anchor.diff:    PYTORCH_BUILD_VERSION = version;
     PYTORCH_BUILD_NUMBER = 0;
   
  +  BUILD_NAMEDTENSOR = buildNamedTensor;  # experimental feature
  +  USE_SYSTEM_NCCL=true;                  # don't build pytorch's third_party NCCL
  +
     # Suppress a weird warning in mkl-dnn, part of ideep in pytorch
     # (upstream seems to have fixed this in the wrong place?)
     # https://github.com/intel/mkl-dnn/commit/8134d346cdb7fe1695a2aa55771071d455fae0bc
  -  NIX_CFLAGS_COMPILE = lib.optionals (numpy.blasImplementation == "mkl") [ "-Wno-error=array-bounds" ];
  +  # https://github.com/pytorch/pytorch/issues/22346
  +  #
  +  # Also of interest: pytorch ignores CXXFLAGS uses CFLAGS for both C and C++:
  +  # https://github.com/pytorch/pytorch/blob/v1.2.0/setup.py#L17
  +  NIX_CFLAGS_COMPILE = lib.optionals (numpy.blas == mkl) [ "-Wno-error=array-bounds" ];
   
     nativeBuildInputs = [
  -     cmake
  -     utillinux
  -     which
  +    cmake
  +    utillinux
  +    which
  +    ninja
     ] ++ lib.optionals cudaSupport [ cudatoolkit_joined ];
   
     buildInputs = [
  -     numpy.blas
  -  ] ++ lib.optionals cudaSupport [ cudnn ]
  +    numpy.blas
  +  ] ++ lib.optionals cudaSupport [ cudnn magma nccl ]
       ++ lib.optionals stdenv.isLinux [ numactl ];
   
     propagatedBuildInputs = [
       cffi
  +    click
       numpy
       pyyaml
  -  ] ++ lib.optional (pythonOlder "3.5") typing;
  -
  -  checkInputs = [ hypothesis ];
  -  checkPhase = ''
  -    ${cudaStubEnv}python test/run_test.py --exclude dataloader sparse torch utils thd_distributed distributed cpp_extensions
  -  '';
  +  ] ++ lib.optionals openMPISupport [ openmpi ]
  +    ++ lib.optional (pythonOlder "3.5") typing
  +    ++ lib.optionals tensorboardSupport [pillow six future tensorflow-tensorboard];
  +
  +  checkInputs = [ hypothesis ninja ];
  +
  +  doCheck = false; # tests take a long time for channel release, so doCheck should be overridden only when developing
  +  checkPhase = "${cudaStubEnv}python test/run_test.py"
  +    + " --exclude utils" # utils requires git, which is not allowed in the check phase
  +
  +    # Other tests which have been disabled in previous nix derivations of pytorch.
  +    # --exclude dataloader sparse torch utils thd_distributed distributed cpp_extensions
  +    ;
  +  postInstall = ''
  +    mkdir $dev
  +    cp -r $out/${python.sitePackages}/torch/lib     $dev/lib
  +    cp -r $out/${python.sitePackages}/torch/include $dev/include
  +  '';
  +
  +  postFixup = stdenv.lib.optionalString stdenv.isDarwin ''
  +    for f in $(ls $dev/lib/*.dylib); do
  +        install_name_tool -id $dev/lib/$(basename $f) $f || true
  +    done
  +
  +    install_name_tool -change @rpath/libshm.dylib $dev/lib/libshm.dylib $dev/lib/libtorch_python.dylib
  +    install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libtorch_python.dylib
  +    install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libtorch_python.dylib
  +
  +    install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libtorch.dylib
  +
  +    install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libcaffe2_observers.dylib
  +    install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libcaffe2_observers.dylib
  +
  +    install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libcaffe2_module_test_dynamic.dylib
  +    install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libcaffe2_module_test_dynamic.dylib
  +
  +    install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libcaffe2_detectron_ops.dylib
  +    install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libcaffe2_detectron_ops.dylib
  +
  +    install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libshm.dylib
  +    install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libshm.dylib
  +  '';
  +
   
     meta = {
       description = "Open source, prototype-to-production deep learning platform";
       homepage    = https://pytorch.org/;
       license     = lib.licenses.bsd3;
  -    platforms   = lib.platforms.linux;
  -    maintainers = with lib.maintainers; [ teh thoughtpolice ];
  +    platforms   = with lib.platforms; linux ++ lib.optionals (!cudaSupport) darwin;
  +    maintainers = with lib.maintainers; [ teh thoughtpolice stites tscholak ]; # tscholak esp. for darwin-related builds
     };
   }
  
positive.code:       RP_NEW=$(join_by : ''${RP[@]:2})
        patchelf --set-rpath \$ORIGIN:''${RP_NEW} "$1"
      }
  
      for f in $(find ''${out} -name 'libcaffe2*.so')
      do
        strip2 $f
  
negative.code: { stdenv, fetchurl, fetchgit, buildPythonPackage, python, pythonOlder,
    cudaSupport ? false, cudatoolkit ? null, cudnn ? null, nccl ? null, magma ? null,
    mklSupport ? false, mkl ? null,
    openMPISupport ? false, openmpi ? null,
    buildNamedTensor ? false,
    buildBinaries ? false,
    cudaArchList ? null,
    fetchFromGitHub, lib, numpy, pyyaml, cffi, click, typing, cmake, hypothesis, numactl,
    linkFarm, symlinkJoin,
  
