nixpkgs/pkgs/development/python-modules/torch/source/default.nix
Connor Baker bf766a2d97 tree-wide: use named CUDA versions and CUDA version utilities
Signed-off-by: Connor Baker <ConnorBaker01@gmail.com>
2025-05-09 21:16:30 +00:00

735 lines
23 KiB
Nix

{
stdenv,
lib,
fetchFromGitHub,
fetchFromGitLab,
git-unroll,
buildPythonPackage,
python,
runCommand,
writeShellScript,
config,
cudaSupport ? config.cudaSupport,
cudaPackages,
autoAddDriverRunpath,
effectiveMagma ?
if cudaSupport then
magma-cuda-static
else if rocmSupport then
magma-hip
else
magma,
magma,
magma-hip,
magma-cuda-static,
# Use the system NCCL as long as we're targeting CUDA on a supported platform.
useSystemNccl ? (cudaSupport && !cudaPackages.nccl.meta.unsupported || rocmSupport),
MPISupport ? false,
mpi,
buildDocs ? false,
# tests.cudaAvailable:
callPackage,
# Native build inputs
cmake,
symlinkJoin,
which,
pybind11,
pkg-config,
removeReferencesTo,
# Build inputs
apple-sdk_13,
numactl,
# dependencies
astunparse,
expecttest,
filelock,
fsspec,
hypothesis,
jinja2,
networkx,
packaging,
psutil,
pyyaml,
requests,
sympy,
types-dataclasses,
typing-extensions,
# ROCm build and `torch.compile` requires `triton`
tritonSupport ? (!stdenv.hostPlatform.isDarwin),
triton,
# TODO: 1. callPackage needs to learn to distinguish between the task
# of "asking for an attribute from the parent scope" and
# the task of "exposing a formal parameter in .override".
# TODO: 2. We should probably abandon attributes such as `torchWithCuda` (etc.)
# as they routinely end up consuming the wrong arguments\
# (dependencies without cuda support).
# Instead we should rely on overlays and nixpkgsFun.
# (@SomeoneSerge)
_tritonEffective ?
if cudaSupport then
triton-cuda
else if rocmSupport then
rocmPackages.triton
else
triton,
triton-cuda,
# Disable MKLDNN on aarch64-darwin, it negatively impacts performance,
# this is also what official pytorch build does
mklDnnSupport ? !(stdenv.hostPlatform.isDarwin && stdenv.hostPlatform.isAarch64),
# virtual pkg that consistently instantiates blas across nixpkgs
# See https://github.com/NixOS/nixpkgs/pull/83888
blas,
# ninja (https://ninja-build.org) must be available to run C++ extensions tests,
ninja,
# dependencies for torch.utils.tensorboard
pillow,
six,
tensorboard,
protobuf,
# ROCm dependencies
rocmSupport ? config.rocmSupport,
rocmPackages,
gpuTargets ? [ ],
vulkanSupport ? false,
vulkan-headers,
vulkan-loader,
shaderc,
}:
let
inherit (lib)
attrsets
lists
strings
trivial
;
inherit (cudaPackages) cudaFlags cudnn nccl;
triton = throw "python3Packages.torch: use _tritonEffective instead of triton to avoid divergence";
setBool = v: if v then "1" else "0";
# https://github.com/pytorch/pytorch/blob/v2.6.0/torch/utils/cpp_extension.py#L2046-L2048
supportedTorchCudaCapabilities =
let
real = [
"3.5"
"3.7"
"5.0"
"5.2"
"5.3"
"6.0"
"6.1"
"6.2"
"7.0"
"7.2"
"7.5"
"8.0"
"8.6"
"8.7"
"8.9"
"9.0"
"9.0a"
"10.0"
];
ptx = lists.map (x: "${x}+PTX") real;
in
real ++ ptx;
# NOTE: The lists.subtractLists function is perhaps a bit unintuitive. It subtracts the elements
# of the first list *from* the second list. That means:
# lists.subtractLists a b = b - a
# For CUDA
supportedCudaCapabilities = lists.intersectLists cudaFlags.cudaCapabilities supportedTorchCudaCapabilities;
unsupportedCudaCapabilities = lists.subtractLists supportedCudaCapabilities cudaFlags.cudaCapabilities;
isCudaJetson = cudaSupport && cudaPackages.cudaFlags.isJetsonBuild;
