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nvidia-apex: move to my overlay

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Anton Bolshakov 2024-07-18 21:55:35 +08:00
parent 8d6bad12f3
commit cecfdf93b7
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1
dev-python/cxxfilt/Manifest
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DIST cxxfilt-0.3.0.tar.gz 4806 BLAKE2B 369f10c547343dc9e4bf87edd4513531109b1792d5196da57fd0a5db9f0830bf5ada1f20dd7799a3a114400a3f03cbfc795c021d2ae763557e848abca4f7094e SHA512 87cf42c8ae7a2eeb74712b2d952d011ca1e2ac90e3d9c7d254eeef1dd88ceba1f25aff377852d384f73c38cd0183898f3ba9ccde0d9f09d03025c0262290704f

24
dev-python/cxxfilt/cxxfilt-0.3.0.ebuild
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# Copyright 1999-2022 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
PYTHON_COMPAT=( python3_{10..12} )
inherit distutils-r1
DESCRIPTION="Python interface to c++filt / abi::__cxa_demangle"
HOMEPAGE="https://github.com/afq984/python-cxxfilt"
SRC_URI="mirror://pypi/${P:0:1}/${PN}/${P}.tar.gz"
LICENSE="BSD-2"
SLOT="0"
KEYWORDS="amd64 ~arm64 x86"
IUSE=""
RESTRICT="test"
RDEPEND=""
DEPEND="${RDEPEND}"
REQUIRED_USE="${PYTHON_REQUIRED_USE}"
#distutils_enable_tests pytest

3
dev-python/nvidia-apex/Manifest
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DIST nvidia-apex-22.12-gh.tar.gz 790427 BLAKE2B 2b53e1c2ded28d6f3de8812bd6e8ed1db35d10b3293ef53ea7df63eebe5474f2b8e8af6c5700e9f19581715cb73de893b2736944ef621f2eff37959650641784 SHA512 418c76f655d0cb1cfebc8b007b914a2b8e3d9d54efd6496911702577913f7887bc744228e3a465c13c77bcccbf7971f9f9a5a2bd217e395abb92b1fdfbc18ff2
DIST nvidia-apex-23.05-gh.tar.gz 818299 BLAKE2B 8be27cf6a6776e6ff4ca6c3871194b2018eb049ef64d3266859531d2a29edfe3c8363b02aa21663cfb09999b8be71049e969407406de64aa841ca8ee3e6539d7 SHA512 0896514e14809bb8b35d13eddb49eb82179ce008e613c968574364d648f50c2b63ce10af10ae77f6cf3a9833450f3732bf22b4ce17557cbcb897e88251ab4d06
DIST nvidia-apex-23.08-gh.tar.gz 848374 BLAKE2B 1a2a0c55735f9afa7eeaec248071bfb343f52c3e162f58db2d5dd500f00fb60ce1e01589db52979a625feb4dcc1be9d1c68faae2f1ba827c2d7f7562a656cf7a SHA512 a6179e642224a9aef128d168becff6ff811c0e01e92e06b937bde69ddd5276b2a3873a021b108c80bdf993e40e74241509d1ef6a585eba745e0fc71e8eb44274

11
dev-python/nvidia-apex/files/disable_cuda.patch
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--- apex-22.08-dev/apex/normalization/fused_layer_norm.py.orig 2022-08-02 08:25:04.000000000 +0800
+++ apex-22.08-dev/apex/normalization/fused_layer_norm.py 2022-11-21 13:12:25.579343523 +0800
@@ -265,7 +265,7 @@
super().__init__()
global fused_layer_norm_cuda
- fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
+ #fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)

14
dev-python/nvidia-apex/metadata.xml
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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE pkgmetadata SYSTEM "https://www.gentoo.org/dtd/metadata.dtd">
<pkgmetadata>
<maintainer type="person">
<email>blshkv@pentoo.ch</email>
<name>Anton Bolshakov</name>
</maintainer>
<use>
<flag name="cuda">Add support for CUDA processing</flag>
</use>
<upstream>
<remote-id type="github">NVIDIA/apex</remote-id>
</upstream>
</pkgmetadata>

