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{
lib,
stdenv,
python,
buildPythonPackage,
fetchurl,
pythonOlder,
pythonAtLeast,
# buildInputs
cudaPackages,
# nativeBuildInputs
addDriverRunpath,
autoPatchelfHook,
# dependencies
pillow,
torch-bin,
}:
let
pyVerNoDot = builtins.replaceStrings [ "." ] [ "" ] python.pythonVersion;
srcs = import ./binary-hashes.nix version;
unsupported = throw "Unsupported system";
version = "0.19.1";
in
buildPythonPackage {
inherit version;
pname = "torchvision";
format = "wheel";
src = fetchurl srcs."${stdenv.system}-${pyVerNoDot}" or unsupported;
disabled = (pythonOlder "3.8") || (pythonAtLeast "3.13");
# Note that we don't rely on config.cudaSupport here, because the Linux wheels all come built with CUDA support.
buildInputs =
with cudaPackages;
lib.optionals stdenv.hostPlatform.isLinux [
# $out/${sitePackages}/torchvision/_C.so wants libcudart.so.11.0 but torchvision.libs only ships
# libcudart.$hash.so.11.0
cuda_cudart
];
nativeBuildInputs = lib.optionals stdenv.hostPlatform.isLinux [
addDriverRunpath
autoPatchelfHook
];
dependencies = [
pillow
torch-bin
];
# The wheel-binary is not stripped to avoid the error of `ImportError: libtorch_cuda_cpp.so: ELF load command address/offset not properly aligned.`.
dontStrip = true;
pythonImportsCheck = [ "torchvision" ];
preInstall = lib.optionalString stdenv.hostPlatform.isLinux ''
addAutoPatchelfSearchPath "${torch-bin}/${python.sitePackages}/torch"
'';
meta = {
description = "PyTorch vision library";
homepage = "https://pytorch.org/";
changelog = "https://github.com/pytorch/vision/releases/tag/v${version}";
# Includes CUDA and Intel MKL, but redistributions of the binary are not limited.
# https://docs.nvidia.com/cuda/eula/index.html
# https://www.intel.com/content/www/us/en/developer/articles/license/onemkl-license-faq.html
license = lib.licenses.bsd3;
sourceProvenance = with lib.sourceTypes; [ binaryNativeCode ];
platforms = [
"aarch64-darwin"
"x86_64-linux"
"aarch64-linux"
];
maintainers = with lib.maintainers; [ junjihashimoto ];
};
}
|
