85 lines
2.4 KiB
Nix
85 lines
2.4 KiB
Nix
{ buildPythonPackage, pythonOlder,
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cudaSupport ? false, cudatoolkit ? null, cudnn ? null,
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fetchFromGitHub, lib, numpy, pyyaml, cffi, typing, cmake,
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linkFarm, symlinkJoin,
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utillinux, which }:
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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 = "0.4.1";
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pname = "pytorch";
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src = fetchFromGitHub {
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owner = "pytorch";
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repo = "pytorch";
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rev = "v${version}";
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fetchSubmodules = true;
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sha256 = "1cr8h47jxgfar5bamyvlayvqymnb2qvp7rr0ka2d2d4rdldf9lrp";
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};
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preConfigure = lib.optionalString cudaSupport ''
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export CC=${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 $f
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done
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'';
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buildInputs = [
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cmake
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numpy.blas
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utillinux
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which
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] ++ lib.optionals cudaSupport [cudatoolkit_joined cudnn];
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propagatedBuildInputs = [
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cffi
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numpy
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pyyaml
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] ++ lib.optional (pythonOlder "3.5") typing;
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checkPhase = ''
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${cudaStubEnv}python test/run_test.py --exclude dataloader sparse torch utils distributed
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'';
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meta = {
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description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration.";
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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 ];
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};
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}
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