58
dev-python/nvidia-apex/nvidia-apex-22.12-r1.ebuild
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# Copyright 1999-2023 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
DISTUTILS_USE_PEP517=setuptools
PYTHON_COMPAT=( python3_{10..12} )
#FIXME: sci-libs/pytorch is single package:
#DISTUTILS_SINGLE_IMPL=1
#inherit python-single-r1
inherit distutils-r1
#git branch
HASH_COMMIT="${PV}-dev"
DESCRIPTION="Tools for easy mixed precision and distributed training in Pytorch"
HOMEPAGE="https://github.com/NVIDIA/apex"
SRC_URI="https://github.com/NVIDIA/apex/archive/${HASH_COMMIT}.tar.gz -> ${P}-gh.tar.gz"
LICENSE="BSD"
SLOT="0"
KEYWORDS="~amd64"
IUSE="cuda"
#FIXME: add --pyprof
#https://github.com/NVIDIA/PyProf
RDEPEND=">=dev-python/cxxfilt-0.2.0[${PYTHON_USEDEP}]
>=dev-python/tqdm-4.28.1[${PYTHON_USEDEP}]
>=dev-python/numpy-1.15.3[${PYTHON_USEDEP}]
>=sci-libs/pytorch-1.12.0
>=dev-python/pyyaml-5.1[${PYTHON_USEDEP}]
>=dev-python/packaging-14.0[${PYTHON_USEDEP}]"
DEPEND="${RDEPEND}"
REQUIRED_USE="${PYTHON_REQUIRED_USE}"
RESTRICT="test"
S="${WORKDIR}/apex-${HASH_COMMIT}"
#If you wish to cross-compile for a single specific architecture,
#export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.
python_configure_all() {
if use cuda; then
# export MAX_JOBS=1
#export TORCH_CUDA_ARCH_LIST="compute capability"
export TORCH_CUDA_ARCH_LIST="7.5"
DISTUTILS_ARGS=( --cpp_ext --cuda_ext )
fi
}
python_compile() {
distutils-r1_python_compile -j1
}

58
dev-python/nvidia-apex/nvidia-apex-23.05.ebuild
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@ -1,58 +0,0 @@
# Copyright 1999-2023 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
DISTUTILS_USE_PEP517=setuptools
PYTHON_COMPAT=( python3_{10..12} )
#FIXME: sci-libs/pytorch is single package:
#DISTUTILS_SINGLE_IMPL=1
#inherit python-single-r1
inherit distutils-r1
#git branch
HASH_COMMIT="${PV}-devel"
DESCRIPTION="Tools for easy mixed precision and distributed training in Pytorch"
HOMEPAGE="https://github.com/NVIDIA/apex"
SRC_URI="https://github.com/NVIDIA/apex/archive/${HASH_COMMIT}.tar.gz -> ${P}-gh.tar.gz"
LICENSE="BSD"
SLOT="0"
KEYWORDS="~amd64"
IUSE="cuda"
#FIXME: add --pyprof
#https://github.com/NVIDIA/PyProf
RDEPEND=">=dev-python/cxxfilt-0.2.0[${PYTHON_USEDEP}]
>=dev-python/tqdm-4.28.1[${PYTHON_USEDEP}]
>=dev-python/numpy-1.15.3[${PYTHON_USEDEP}]
>=sci-libs/pytorch-1.12.0
>=dev-python/pyyaml-5.1[${PYTHON_USEDEP}]
>=dev-python/packaging-14.0[${PYTHON_USEDEP}]"
DEPEND="${RDEPEND}"
REQUIRED_USE="${PYTHON_REQUIRED_USE}"
RESTRICT="test"
S="${WORKDIR}/apex-${HASH_COMMIT}"
#If you wish to cross-compile for a single specific architecture,
#export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.
python_configure_all() {
if use cuda; then
# export MAX_JOBS=1
#export TORCH_CUDA_ARCH_LIST="compute capability"
export TORCH_CUDA_ARCH_LIST="7.5"
DISTUTILS_ARGS=( --cpp_ext --cuda_ext )
fi
}
python_compile() {
distutils-r1_python_compile -j1
}

55
dev-python/nvidia-apex/nvidia-apex-23.08.ebuild
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@ -1,55 +0,0 @@
# Copyright 1999-2023 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
DISTUTILS_USE_PEP517=setuptools
PYTHON_COMPAT=( python3_{10..12} )
#FIXME: sci-libs/pytorch is single package:
#DISTUTILS_SINGLE_IMPL=1
#inherit python-single-r1
inherit distutils-r1
DESCRIPTION="Tools for easy mixed precision and distributed training in Pytorch"
HOMEPAGE="https://github.com/NVIDIA/apex"
SRC_URI="https://github.com/NVIDIA/apex/archive/refs/tags/${PV}.tar.gz -> ${P}-gh.tar.gz"
LICENSE="BSD"
SLOT="0"
KEYWORDS="~amd64"
IUSE="cuda"
#FIXME: add --pyprof
#https://github.com/NVIDIA/PyProf
RDEPEND=">=dev-python/cxxfilt-0.2.0[${PYTHON_USEDEP}]
>=dev-python/tqdm-4.28.1[${PYTHON_USEDEP}]
>=dev-python/numpy-1.15.3[${PYTHON_USEDEP}]
>=sci-libs/pytorch-1.12.0
>=dev-python/pyyaml-5.1[${PYTHON_USEDEP}]
>=dev-python/packaging-14.0[${PYTHON_USEDEP}]"
DEPEND="${RDEPEND}"
REQUIRED_USE="${PYTHON_REQUIRED_USE}"
RESTRICT="test"
S="${WORKDIR}/apex-${PV}"
#If you wish to cross-compile for a single specific architecture,
#export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.
python_configure_all() {
if use cuda; then
# export MAX_JOBS=1
#export TORCH_CUDA_ARCH_LIST="compute capability"
export TORCH_CUDA_ARCH_LIST="7.5"
DISTUTILS_ARGS=( --cpp_ext --cuda_ext )
fi
}
python_compile() {
distutils-r1_python_compile -j1
}