117 lines
3.1 KiB
Nix
117 lines
3.1 KiB
Nix
{ stdenv
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, lib
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, fetchFromGitHub
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, fetchpatch
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, fetchurl
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, pkg-config
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, cmake
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, python3
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, libpng
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, zlib
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, eigen
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, protobuf
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, howard-hinnant-date
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, nlohmann_json
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, boost
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, oneDNN
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, gtest
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}:
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let
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# prefetch abseil
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# Note: keep URL in sync with `cmake/external/abseil-cpp.cmake`
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abseil = fetchurl {
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url = "https://github.com/abseil/abseil-cpp/archive/refs/tags/20211102.0.zip";
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sha256 = "sha256-pFZ/8C+spnG5XjHTFbqxi0K2xvGmDpHG6oTlohQhEsI=";
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};
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in
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stdenv.mkDerivation rec {
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pname = "onnxruntime";
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version = "1.12.1";
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src = fetchFromGitHub {
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owner = "microsoft";
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repo = "onnxruntime";
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rev = "v${version}";
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sha256 = "sha256-wwllEemiHTp9aJcCd1gsTS4WUVMp5wW+4i/+6DzmAeM=";
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fetchSubmodules = true;
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};
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patches = [
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# Use dnnl from nixpkgs instead of submodules
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(fetchpatch {
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name = "system-dnnl.patch";
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url = "https://aur.archlinux.org/cgit/aur.git/plain/system-dnnl.diff?h=python-onnxruntime&id=0185531906bda3a9aba93bbb0f3dcfeb0ae671ad";
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sha256 = "sha256-58RBrQnAWNtc/1pmFs+PkZ6qCsL1LfMY3P0exMKzotA=";
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})
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];
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nativeBuildInputs = [
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cmake
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pkg-config
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python3
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gtest
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];
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buildInputs = [
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libpng
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zlib
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protobuf
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howard-hinnant-date
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nlohmann_json
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boost
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oneDNN
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];
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# TODO: build server, and move .so's to lib output
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outputs = [ "out" "dev" ];
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enableParallelBuilding = true;
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cmakeDir = "../cmake";
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cmakeFlags = [
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"-Donnxruntime_PREFER_SYSTEM_LIB=ON"
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"-Donnxruntime_BUILD_SHARED_LIB=ON"
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"-Donnxruntime_ENABLE_LTO=ON"
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"-Donnxruntime_BUILD_UNIT_TESTS=ON"
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"-Donnxruntime_USE_PREINSTALLED_EIGEN=ON"
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"-Donnxruntime_USE_MPI=ON"
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"-Deigen_SOURCE_PATH=${eigen.src}"
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"-Donnxruntime_USE_DNNL=YES"
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];
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doCheck = true;
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postPatch = ''
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substituteInPlace cmake/external/abseil-cpp.cmake \
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--replace "${abseil.url}" "${abseil}"
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'';
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postInstall = ''
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# perform parts of `tools/ci_build/github/linux/copy_strip_binary.sh`
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install -m644 -Dt $out/include \
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../include/onnxruntime/core/framework/provider_options.h \
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../include/onnxruntime/core/providers/cpu/cpu_provider_factory.h \
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../include/onnxruntime/core/session/onnxruntime_*.h
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'';
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meta = with lib; {
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description = "Cross-platform, high performance scoring engine for ML models";
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longDescription = ''
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ONNX Runtime is a performance-focused complete scoring engine
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for Open Neural Network Exchange (ONNX) models, with an open
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extensible architecture to continually address the latest developments
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in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX
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standard with complete implementation of all ONNX operators, and
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supports all ONNX releases (1.2+) with both future and backwards
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compatibility.
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'';
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homepage = "https://github.com/microsoft/onnxruntime";
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changelog = "https://github.com/microsoft/onnxruntime/releases/tag/v${version}";
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# https://github.com/microsoft/onnxruntime/blob/master/BUILD.md#architectures
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platforms = platforms.unix;
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license = licenses.mit;
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maintainers = with maintainers; [ jonringer puffnfresh ck3d ];
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};
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}
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