# Use trivial.warnIf to print a warning if any unsupported GPU targets are specified.
gpuArchWarner =
supported: unsupported:
trivial.throwIf (supported == [ ]) (
"No supported GPU targets specified. Requested GPU targets: "
+ strings.concatStringsSep ", " unsupported
) supported;
# Create the gpuTargetString.
gpuTargetString = strings.concatStringsSep ";" (
if gpuTargets != [ ] then
# If gpuTargets is specified, it always takes priority.
gpuTargets
else if cudaSupport then
gpuArchWarner supportedCudaCapabilities unsupportedCudaCapabilities
else if rocmSupport then
rocmPackages.clr.gpuTargets
else
throw "No GPU targets specified"
);
rocmtoolkit_joined = symlinkJoin {
name = "rocm-merged";
paths = with rocmPackages; [
rocm-core
clr
rccl
miopen
aotriton
rocrand
rocblas
rocsparse
hipsparse
rocthrust
rocprim
hipcub
roctracer
rocfft
rocsolver
hipfft
hiprand
hipsolver
hipblas-common
hipblas
hipblaslt
rocminfo
rocm-comgr
rocm-device-libs
rocm-runtime
clr.icd
hipify
];
# Fix `setuptools` not being found
postBuild = ''
rm -rf $out/nix-support
'';
};
brokenConditions = attrsets.filterAttrs (_: cond: cond) {
"CUDA and ROCm are mutually exclusive" = cudaSupport && rocmSupport;
"CUDA is not targeting Linux" = cudaSupport && !stdenv.hostPlatform.isLinux;
"Unsupported CUDA version" =
cudaSupport
&& !(builtins.elem cudaPackages.cudaMajorVersion [
"11"
"12"
]);
"MPI cudatoolkit does not match cudaPackages.cudatoolkit" =
MPISupport && cudaSupport && (mpi.cudatoolkit != cudaPackages.cudatoolkit);
# This used to be a deep package set comparison between cudaPackages and
# effectiveMagma.cudaPackages, making torch too strict in cudaPackages.
# In particular, this triggered warnings from cuda's `aliases.nix`
"Magma cudaPackages does not match cudaPackages" =
cudaSupport
&& (effectiveMagma.cudaPackages.cudaMajorMinorVersion != cudaPackages.cudaMajorMinorVersion);
};
unroll-src = writeShellScript "unroll-src" ''
echo "{
version,
fetchFromGitLab,
fetchFromGitHub,
runCommand,
}:
assert version == "'"'$1'"'";"
${lib.getExe git-unroll} https://github.com/pytorch/pytorch v$1
echo
echo "# Update using: unroll-src [version]"
'';
stdenv' = if cudaSupport then cudaPackages.backendStdenv else stdenv;
in
buildPythonPackage rec {
pname = "torch";
# Don't forget to update torch-bin to the same version.
version = "2.6.0";
pyproject = true;
stdenv = stdenv';
outputs = [
"out" # output standard python package
"dev" # output libtorch headers
"lib" # output libtorch libraries
"cxxdev" # propagated deps for the cmake consumers of torch
];
cudaPropagateToOutput = "cxxdev";
src = callPackage ./src.nix {
inherit
version
fetchFromGitHub
fetchFromGitLab
runCommand
;
};
patches =
[
./clang19-template-warning.patch
# fix invalid static cast in XNNPACK
# https://github.com/google/XNNPACK/issues/7489
./xnnpack-bfloat16.patch
]
++ lib.optionals cudaSupport [ ./fix-cmake-cuda-toolkit.patch ]
++ lib.optionals stdenv.hostPlatform.isLinux [
# Propagate CUPTI to Kineto by overriding the search path with environment variables.
# https://github.com/pytorch/pytorch/pull/108847
./pytorch-pr-108847.patch
]
