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What happens if I push all of nixpkgs
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Nix
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123{ callPackage, stdenv, fetchzip, lib, libcxx, llvmPackages, config,
addDriverRunpath, patchelf, fixDarwinDylibNames,
cudaSupport ? config.cudaSupport,}:
let # The binary libtorch distribution statically links the CUDA # toolkit. This means that we do not need to provide CUDA to # this derivation. However, we should ensure on version bumps # that the CUDA toolkit for `passthru.tests` is still # up-to-date. version = "2.5.0"; device = if cudaSupport then "cuda" else "cpu"; srcs = import ./binary-hashes.nix version; unavailable = throw "libtorch is not available for this platform"; libcxx-for-libtorch = if stdenv.hostPlatform.isDarwin then libcxx else (lib.getLib stdenv.cc.cc);instdenv.mkDerivation { inherit version; pname = "libtorch";
src = fetchzip srcs."${stdenv.hostPlatform.system}-${device}" or unavailable;
nativeBuildInputs = if stdenv.hostPlatform.isDarwin then [ fixDarwinDylibNames ] else [ patchelf ] ++ lib.optionals cudaSupport [ addDriverRunpath ];
dontBuild = true; dontConfigure = true; dontStrip = true;
installPhase = '' # Copy headers and CMake files. mkdir -p $dev cp -r include $dev cp -r share $dev
install -Dm755 -t $out/lib lib/*${stdenv.hostPlatform.extensions.sharedLibrary}*
# We do not care about Java support... rm -f $out/lib/lib*jni* 2> /dev/null || true
# Fix up library paths for split outputs substituteInPlace $dev/share/cmake/Torch/TorchConfig.cmake \ --replace \''${TORCH_INSTALL_PREFIX}/lib "$out/lib" \
substituteInPlace \ $dev/share/cmake/Caffe2/Caffe2Targets-release.cmake \ --replace \''${_IMPORT_PREFIX}/lib "$out/lib" \ '';
postFixup = let rpath = lib.makeLibraryPath [ stdenv.cc.cc ]; in lib.optionalString stdenv.hostPlatform.isLinux '' find $out/lib -type f \( -name '*.so' -or -name '*.so.*' \) | while read lib; do echo "setting rpath for $lib..." patchelf --set-rpath "${rpath}:$out/lib" "$lib" ${lib.optionalString cudaSupport '' addDriverRunpath "$lib" ''} done '' + lib.optionalString stdenv.hostPlatform.isDarwin '' for f in $out/lib/*.dylib; do otool -L $f done for f in $out/lib/*.dylib; do if otool -L $f | grep "@rpath/libomp.dylib" >& /dev/null; then install_name_tool -change "@rpath/libomp.dylib" ${llvmPackages.openmp}/lib/libomp.dylib $f fi install_name_tool -id $out/lib/$(basename $f) $f || true for rpath in $(otool -L $f | grep rpath | awk '{print $1}');do install_name_tool -change $rpath $out/lib/$(basename $rpath) $f done if otool -L $f | grep /usr/lib/libc++ >& /dev/null; then install_name_tool -change /usr/lib/libc++.1.dylib ${libcxx-for-libtorch.outPath}/lib/libc++.1.0.dylib $f fi done for f in $out/lib/*.dylib; do otool -L $f done '';
outputs = [ "out" "dev" ];
passthru.tests.cmake = callPackage ./test { inherit cudaSupport; };
meta = with lib; { description = "C++ API of the PyTorch machine learning framework"; homepage = "https://pytorch.org/"; sourceProvenance = with sourceTypes; [ binaryNativeCode ]; # 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 = licenses.bsd3; maintainers = with maintainers; [ junjihashimoto ]; platforms = [ "aarch64-darwin" "x86_64-linux" ]; };}