{ buildPythonPackage, pythonOlder, cudaSupport ? false, cudatoolkit ? null, cudnn ? null, fetchFromGitHub, lib, numpy, pyyaml, cffi, typing, cmake, linkFarm, symlinkJoin, utillinux, which }: assert cudnn == null || cudatoolkit != null; assert !cudaSupport || cudatoolkit != null; let cudatoolkit_joined = symlinkJoin { name = "${cudatoolkit.name}-unsplit"; paths = [ cudatoolkit.out cudatoolkit.lib ]; }; # Normally libcuda.so.1 is provided at runtime by nvidia-x11 via # LD_LIBRARY_PATH=/run/opengl-driver/lib. We only use the stub # libcuda.so from cudatoolkit for running tests, so that we don’t have # to recompile pytorch on every update to nvidia-x11 or the kernel. cudaStub = linkFarm "cuda-stub" [{ name = "libcuda.so.1"; path = "${cudatoolkit}/lib/stubs/libcuda.so"; }]; cudaStubEnv = lib.optionalString cudaSupport "LD_LIBRARY_PATH=${cudaStub}\${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH} "; in buildPythonPackage rec { version = "0.4.0"; pname = "pytorch"; src = fetchFromGitHub { owner = "pytorch"; repo = "pytorch"; rev = "v${version}"; fetchSubmodules = true; sha256 = "12d5vqqaprk0igmih7fwa65ldmaawgijxl58h6dnw660wysc132j"; }; preConfigure = lib.optionalString cudaSupport '' export CC=${cudatoolkit.cc}/bin/gcc CXX=${cudatoolkit.cc}/bin/g++ '' + lib.optionalString (cudaSupport && cudnn != null) '' export CUDNN_INCLUDE_DIR=${cudnn}/include ''; buildInputs = [ cmake numpy.blas utillinux which ] ++ lib.optionals cudaSupport [cudatoolkit_joined cudnn]; propagatedBuildInputs = [ cffi numpy pyyaml ] ++ lib.optional (pythonOlder "3.5") typing; checkPhase = '' ${cudaStubEnv}python test/run_test.py --exclude distributed ''; meta = { description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration."; homepage = https://pytorch.org/; license = lib.licenses.bsd3; platforms = lib.platforms.linux; maintainers = with lib.maintainers; [ teh ]; }; }