++ lib.optionals (lib.getName blas.provider == "mkl") [
# The CMake install tries to add some hardcoded rpaths, incompatible
# with the Nix store, which fails. Simply remove this step to get
# rpaths that point to the Nix store.
./disable-cmake-mkl-rpath.patch
];
postPatch =
''
substituteInPlace cmake/public/cuda.cmake \
--replace-fail \
'message(FATAL_ERROR "Found two conflicting CUDA' \
'message(WARNING "Found two conflicting CUDA' \
--replace-warn \
"set(CUDAToolkit_ROOT" \
"# Upstream: set(CUDAToolkit_ROOT"
substituteInPlace third_party/gloo/cmake/Cuda.cmake \
--replace-warn "find_package(CUDAToolkit 7.0" "find_package(CUDAToolkit"
# annotations (3.7), print_function (3.0), with_statement (2.6) are all supported
sed -i -e "/from __future__ import/d" **.py
substituteInPlace third_party/NNPACK/CMakeLists.txt \
--replace-fail "PYTHONPATH=" 'PYTHONPATH=$ENV{PYTHONPATH}:'
# flag from cmakeFlags doesn't work, not clear why
# setting it at the top of NNPACK's own CMakeLists does
sed -i '2s;^;set(PYTHON_SIX_SOURCE_DIR ${six.src})\n;' third_party/NNPACK/CMakeLists.txt
''
+ lib.optionalString rocmSupport ''
# https://github.com/facebookincubator/gloo/pull/297
substituteInPlace third_party/gloo/cmake/Hipify.cmake \
--replace-fail "\''${HIPIFY_COMMAND}" "python \''${HIPIFY_COMMAND}"
# Replace hard-coded rocm paths
substituteInPlace caffe2/CMakeLists.txt \
--replace-fail "/opt/rocm" "${rocmtoolkit_joined}" \
--replace-fail "hcc/include" "hip/include" \
--replace-fail "rocblas/include" "include/rocblas" \
--replace-fail "hipsparse/include" "include/hipsparse"
# Doesn't pick up the environment variable?
substituteInPlace third_party/kineto/libkineto/CMakeLists.txt \
--replace-fail "\''$ENV{ROCM_SOURCE_DIR}" "${rocmtoolkit_joined}" \
--replace-fail "/opt/rocm" "${rocmtoolkit_joined}"
# Strangely, this is never set in cmake
substituteInPlace cmake/public/LoadHIP.cmake \
--replace "set(ROCM_PATH \$ENV{ROCM_PATH})" \
"set(ROCM_PATH \$ENV{ROCM_PATH})''\nset(ROCM_VERSION ${lib.concatStrings (lib.intersperse "0" (lib.splitVersion rocmPackages.clr.version))})"
''
# Detection of NCCL version doesn't work particularly well when using the static binary.
+ lib.optionalString cudaSupport ''
substituteInPlace cmake/Modules/FindNCCL.cmake \
--replace-fail \
'message(FATAL_ERROR "Found NCCL header version and library version' \
'message(WARNING "Found NCCL header version and library version'
''
# Remove PyTorch's FindCUDAToolkit.cmake and use CMake's default.
# NOTE: Parts of pytorch rely on unmaintained FindCUDA.cmake with custom patches to support e.g.
# newer architectures (sm_90a). We do want to delete vendored patches, but have to keep them
# until https://github.com/pytorch/pytorch/issues/76082 is addressed
+ lib.optionalString cudaSupport ''
rm cmake/Modules/FindCUDAToolkit.cmake
'';
# NOTE(@connorbaker): Though we do not disable Gloo or MPI when building with CUDA support, caution should be taken
# when using the different backends. Gloo's GPU support isn't great, and MPI and CUDA can't be used at the same time
# without extreme care to ensure they don't lock each other out of shared resources.
# For more, see https://github.com/open-mpi/ompi/issues/7733#issuecomment-629806195.
preConfigure =
lib.optionalString cudaSupport ''
export TORCH_CUDA_ARCH_LIST="${gpuTargetString}"
export CUPTI_INCLUDE_DIR=${lib.getDev cudaPackages.cuda_cupti}/include
export CUPTI_LIBRARY_DIR=${lib.getLib cudaPackages.cuda_cupti}/lib
''
+ lib.optionalString (cudaSupport && cudaPackages ? cudnn) ''
export CUDNN_INCLUDE_DIR=${lib.getLib cudnn}/include
export CUDNN_LIB_DIR=${lib.getLib cudnn}/lib
''
+ lib.optionalString rocmSupport ''
export ROCM_PATH=${rocmtoolkit_joined}
export ROCM_SOURCE_DIR=${rocmtoolkit_joined}
export PYTORCH_ROCM_ARCH="${gpuTargetString}"
export CMAKE_CXX_FLAGS="-I${rocmtoolkit_joined}/include -I${rocmtoolkit_joined}/include/rocblas"
python tools/amd_build/build_amd.py
'';
# Use pytorch's custom configurations
dontUseCmakeConfigure = true;
# causes possible redefinition of _FORTIFY_SOURCE
hardeningDisable = [ "fortify3" ];
BUILD_NAMEDTENSOR = setBool true;
BUILD_DOCS = setBool buildDocs;
