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1.1.0 recipe
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‎conda/pytorch-1.1.0/bld.bat

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@echo off
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set TH_BINARY_BUILD=1
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set PYTORCH_BUILD_VERSION=%PKG_VERSION%
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set PYTORCH_BUILD_NUMBER=%PKG_BUILDNUM%
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if "%NO_CUDA%" == "" (
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set build_with_cuda=1
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set desired_cuda=%CUDA_VERSION:~0,-1%.%CUDA_VERSION:~-1,1%
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) else (
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set build_with_cuda=
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set USE_CUDA=0
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)
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if "%build_with_cuda%" == "" goto cuda_flags_end
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set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%desired_cuda%
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set CUDA_BIN_PATH=%CUDA_PATH%\bin
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set TORCH_CUDA_ARCH_LIST=3.5;5.0+PTX
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if "%desired_cuda%" == "8.0" set TORCH_CUDA_ARCH_LIST=%TORCH_CUDA_ARCH_LIST%;6.0;6.1
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if "%desired_cuda%" == "9.0" set TORCH_CUDA_ARCH_LIST=%TORCH_CUDA_ARCH_LIST%;6.0;7.0
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if "%desired_cuda%" == "9.2" set TORCH_CUDA_ARCH_LIST=%TORCH_CUDA_ARCH_LIST%;6.0;6.1;7.0
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if "%desired_cuda%" == "10.0" set TORCH_CUDA_ARCH_LIST=%TORCH_CUDA_ARCH_LIST%;6.0;6.1;7.0;7.5
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set TORCH_NVCC_FLAGS=-Xfatbin -compress-all
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:cuda_flags_end
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set DISTUTILS_USE_SDK=1
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curl https://s3.amazonaws.com/ossci-windows/mkl_2018.2.185.7z -k -O
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7z x -aoa mkl_2018.2.185.7z -omkl
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set CMAKE_INCLUDE_PATH=%SRC_DIR%\mkl\include
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set LIB=%SRC_DIR%\mkl\lib;%LIB%
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IF "%USE_SCCACHE%" == "1" (
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mkdir %SRC_DIR%\tmp_bin
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curl -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output %SRC_DIR%\tmp_bin\sccache.exe
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copy %SRC_DIR%\tmp_bin\sccache.exe %SRC_DIR%\tmp_bin\nvcc.exe
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set "PATH=%SRC_DIR%\tmp_bin;%PATH%"
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)
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IF "%build_with_cuda%" == "" goto cuda_end
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IF "%desired_cuda%" == "8.0" (
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set MAGMA_VERSION=2.4.0
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) ELSE (
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set MAGMA_VERSION=2.5.0
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)
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curl https://s3.amazonaws.com/ossci-windows/magma_%MAGMA_VERSION%_cuda%CUDA_VERSION%_release.7z -k -O
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7z x -aoa magma_%MAGMA_VERSION%_cuda%CUDA_VERSION%_release.7z -omagma_cuda%CUDA_VERSION%_release
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set MAGMA_HOME=%cd%\magma_cuda%CUDA_VERSION%_release
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IF "%USE_SCCACHE%" == "1" (
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set CUDA_NVCC_EXECUTABLE=%SRC_DIR%\tmp_bin\nvcc
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)
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set "PATH=%CUDA_BIN_PATH%;%PATH%"
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if "%CUDA_VERSION%" == "80" (
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:: Only if you use Ninja with CUDA 8
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set "CUDAHOSTCXX=%VS140COMNTOOLS%\..\..\VC\bin\amd64\cl.exe"
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)
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:cuda_end
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set CMAKE_GENERATOR=Ninja
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IF NOT "%USE_SCCACHE%" == "1" goto sccache_end
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sccache --stop-server
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sccache --start-server
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sccache --zero-stats
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set CC=sccache cl
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set CXX=sccache cl
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:sccache_end
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python setup.py install
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if errorlevel 1 exit /b 1
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IF "%USE_SCCACHE%" == "1" (
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taskkill /im sccache.exe /f /t || ver > nul
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taskkill /im nvcc.exe /f /t || ver > nul
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)
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if NOT "%build_with_cuda%" == "" (
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copy "%CUDA_BIN_PATH%\cudnn64_%CUDNN_VERSION%.dll*" %SP_DIR%\torch\lib
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)
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exit /b 0

