path: root/third_party/gpus/cuda/cuda_config.sh

#!/bin/bash

# A simple script to configure the Cuda tree needed for the TensorFlow GPU
# build. We need both Cuda toolkit 7.0 and Cudnn 6.5.
# Useage:
#    * User edit cuda.config to point both Cuda toolkit and Cudnn libraries to their local path
#    * run cuda_config.sh to generate symbolic links in the source tree to reflect
#    * the file organizations needed by TensorFlow.

print_usage() {
cat << EOF
Usage: $0 [--check]
  Configure TensorFlow's canonical view of Cuda libraries using cuda.config.
Arguments:
  --check: Only check that the proper Cuda dependencies has already been
       properly configured in the source tree. It also creates symbolic links to
       the files in the gen-tree to make bazel happy.
EOF
}

CHECK_ONLY=0
# Parse the arguments. Add more arguments as the "case" line when needed.
while [[ $# > 0 ]]; do
  argument="$1"
  shift
  case $argument in
    --check)
      CHECK_ONLY=1
      ;;
    *)
      echo "Error: unknown arguments"
      print_usage
      exit -1
      ;;
  esac
done

source cuda.config || exit -1

OUTPUTDIR=${OUTPUTDIR:-../../..}
CUDA_TOOLKIT_PATH=${CUDA_TOOLKIT_PATH:-/usr/local/cuda}
CUDNN_INSTALL_PATH=${CUDNN_INSTALL_PATH:-/usr/local/cuda}

# An error message when the Cuda toolkit is not found
function CudaError {
  echo ERROR: $1
cat << EOF
##############################################################################
##############################################################################
Cuda 7.0 toolkit is missing.
1. Download and install the CUDA 7.0 toolkit and CUDNN 6.5 library;
2. Run configure from the root of the source tree, before rerunning bazel;
Please refer to README.md for more details.
##############################################################################
##############################################################################
EOF
  exit -1
}

# An error message when CUDNN is not found
function CudnnError {
  echo ERROR: $1
cat << EOF
##############################################################################
##############################################################################
Cudnn 6.5 is missing.
1. Download and install the CUDA 7.0 toolkit and CUDNN 6.5 library;
2. Run configure from the root of the source tree, before rerunning bazel;
Please refer to README.md for more details.
##############################################################################
##############################################################################
EOF
  exit -1
}

# Check that Cuda libraries has already been properly configured in the source tree.
# We still need to create links to the gen-tree to make bazel happy.
function CheckAndLinkToSrcTree {
  ERROR_FUNC=$1
  FILE=$2
  if test ! -e $FILE; then
    $ERROR_FUNC "$PWD/$FILE cannot be found"
  fi

  # Link the output file to the source tree, avoiding self links if they are
  # the same. This could happen if invoked from the source tree by accident.
  if [ ! `readlink -f $PWD` == `readlink -f $OUTPUTDIR/third_party/gpus/cuda` ]; then
    mkdir -p `dirname $OUTPUTDIR/third_party/gpus/cuda/$FILE`
    ln -sf $PWD/$FILE $OUTPUTDIR/third_party/gpus/cuda/$FILE
  fi
}

if [ "$CHECK_ONLY" == "1" ]; then
  CheckAndLinkToSrcTree CudaError include/cuda.h
  CheckAndLinkToSrcTree CudaError include/cublas.h
  CheckAndLinkToSrcTree CudnnError include/cudnn.h
  CheckAndLinkToSrcTree CudaError lib64/libcudart_static.a
  CheckAndLinkToSrcTree CudaError lib64/libcublas.so.7.0
  CheckAndLinkToSrcTree CudnnError lib64/libcudnn.so.6.5
  CheckAndLinkToSrcTree CudaError lib64/libcudart.so.7.0
  exit 0
fi

# Actually configure the source tree for TensorFlow's canonical view of Cuda
# libraries.

if test ! -e ${CUDA_TOOLKIT_PATH}/lib64/libcudart.so.7.0; then
  CudaError "cannot find ${CUDA_TOOLKIT_PATH}/lib64/libcudart.so.7.0"
fi

if test ! -d ${CUDNN_INSTALL_PATH}; then
  CudnnError "cannot find dir: ${CUDNN_INSTALL_PATH}"
fi

# Locate cudnn.h
if test -e ${CUDNN_INSTALL_PATH}/cudnn.h; then
  CUDNN_HEADER_PATH=${CUDNN_INSTALL_PATH}
elif test -e ${CUDNN_INSTALL_PATH}/include/cudnn.h; then
  CUDNN_HEADER_PATH=${CUDNN_INSTALL_PATH}/include
else
  CudnnError "cannot find cudnn.h under: ${CUDNN_INSTALL_PATH}"
fi

# Locate libcudnn.so.6.5
if test -e ${CUDNN_INSTALL_PATH}/libcudnn.so.6.5; then
  CUDNN_LIB_PATH=${CUDNN_INSTALL_PATH}
elif test -e ${CUDNN_INSTALL_PATH}/lib64/libcudnn.so.6.5; then
  CUDNN_LIB_PATH=${CUDNN_INSTALL_PATH}/lib64
else
  CudnnError "cannot find libcudnn.so.6.5 under: ${CUDNN_INSTALL_PATH}"
fi

# Helper function to build symbolic links for all files under a directory.
function LinkOneDir {
  SRC_PREFIX=$1
  DST_PREFIX=$2
  SRC_DIR=$3
  DST_DIR=`echo $SRC_DIR | sed "s,^$SRC_PREFIX,$DST_PREFIX,"`
  mkdir -p $DST_DIR
  FILE_LIST=`find -L $SRC_DIR -maxdepth 1 -type f`
  if test "$FILE_LIST" != ""; then
    ln -sf $FILE_LIST $DST_DIR/ || exit -1
  fi
}
export -f LinkOneDir

# Build links for all files under the directory, including subdirectoreis.
function LinkAllFiles {
  SRC_DIR=$1
  DST_DIR=$2
  find -L $SRC_DIR -type d | xargs -I {} bash -c "LinkOneDir $SRC_DIR $DST_DIR {}" || exit -1
}

# Set up the symbolic links for cuda toolkit. We link at individual file level,
# not at the directory level.
# This is because the external library may have different file layout from our desired structure.
mkdir -p $OUTPUTDIR/third_party/gpus/cuda
echo "Setting up Cuda include"
LinkAllFiles ${CUDA_TOOLKIT_PATH}/include $OUTPUTDIR/third_party/gpus/cuda/include || exit -1
echo "Setting up Cuda lib64"
LinkAllFiles ${CUDA_TOOLKIT_PATH}/lib64 $OUTPUTDIR/third_party/gpus/cuda/lib64 || exit -1
echo "Setting up Cuda bin"
LinkAllFiles ${CUDA_TOOLKIT_PATH}/bin $OUTPUTDIR/third_party/gpus/cuda/bin || exit -1
echo "Setting up Cuda nvvm"
LinkAllFiles ${CUDA_TOOLKIT_PATH}/nvvm $OUTPUTDIR/third_party/gpus/cuda/nvvm || exit -1

# Set up symbolic link for cudnn
ln -sf $CUDNN_HEADER_PATH/cudnn.h $OUTPUTDIR/third_party/gpus/cuda/include/cudnn.h || exit -1
ln -sf $CUDNN_LIB_PATH/libcudnn.so.6.5 $OUTPUTDIR/third_party/gpus/cuda/lib64/libcudnn.so.6.5 || exit -1