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saudetkarllessard
saudet
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karllessard
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Add initial build workflow for CI using GitHub Actions
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‎.github/workflows/ci.yml‎

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name: CI jobs
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on: [push, pull_request]
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jobs:
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linux-x86_64:
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runs-on: ubuntu-latest
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container: nvidia/cuda:10.1-cudnn7-devel-centos7
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strategy:
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matrix:
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ext: ["", -mkl, -gpu, -mkl-gpu]
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steps:
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- name: Install environment
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run: |
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yum -y update
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yum -y install centos-release-scl-rh epel-release
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yum -y install java-1.8.0-openjdk-devel devtoolset-7 rh-git218 rh-maven35 patch python36-devel python36-pip python36-six
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echo Downloading Bazel
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curl -L https://github.com/bazelbuild/bazel/releases/download/0.29.1/bazel-0.29.1-installer-linux-x86_64.sh -o bazel.sh --retry 10
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bash bazel.sh
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- name: Checkout repository
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uses: actions/checkout@v1
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- name: Build project
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run: |
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source scl_source enable devtoolset-7 rh-git218 rh-maven35 || true
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git --version
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gcc --version
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mvn -version
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bazel version
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df -h
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echo Executing Maven without Wagon HTTP pool that fails under Docker
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mvn clean install -B -U -e -Dmaven.wagon.http.pool=false -Djavacpp.platform=linux-x86_64 -Djavacpp.platform.extension=${{ matrix.ext }}
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df -h
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macosx-x86_64:
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runs-on: macos-latest
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strategy:
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matrix:
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ext: ["", -mkl]
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steps:
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- name: Install environment
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run: |
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python3 -m pip install six
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echo Downloading Bazel
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curl -L https://github.com/bazelbuild/bazel/releases/download/0.29.1/bazel-0.29.1-installer-darwin-x86_64.sh -o bazel.sh --retry 10
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bash bazel.sh
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- name: Checkout repository
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uses: actions/checkout@v1
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- name: Build project
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run: |
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git --version
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clang --version
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mvn -version
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bazel version
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df -h
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echo Executing Maven
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mvn clean install -B -U -e -Djavacpp.platform=macosx-x86_64 -Djavacpp.platform.extension=${{ matrix.ext }}
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df -h
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windows-x86_64:
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runs-on: windows-latest
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strategy:
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matrix:
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ext: ["", -mkl, -gpu, -mkl-gpu]
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steps:
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- name: Install environment
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shell: cmd
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run: |
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python -m pip install six
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echo Removing some unused stuff to avoid running out of disk space
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rm.exe -Rf "C:/Program Files (x86)/Android" "C:/Program Files/dotnet" "%CONDA%" "%GOROOT_1_10_X64%" "%GOROOT_1_11_X64%" "%GOROOT_1_12_X64%" "%GOROOT_1_13_X64%" "C:\hostedtoolcache\windows\Ruby" "C:\Rust"
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echo Removing old versions of MSVC that interfere with Bazel
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bash.exe -lc "find 'C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/' -iname '14.1*' -exec rm -Rf {} \;"
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echo Downloading Bazel
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mkdir C:\bazel
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curl.exe -L https://github.com/bazelbuild/bazel/releases/download/0.29.1/bazel-0.29.1-windows-x86_64.exe -o C:/bazel/bazel.exe --retry 10
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echo Installing CUDA
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curl.exe -L http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_426.00_windows.exe -o cuda.exe
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curl.exe -L https://developer.download.nvidia.com/compute/redist/cudnn/v7.6.4/cudnn-10.1-windows7-x64-v7.6.4.38.zip -o cudnn.zip
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cuda.exe -s
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mkdir cuda
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unzip.exe cudnn.zip
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cp.exe -a cuda/include cuda/lib cuda/bin "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.1/"
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- name: Checkout repository
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uses: actions/checkout@v1
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- name: Build project
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shell: cmd
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run: |
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call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64
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set "CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1"
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set "CUDA_PATH_V10_1=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1"
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set "PATH=C:\bazel;%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin;%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp;%PATH%"
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echo Shorten work paths to prevent Bazel from reaching MAX_PATH limit
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set "TEST_TMPDIR=C:\tmp"
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set "TMPDIR=C:\tmp"
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set "TEMP=C:\tmp"
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set "TMP=C:\tmp"
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mkdir C:\tmp
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git --version
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cl
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call mvn -version
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bazel version
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df -h
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wmic pagefile list /format:list
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echo Executing Maven
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call mvn clean install -B -U -e -Djavacpp.platform=windows-x86_64 -Djavacpp.platform.extension=${{ matrix.ext }}
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df -h
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wmic pagefile list /format:list

‎README.md‎

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Be aware though that the native library is quite large and including too many versions of it may
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significantly increase the size of your JAR. So it is good practice to limit your dependencies to
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the platforms you are targeting.
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the platforms you are targeting. For this purpose the `-platform` artifacts include profiles that follow
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the conventions established on this page:
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* [Reducing the Number of Dependencies](https://github.com/bytedeco/javacpp-presets/wiki/Reducing-the-Number-of-Dependencies)
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*Note: the `tensorflow-starters` artifact is not available at this moment*
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‎tensorflow-core/tensorflow-core-api/WORKSPACE‎