‎conda/pytorch-1.1.0/build.sh

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#!/usr/bin/env bash
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set -ex
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export CMAKE_LIBRARY_PATH=$PREFIX/lib:$PREFIX/include:$CMAKE_LIBRARY_PATH
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export CMAKE_PREFIX_PATH=$PREFIX
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export TH_BINARY_BUILD=1 # links CPU BLAS libraries thrice in a row (was needed for some MKL static linkage)
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export PYTORCH_BUILD_VERSION=$PKG_VERSION
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export PYTORCH_BUILD_NUMBER=$PKG_BUILDNUM
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# Why do we disable Ninja when ninja is included in the meta.yaml? Well, using
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# ninja in the conda builds leads to a system python2.7 library being called
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# which leads to ascii decode errors when building third_party/onnx. Is the
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# ninja n this conda env being picked up? We still need ninja in the meta.yaml
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# for cpp_tests I believe though. TODO figure out what's going on here and fix
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# it. It would be nice to use ninja in the builds of the conda binaries as well
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export USE_NINJA=OFF
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# MacOS build is simple, and will not be for CUDA
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if [[ "$OSTYPE" == "darwin"* ]]; then
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MACOSX_DEPLOYMENT_TARGET=10.9 \
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CXX=clang++ \
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CC=clang \
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python setup.py install
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exit 0
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fi
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if [[ -z "$NO_CUDA" || "$NO_CUDA" == 0 ]]; then
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build_with_cuda=1
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fi
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if [[ -n "$build_with_cuda" ]]; then
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export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX"
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if [[ $CUDA_VERSION == 8.0* ]]; then
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export TORCH_CUDA_ARCH_LIST="$TORCH_CUDA_ARCH_LIST;6.0;6.1"
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elif [[ $CUDA_VERSION == 9.0* ]]; then
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export TORCH_CUDA_ARCH_LIST="$TORCH_CUDA_ARCH_LIST;6.0;7.0"
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elif [[ $CUDA_VERSION == 9.2* ]]; then
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export TORCH_CUDA_ARCH_LIST="$TORCH_CUDA_ARCH_LIST;6.0;6.1;7.0"
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elif [[ $CUDA_VERSION == 10.0* ]]; then
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export TORCH_CUDA_ARCH_LIST="$TORCH_CUDA_ARCH_LIST;6.0;6.1;7.0;7.5"
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fi
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export TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
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export NCCL_ROOT_DIR=/usr/local/cuda
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export USE_STATIC_CUDNN=1 # links cudnn statically (driven by tools/setup_helpers/cudnn.py)
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export USE_STATIC_NCCL=1 # links nccl statically (driven by tools/setup_helpers/nccl.py, some of the NCCL cmake files such as FindNCCL.cmake and gloo/FindNCCL.cmake)
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# not needed if using conda's cudatoolkit package. Uncomment to statically link a new CUDA version that's not available in conda yet
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# export ATEN_STATIC_CUDA=1 # links ATen / libcaffe2_gpu.so with static CUDA libs, also sets up special cufft linkage
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# export USE_CUDA_STATIC_LINK=1 # links libcaffe2_gpu.so with static CUDA libs. Likely both these flags can be de-duplicated
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fi
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fname_with_sha256() {
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HASH=$(sha256sum $1 | cut -c1-8)
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DIRNAME=$(dirname $1)
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BASENAME=$(basename $1)
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if [[ $BASENAME == "libnvrtc-builtins.so" ]]; then
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echo $1
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else
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INITNAME=$(echo $BASENAME | cut -f1 -d".")
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ENDNAME=$(echo $BASENAME | cut -f 2- -d".")
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echo "$DIRNAME/$INITNAME-$HASH.$ENDNAME"
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fi
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}
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DEPS_LIST=()
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# not needed if using conda's cudatoolkit package. Uncomment to statically link a new CUDA version that's not available in conda yet
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# if [[ -n "$build_with_cuda" ]]; then
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# cuda_majmin="$(echo $CUDA_VERSION | cut -f1,2 -d'.')"
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# DEPS_LIST+=("/usr/local/cuda/lib64/libcudart.so.$cuda_majmin")
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# DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
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# DEPS_LIST+=("/usr/local/cuda/lib64/libnvrtc.so.$cuda_majmin")
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# DEPS_LIST+=("/usr/local/cuda/lib64/libnvrtc-builtins.so")
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# fi
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# install
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python setup.py install
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# copy over needed dependent .so files over and tag them with their hash
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patched=()
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for filepath in "${DEPS_LIST[@]}"; do
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filename=$(basename $filepath)
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destpath=$SP_DIR/torch/lib/$filename
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cp $filepath $destpath
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patchedpath=$(fname_with_sha256 $destpath)
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patchedname=$(basename $patchedpath)
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if [[ "$destpath" != "$patchedpath" ]]; then
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mv $destpath $patchedpath
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fi
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patched+=("$patchedname")
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echo "Copied $filepath to $patchedpath"
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done
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# run patchelf to fix the so names to the hashed names
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for ((i=0;i<${#DEPS_LIST[@]};++i)); do
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find $SP_DIR/torch -name '*.so*' | while read sofile; do
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origname="$(basename ${DEPS_LIST[i]})"
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patchedname=${patched[i]}
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set +e
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patchelf --print-needed $sofile | grep $origname 2>&1 >/dev/null
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ERRCODE=$?
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set -e
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if [ "$ERRCODE" -eq "0" ]; then
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echo "patching $sofile entry $origname to $patchedname"
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patchelf --replace-needed $origname $patchedname $sofile
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fi
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done
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done
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# set RPATH of _C.so and similar to $ORIGIN, $ORIGIN/lib and conda/lib
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find $SP_DIR/torch -name "*.so*" -maxdepth 1 -type f | while read sofile; do
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echo "Setting rpath of $sofile to " '$ORIGIN:$ORIGIN/lib:$ORIGIN/../../..'
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patchelf --set-rpath '$ORIGIN:$ORIGIN/lib:$ORIGIN/../../..' $sofile
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patchelf --print-rpath $sofile
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done
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# set RPATH of lib/ files to $ORIGIN and conda/lib
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find $SP_DIR/torch/lib -name "*.so*" -maxdepth 1 -type f | while read sofile; do
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echo "Setting rpath of $sofile to " '$ORIGIN:$ORIGIN/lib:$ORIGIN/../../../..'
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patchelf --set-rpath '$ORIGIN:$ORIGIN/../../../..' $sofile
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patchelf --print-rpath $sofile
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done