# We only do an imports check, so do not build tests either.
BUILD_TEST = setBool false;
# ninja hook doesn't automatically turn on ninja
# because pytorch setup.py is responsible for this
CMAKE_GENERATOR = "Ninja";
# Unlike MKL, oneDNN (née MKLDNN) is FOSS, so we enable support for
# it by default. PyTorch currently uses its own vendored version
# of oneDNN through Intel iDeep.
USE_MKLDNN = setBool mklDnnSupport;
USE_MKLDNN_CBLAS = setBool mklDnnSupport;
# Avoid using pybind11 from git submodule
# Also avoids pytorch exporting the headers of pybind11
USE_SYSTEM_PYBIND11 = true;
# Multicore CPU convnet support
USE_NNPACK = 1;
# Explicitly enable MPS for Darwin
USE_MPS = setBool stdenv.hostPlatform.isDarwin;
# building torch.distributed on Darwin is disabled by default
# https://pytorch.org/docs/stable/distributed.html#torch.distributed.is_available
USE_DISTRIBUTED = setBool true;
cmakeFlags =
[
(lib.cmakeFeature "PYTHON_SIX_SOURCE_DIR" "${six.src}")
# (lib.cmakeBool "CMAKE_FIND_DEBUG_MODE" true)
(lib.cmakeFeature "CUDAToolkit_VERSION" cudaPackages.cudaMajorMinorVersion)
]
++ lib.optionals cudaSupport [
# Unbreaks version discovery in enable_language(CUDA) when wrapping nvcc with ccache
# Cf. https://gitlab.kitware.com/cmake/cmake/-/issues/26363
(lib.cmakeFeature "CMAKE_CUDA_COMPILER_TOOLKIT_VERSION" cudaPackages.cudaMajorMinorVersion)
];
preBuild = ''
export MAX_JOBS=$NIX_BUILD_CORES
${python.pythonOnBuildForHost.interpreter} setup.py build --cmake-only
${cmake}/bin/cmake build
'';
preFixup = ''
function join_by { local IFS="$1"; shift; echo "$*"; }
function strip2 {
IFS=':'
read -ra RP <<< $(patchelf --print-rpath $1)
IFS=' '
RP_NEW=$(join_by : ''${RP[@]:2})
patchelf --set-rpath \$ORIGIN:''${RP_NEW} "$1"
}
for f in $(find ''${out} -name 'libcaffe2*.so')
do
strip2 $f
done
'';
# Override the (weirdly) wrong version set by default. See
# https://github.com/NixOS/nixpkgs/pull/52437#issuecomment-449718038
# https://github.com/pytorch/pytorch/blob/v1.0.0/setup.py#L267
PYTORCH_BUILD_VERSION = version;
PYTORCH_BUILD_NUMBER = 0;
# In-tree builds of NCCL are not supported.
# Use NCCL when cudaSupport is enabled and nccl is available.
USE_NCCL = setBool useSystemNccl;
USE_SYSTEM_NCCL = USE_NCCL;
USE_STATIC_NCCL = USE_NCCL;
# Set the correct Python library path, broken since
# https://github.com/pytorch/pytorch/commit/3d617333e
PYTHON_LIB_REL_PATH = "${placeholder "out"}/${python.sitePackages}";
