5aa4b19946
Refs: e6754980264fe927320d5ff2dbd24ca4fac9a160 1e9cc5b9844ef603fe160e9f671178f96200774f 793a2fe1e8bb886ca2096c5904e1193dc3268b6d c19cf65261639f749012454932a532aa7c681e4b f6544d618f30fae0bc4798c4387a8c7c9c047a7c
112 lines
3.5 KiB
Nix
112 lines
3.5 KiB
Nix
{ stdenv, fetchurl, buildPythonPackage, pythonOlder,
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cudaSupport ? false, cudatoolkit ? null, cudnn ? null,
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fetchFromGitHub, lib, numpy, pyyaml, cffi, typing, cmake, hypothesis, numactl,
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linkFarm, symlinkJoin,
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utillinux, which, isPy3k }:
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assert cudnn == null || cudatoolkit != null;
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assert !cudaSupport || cudatoolkit != null;
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let
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cudatoolkit_joined = symlinkJoin {
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name = "${cudatoolkit.name}-unsplit";
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paths = [ cudatoolkit.out cudatoolkit.lib ];
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};
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# Normally libcuda.so.1 is provided at runtime by nvidia-x11 via
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# LD_LIBRARY_PATH=/run/opengl-driver/lib. We only use the stub
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# libcuda.so from cudatoolkit for running tests, so that we don’t have
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# to recompile pytorch on every update to nvidia-x11 or the kernel.
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cudaStub = linkFarm "cuda-stub" [{
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name = "libcuda.so.1";
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path = "${cudatoolkit}/lib/stubs/libcuda.so";
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}];
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cudaStubEnv = lib.optionalString cudaSupport
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"LD_LIBRARY_PATH=${cudaStub}\${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH} ";
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in buildPythonPackage rec {
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version = "1.0.0";
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pname = "pytorch";
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disabled = !isPy3k;
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src = fetchFromGitHub {
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owner = "pytorch";
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repo = "pytorch";
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rev = "v${version}";
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fetchSubmodules = true;
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sha256 = "076cpbig4sywn9vv674c0xdg832sdrd5pk1d0725pjkm436kpvlm";
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};
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patches =
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[ # Skips two tests that are only meant to run on multi GPUs
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(fetchurl {
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url = "https://github.com/pytorch/pytorch/commit/bfa666eb0deebac21b03486e26642fd70d66e478.patch";
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sha256 = "1fgblcj02gjc0y62svwc5gnml879q3x2z7m69c9gax79dpr37s9i";
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})
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];
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dontUseCmakeConfigure = true;
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preConfigure = lib.optionalString cudaSupport ''
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export CC=${cudatoolkit.cc}/bin/gcc CXX=${cudatoolkit.cc}/bin/g++
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'' + lib.optionalString (cudaSupport && cudnn != null) ''
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export CUDNN_INCLUDE_DIR=${cudnn}/include
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'';
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preFixup = ''
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function join_by { local IFS="$1"; shift; echo "$*"; }
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function strip2 {
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IFS=':'
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read -ra RP <<< $(patchelf --print-rpath $1)
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IFS=' '
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RP_NEW=$(join_by : ''${RP[@]:2})
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patchelf --set-rpath \$ORIGIN:''${RP_NEW} "$1"
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}
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for f in $(find ''${out} -name 'libcaffe2*.so')
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do
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strip2 $f
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done
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'';
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# Override the (weirdly) wrong version set by default. See
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# https://github.com/NixOS/nixpkgs/pull/52437#issuecomment-449718038
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# https://github.com/pytorch/pytorch/blob/v1.0.0/setup.py#L267
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PYTORCH_BUILD_VERSION = version;
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PYTORCH_BUILD_NUMBER = 0;
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# Suppress a weird warning in mkl-dnn, part of ideep in pytorch
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# (upstream seems to have fixed this in the wrong place?)
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# https://github.com/intel/mkl-dnn/commit/8134d346cdb7fe1695a2aa55771071d455fae0bc
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NIX_CFLAGS_COMPILE = lib.optionals (numpy.blasImplementation == "mkl") [ "-Wno-error=array-bounds" ];
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nativeBuildInputs = [
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cmake
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utillinux
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which
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] ++ lib.optionals cudaSupport [ cudatoolkit_joined ];
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buildInputs = [
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numpy.blas
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] ++ lib.optionals cudaSupport [ cudnn ]
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++ lib.optionals stdenv.isLinux [ numactl ];
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propagatedBuildInputs = [
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cffi
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numpy
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pyyaml
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] ++ lib.optional (pythonOlder "3.5") typing;
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checkInputs = [ hypothesis ];
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checkPhase = ''
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${cudaStubEnv}python test/run_test.py --exclude dataloader sparse torch utils thd_distributed distributed cpp_extensions
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'';
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meta = {
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description = "Open source, prototype-to-production deep learning platform";
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homepage = https://pytorch.org/;
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license = lib.licenses.bsd3;
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platforms = lib.platforms.linux;
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maintainers = with lib.maintainers; [ teh thoughtpolice ];
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};
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}
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