‎conda/pytorch-1.1.0/conda_build_config.yaml

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blas_impl:
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- mkl # [x86_64]
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c_compiler:
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- vs2017 # [win]
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cxx_compiler:
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- vs2017 # [win]
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python:
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- 3.5
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- 3.6
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# This differs from target_platform in that it determines what subdir the compiler
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# will target, not what subdir the compiler package will be itself.
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# For example, we need a win-64 vs2008_win-32 package, so that we compile win-32
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# code on win-64 miniconda.
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cross_compiler_target_platform:
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- win-64 # [win]
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target_platform:
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- win-64 # [win]
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vc:
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- 14
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zip_keys:
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- # [win]
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- vc # [win]
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- c_compiler # [win]
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- cxx_compiler # [win]

‎conda/pytorch-1.1.0/meta.yaml

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package:
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name: pytorch{{ environ.get('PYTORCH_PACKAGE_SUFFIX') }}
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version: "{{ environ.get('PYTORCH_BUILD_VERSION') }}"
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source:
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path: "{{ environ.get('PYTORCH_GITHUB_ROOT_DIR') }}"
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requirements:
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build:
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- cmake
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- {{ compiler('c') }} # [win]
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host:
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- python
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- numpy 1.11.*
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- setuptools
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- pyyaml
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- cffi
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- mkl >=2018
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- mkl-include
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- typing
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- ninja
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{{ environ.get('MAGMA_PACKAGE') }}
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run:
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- python
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- numpy >=1.11
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- mkl >=2018
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- cffi
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- ninja
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{{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
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build:
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number: {{ environ.get('PYTORCH_BUILD_NUMBER') }}
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detect_binary_files_with_prefix: False
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string: "{{ environ.get('PYTORCH_BUILD_STRING') }}"
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script_env:
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- CUDA_VERSION
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- CUDNN_VERSION
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- CONDA_CUDATOOLKIT_CONSTRAINT
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- NO_CUDA
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- CMAKE_ARGS
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- EXTRA_CAFFE2_CMAKE_FLAGS
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- DEVELOPER_DIR
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- DEBUG
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- NO_FBGEMM
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- USE_SCCACHE # [win]
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test:
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imports:
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- torch
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source_files:
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- test
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commands:
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- OMP_NUM_THREADS=4 python ./test/run_test.py || true # [not win]
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- python ./test/run_test.py # [win]
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about:
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home: http://pytorch.org/
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license: BSD 3-Clause
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license_family: BSD
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license_file: LICENSE
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summary: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.

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