env =
{
# disable warnings as errors as they break the build on every compiler
# bump, among other things.
# Also of interest: pytorch ignores CXXFLAGS uses CFLAGS for both C and C++:
# https://github.com/pytorch/pytorch/blob/v1.11.0/setup.py#L17
NIX_CFLAGS_COMPILE = toString (
[
"-Wno-error"
]
# fix build aarch64-linux build failure with GCC14
++ lib.optionals (stdenv.hostPlatform.isLinux && stdenv.hostPlatform.isAarch64) [
"-Wno-error=incompatible-pointer-types"
]
);
USE_VULKAN = setBool vulkanSupport;
}
// lib.optionalAttrs vulkanSupport {
VULKAN_SDK = shaderc.bin;
}
// lib.optionalAttrs rocmSupport {
AOTRITON_INSTALLED_PREFIX = "${rocmPackages.aotriton}";
};
nativeBuildInputs =
[
cmake
which
ninja
pybind11
pkg-config
removeReferencesTo
]
++ lib.optionals cudaSupport (
with cudaPackages;
[
autoAddDriverRunpath
cuda_nvcc
]
)
++ lib.optionals isCudaJetson [ cudaPackages.autoAddCudaCompatRunpath ]
++ lib.optionals rocmSupport [ rocmtoolkit_joined ];
buildInputs =
[
blas
blas.provider
]
++ lib.optionals cudaSupport (
with cudaPackages;
[
cuda_cccl # <thrust/*>
cuda_cudart # cuda_runtime.h and libraries
cuda_cupti # For kineto
cuda_nvcc # crt/host_config.h; even though we include this in nativeBuildInputs, it's needed here too
cuda_nvml_dev # <nvml.h>
cuda_nvrtc
cuda_nvtx # -llibNVToolsExt
cusparselt
libcublas
libcufft
libcurand
libcusolver
libcusparse
]
++ lists.optionals (cudaPackages ? cudnn) [ cudnn ]
++ lists.optionals useSystemNccl [
# Some platforms do not support NCCL (i.e., Jetson)
nccl # Provides nccl.h AND a static copy of NCCL!
]
++ lists.optionals (cudaOlder "11.8") [
cuda_nvprof # <cuda_profiler_api.h>
]
++ lists.optionals (cudaAtLeast "11.8") [
cuda_profiler_api # <cuda_profiler_api.h>
]
)
++ lib.optionals rocmSupport [ rocmPackages.llvm.openmp ]
++ lib.optionals (cudaSupport || rocmSupport) [ effectiveMagma ]
++ lib.optionals stdenv.hostPlatform.isLinux [ numactl ]
++ lib.optionals stdenv.hostPlatform.isDarwin [
apple-sdk_13
]
++ lib.optionals tritonSupport [ _tritonEffective ]
++ lib.optionals MPISupport [ mpi ]
++ lib.optionals rocmSupport [
rocmtoolkit_joined
rocmPackages.clr # Added separately so setup hook applies
];
pythonRelaxDeps = [
"sympy"
];
dependencies =
[
astunparse
expecttest
filelock
fsspec
hypothesis
jinja2
networkx
ninja
packaging
psutil
pyyaml
requests
sympy
types-dataclasses
typing-extensions
# the following are required for tensorboard support
pillow
six
tensorboard
protobuf
# torch/csrc requires `pybind11` at runtime
pybind11
]
++ lib.optionals tritonSupport [ _tritonEffective ]
++ lib.optionals vulkanSupport [
vulkan-headers
vulkan-loader
];
propagatedCxxBuildInputs =
[ ] ++ lib.optionals MPISupport [ mpi ] ++ lib.optionals rocmSupport [ rocmtoolkit_joined ];
# Tests take a long time and may be flaky, so just sanity-check imports
doCheck = false;
pythonImportsCheck = [ "torch" ];
nativeCheckInputs = [
hypothesis
ninja
psutil
];
checkPhase =
with lib.versions;
with lib.strings;
concatStringsSep " " [
"runHook preCheck"
"${python.interpreter} test/run_test.py"
"--exclude"
(concatStringsSep " " [
"utils" # utils requires git, which is not allowed in the check phase
# "dataloader" # psutils correctly finds and triggers multiprocessing, but is too sandboxed to run -- resulting in numerous errors
# ^^^^^^^^^^^^ NOTE: while test_dataloader does return errors, these are acceptable errors and do not interfere with the build
# tensorboard has acceptable failures for pytorch 1.3.x due to dependencies on tensorboard-plugins
(optionalString (majorMinor version == "1.3") "tensorboard")
])
"runHook postCheck"
];
pythonRemoveDeps = [
# In our dist-info the name is just "triton"
"pytorch-triton-rocm"
];
postInstall =
''
find "$out/${python.sitePackages}/torch/include" "$out/${python.sitePackages}/torch/lib" -type f -exec remove-references-to -t ${stdenv.cc} '{}' +