    # ninja (https://ninja-build.org) must be available to run C++ extensions tests,
    ninja,
  
    # dependencies for torch.utils.tensorboard
    tensorboardSupport ? true, pillow, six, future, tensorflow-tensorboard,
  
    utillinux, which, isPy3k }:
  
  assert !openMPISupport || openmpi != null;
  assert !tensorboardSupport || tensorflow-tensorboard != null;
  
  # assert that everything needed for cuda is present and that the correct cuda versions are used
  assert !cudaSupport || cudatoolkit != null;
  assert cudnn == null || cudatoolkit != null;
  assert !cudaSupport || (let majorIs = lib.versions.major cudatoolkit.version;
                          in majorIs == "9" || majorIs == "10");
  
  let
    hasDependency = dep: pkg: lib.lists.any (inp: inp == dep) pkg.buildInputs;
    matchesCudatoolkit = hasDependency cudatoolkit;
    matchesMkl = hasDependency mkl;
  in
  # confirm that cudatoolkits are sync'd across dependencies
  assert !(openMPISupport && cudaSupport) || matchesCudatoolkit openmpi;
  assert !cudaSupport || matchesCudatoolkit magma;
  
  # confirm that mkl is sync'd across dependencies
  assert !mklSupport || mkl != null;
  assert !(mklSupport && cudaSupport) || matchesMkl magma;
  assert !mklSupport || (numpy.blasImplementation == "mkl" && numpy.blas == mkl);
  
  let
    cudatoolkit_joined = symlinkJoin {
      name = "${cudatoolkit.name}-unsplit";
      # nccl is here purely for semantic grouping it could be moved to nativeBuildInputs
      paths = [ cudatoolkit.out cudatoolkit.lib nccl.dev nccl.out ];
    };
  
    # Give an explicit list of supported architectures for the build, See:
    # - pytorch bug report: https://github.com/pytorch/pytorch/issues/23573
    # - pytorch-1.2.0 build on nixpks: https://github.com/NixOS/nixpkgs/pull/65041
    #
    # This list was selected by omitting the TORCH_CUDA_ARCH_LIST parameter,
    # observing the fallback option (which selected all architectures known
    # from cudatoolkit_10_0, pytorch-1.2, and python-3.6), and doing a binary
    # searching to find offending architectures.
    #
    # NOTE: Because of sandboxing, this derivation can't auto-detect the hardware's
    # cuda architecture, so there is also now a problem around new architectures
    # not being supported until explicitly added to this derivation.
    #
    # FIXME: CMake is throwing the following warning on python-1.2:
    #
    # ```
    # CMake Warning at cmake/public/utils.cmake:172 (message):
    #   In the future we will require one to explicitly pass TORCH_CUDA_ARCH_LIST
    #   to cmake instead of implicitly setting it as an env variable.  This will
    #   become a FATAL_ERROR in future version of pytorch.
    # ```
    # If this is causing problems for your build, this derivation may have to strip
    # away the standard `buildPythonPackage` and use the
    # [*Adjust Build Options*](https://github.com/pytorch/pytorch/tree/v1.2.0#adjust-build-options-optional)
    # instructions. This will also add more flexibility around configurations
    # (allowing FBGEMM to be built in pytorch-1.1), and may future proof this
    # derivation.
    brokenArchs = [ "3.0" ]; # this variable is only used as documentation.
    cuda9ArchList = [
      "3.5"
      "5.0"
      "5.2"
      "6.0"
      "6.1"
      "7.0"
      "7.0+PTX"  # I am getting a "undefined architecture compute_75" on cuda 9
                 # which leads me to believe this is the final cuda-9-compatible architecture.
    ];
    cuda10ArchList = cuda9ArchList ++ [
      "7.5"
      "7.5+PTX"  # < most recent architecture as of cudatoolkit_10_0 and pytorch-1.2.0
    ];
    final_cudaArchList =
      if !cudaSupport || cudaArchList != null
      then cudaArchList
      else
        if lib.versions.major cudatoolkit.version == "9"
        then cuda9ArchList
        else cuda10ArchList; # the assert above removes any ambiguity here.
  
    # Normally libcuda.so.1 is provided at runtime by nvidia-x11 via
    # LD_LIBRARY_PATH=/run/opengl-driver/lib.  We only use the stub
  