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],
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patch_args = ["-p1"],
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urls = [
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"https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/v2.0.0.tar.gz",
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"https://github.com/tensorflow/tensorflow/archive/v2.0.0.tar.gz",
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"https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/v2.1.0-rc2.tar.gz",
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"https://github.com/tensorflow/tensorflow/archive/v2.1.0-rc2.tar.gz",
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],
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sha256 = "49b5f0495cd681cbcb5296a4476853d4aea19a43bdd9f179c928a977308a0617",
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strip_prefix = "tensorflow-2.0.0"
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sha256 = "8f0c227024fe2dffc691e71f4498c217f604d045079db254a300720db1eb4693",
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strip_prefix = "tensorflow-2.1.0-rc2"
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)
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# START: Upstream TensorFlow dependencies

‎tensorflow-core/tensorflow-core-api/build.sh‎

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export BAZEL_VC="${VCINSTALLDIR:-}"
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if [[ -d $BAZEL_VC ]]; then
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# Work around compiler issues on Windows documented mainly in configure.py
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# Work around compiler issues on Windows documented mainly in configure.py but also elsewhere
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export BUILD_FLAGS="--copt=//arch:AVX `#--copt=//arch:AVX2` --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --define=override_eigen_strong_inline=true"
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# https://software.intel.com/en-us/articles/intel-optimization-for-tensorflow-installation-guide#wind_B_S
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export PATH=$PATH:$(pwd)/bazel-tensorflow-core-api/external/mkl_windows/lib/
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export PYTHON_BIN_PATH=$(which python.exe)
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else
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export BUILD_FLAGS="--copt=-msse4.1 --copt=-msse4.2 --copt=-mavx `#--copt=-mavx2 --copt=-mfma`"
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export BUILD_FLAGS="--copt=-msse4.1 --copt=-msse4.2 --copt=-mavx `#--copt=-mavx2 --copt=-mfma` --cxxopt=-std=c++14 --host_cxxopt=-std=c++14 --linkopt=-lstdc++ --host_linkopt=-lstdc++"
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export PYTHON_BIN_PATH=$(which python3)
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fi
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if [[ "${EXTENSION:-}" == *gpu* ]]; then
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export BUILD_FLAGS="$BUILD_FLAGS --config=cuda"
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if [[ -z ${TF_CUDA_PATHS:-} ]] && [[ -d ${CUDA_PATH:-} ]]; then
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# Work around some issue with Bazel preventing it from detecting CUDA on Windows
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export TF_CUDA_PATHS="$CUDA_PATH"
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fi
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fi
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# Build C API of TensorFlow itself including a target to generate ops for Java
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bazel build $BUILD_FLAGS --python_path="$PYTHON_BIN_PATH" --config=monolithic --output_filter=DONT_MATCH_ANYTHING --verbose_failures @org_tensorflow//tensorflow:tensorflow :java_op_gen_sources
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ls -l bazel-bin/external/org_tensorflow/tensorflow/
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# Normalize some paths with symbolic links
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TENSORFLOW_SO=(bazel-bin/external/org_tensorflow/tensorflow/libtensorflow.so.?.?.?)
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ln -sf $(basename $TENSORFLOW_DYLIB) bazel-bin/external/org_tensorflow/tensorflow/libtensorflow.dylib
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ln -sf $(basename $TENSORFLOW_DYLIB) bazel-bin/external/org_tensorflow/tensorflow/libtensorflow.2.dylib
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fi
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TENSORFLOW_LIB=(bazel-bin/external/org_tensorflow/tensorflow/tensorflow.dll.if.lib)
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if [[ -f $TENSORFLOW_LIB ]]; then
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ln -sf $(basename $TENSORFLOW_LIB) bazel-bin/external/org_tensorflow/tensorflow/tensorflow.lib
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fi
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TENSORFLOW_LIBS=(bazel-bin/external/org_tensorflow/tensorflow/tensorflow.dll.if.lib bazel-bin/external/org_tensorflow/tensorflow/libtensorflow.dll.ifso)
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for TENSORFLOW_LIB in ${TENSORFLOW_LIBS[@]}; do
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if [[ -f $TENSORFLOW_LIB ]]; then
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ln -sf $(basename $TENSORFLOW_LIB) bazel-bin/external/org_tensorflow/tensorflow/tensorflow.lib
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fi
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done
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# Copy only main generated Java source files for ops
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mkdir -p src/gen/java/

‎tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/internal/c_api/presets/tensorflow.java‎

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case "linux-ppc64le":
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case "linux-x86_64":
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case "macosx-x86_64":
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lib += lib.equals("cudnn") ? "@.7" : lib.equals("nccl") ? "@.2" : lib.equals("nvinfer") ? "@.5" : lib.equals("cudart") ? "@.10.1" : "@.10";
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lib += lib.equals("cudnn") ? "@.7" : lib.equals("nccl") ? "@.2" : lib.equals("nvinfer") ? "@.6" : lib.equals("cudart") ? "@.10.1" : "@.10";
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break;
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case "windows-x86_64":
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lib += lib.equals("cudnn") ? "64_7" : lib.equals("cudart") ? "64_101" : "64_10";

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