION=	2.8.0

PORTREVISION=	1

CATEGORIES=	misc python # machine-learning

#MASTER_SITES=	PYPI # no tests

PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}

DISTNAME=	${PORTNAME:S/-/_/}-${PORTVERSION}



MAINTAINER=	yuri@FreeBSD.org

COMMENT=	Graph neural network library for PyTorch

WWW=		https://pyg.org/ \

		https://pytorch-geometric.readthedocs.io/en/latest/ \

		https://github.com/pyg-team/pytorch_geometric



LICENSE=	MIT



DEPRECATED=	Depends on expiring math/py-dgl

EXPIRATION_DATE=2026-09-15



BUILD_DEPENDS=	${PYTHON_PKGNAMEPREFIX}flit-core>0:devel/py-flit-core@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR}

RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}aiohttp>0:www/py-aiohttp@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}dgl>0:math/py-dgl@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}fsspec>0:filesystems/py-fsspec@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}Jinja2>0:devel/py-Jinja2@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}numpy>0:math/py-numpy@${PY_FLAVOR}  \

		${PYTHON_PKGNAMEPREFIX}psutil>=5.8.0:sysutils/py-psutil@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}pyparsing>0:devel/py-pyparsing@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}pytorch>0:misc/py-pytorch@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}requests>0:www/py-requests@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}scikit-learn>0:science/py-scikit-learn@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}scipy>0:science/py-scipy@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}tqdm>0:misc/py-tqdm@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}xxhash>0:devel/py-xxhash@${PY_FLAVOR}

TEST_DEPENDS=	${PYTHON_PKGNAMEPREFIX}faiss>0:math/py-faiss@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}huggingface-hub>0:misc/py-huggingface-hub@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}matplotlib>0:math/py-matplotlib@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}networkx>0:math/py-networkx@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}pandas>0:math/py-pandas@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}pytest-cov>0:devel/py-pytest-cov@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}pytorch-lightning>0:misc/py-pytorch-lightning@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}tabulate>0:devel/py-tabulate@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}torchmetrics>0:misc/py-torchmetrics@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}torchvision>0:misc/py-torchvision@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}wandb>0:misc/py-wandb@${PY_FLAVOR} \

		${PYTHON_PKGNAMEPREFIX}yacs>0:devel/py-yacs@${PY_FLAVOR}

#TEST_DEPENDS additionally needs misc/py-torch-sparse, misc/py-pyg_lib, etc. for some skipped tests



USES=		python

USE_PYTHON=	pep517 autoplist pytest # 27 tests fail, see https://github.com/pyg-team/pytorch_geometric/issues/9660



USE_GITHUB=	yes

GH_ACCOUNT=	pyg-team

GH_PROJECT=	pytorch_geometric



NO_ARCH=	yes



# tests as of version 2.6.1: 17 failed, 5452 passed, 986 skipped, 12 warnings in 330.89s (0:05:30)



.include <bsd.port.mk>

misc/py-torch-geometric/distinfo

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TIMESTAMP = 1781661090

SHA256 (pyg-team-pytorch_geometric-2.8.0_GH0.tar.gz) = 369bcbed7e5835feb3eba0f78791d90dd1790ba523face79c78bbd093ef987a3

SIZE (pyg-team-pytorch_geometric-2.8.0_GH0.tar.gz) = 4253177

misc/py-torch-geometric/pkg-descr

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PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and

train Graph Neural Networks (GNNs) for a wide range of applications related

to structured data.



It consists of various methods for deep learning on graphs and other irregular

structures, also known as geometric deep learning, from a variety of published

papers. In addition, it consists of easy-to-use mini-batch loaders for

operating on many small and single giant graphs, multi GPU-support,

torch.compile support, DataPipe support, a large number of common benchmark

datasets (based on simple interfaces to create your own), the GraphGym

experiment manager, and helpful transforms, both for learning on arbitrary

graphs as well as on 3D meshes or point clouds.