mkdir $dev
# CppExtension requires that include files are packaged with the main
# python library output; which is why they are copied here.
cp -r $out/${python.sitePackages}/torch/include $dev/include
# Cmake files under /share are different and can be safely moved. This
# avoids unnecessary closure blow-up due to apple sdk references when
# USE_DISTRIBUTED is enabled.
mv $out/${python.sitePackages}/torch/share $dev/share
# Fix up library paths for split outputs
substituteInPlace \
$dev/share/cmake/Torch/TorchConfig.cmake \
--replace-fail \''${TORCH_INSTALL_PREFIX}/lib "$lib/lib"
substituteInPlace \
$dev/share/cmake/Caffe2/Caffe2Targets-release.cmake \
--replace-fail \''${_IMPORT_PREFIX}/lib "$lib/lib"
mkdir $lib
mv $out/${python.sitePackages}/torch/lib $lib/lib
ln -s $lib/lib $out/${python.sitePackages}/torch/lib
''
+ lib.optionalString rocmSupport ''
substituteInPlace $dev/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake \
--replace-fail "\''${_IMPORT_PREFIX}/lib64" "$lib/lib"
substituteInPlace $dev/share/cmake/ATen/ATenConfig.cmake \
--replace-fail "/build/source/torch/include" "$dev/include"
'';
postFixup =
''
mkdir -p "$cxxdev/nix-support"
printWords "''${propagatedCxxBuildInputs[@]}" >> "$cxxdev/nix-support/propagated-build-inputs"
''
+ lib.optionalString stdenv.hostPlatform.isDarwin ''
for f in $(ls $lib/lib/*.dylib); do
install_name_tool -id $lib/lib/$(basename $f) $f || true
done
install_name_tool -change @rpath/libshm.dylib $lib/lib/libshm.dylib $lib/lib/libtorch_python.dylib
install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libtorch_python.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libtorch_python.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libtorch.dylib
install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libshm.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libshm.dylib
'';
# See https://github.com/NixOS/nixpkgs/issues/296179
#
# This is a quick hack to add `libnvrtc` to the runpath so that torch can find
# it when it is needed at runtime.
extraRunpaths = lib.optionals cudaSupport [ "${lib.getLib cudaPackages.cuda_nvrtc}/lib" ];
postPhases = lib.optionals stdenv.hostPlatform.isLinux [ "postPatchelfPhase" ];
postPatchelfPhase = ''
while IFS= read -r -d $'\0' elf ; do
for extra in $extraRunpaths ; do
echo patchelf "$elf" --add-rpath "$extra" >&2
patchelf "$elf" --add-rpath "$extra"
done
done < <(
find "''${!outputLib}" "$out" -type f -iname '*.so' -print0
)
'';
# Builds in 2+h with 2 cores, and ~15m with a big-parallel builder.
requiredSystemFeatures = [ "big-parallel" ];
passthru = {
inherit
cudaSupport
cudaPackages
rocmSupport
rocmPackages
unroll-src
;
cudaCapabilities = if cudaSupport then supportedCudaCapabilities else [ ];
# At least for 1.10.2 `torch.fft` is unavailable unless BLAS provider is MKL. This attribute allows for easy detection of its availability.
blasProvider = blas.provider;
# To help debug when a package is broken due to CUDA support
inherit brokenConditions;
tests = callPackage ../tests { };
};
meta = {
changelog = "https://github.com/pytorch/pytorch/releases/tag/v${version}";
# keep PyTorch in the description so the package can be found under that name on search.nixos.org
description = "PyTorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration";
homepage = "https://pytorch.org/";
license = lib.licenses.bsd3;
maintainers = with lib.maintainers; [
teh
thoughtpolice
tscholak
]; # tscholak esp. for darwin-related builds
platforms =
lib.platforms.linux
++ lib.optionals (!cudaSupport && !rocmSupport) lib.platforms.darwin;
broken = builtins.any trivial.id (builtins.attrValues brokenConditions);
};
}