This repository represents a fork of this repository. With some changes

• Upgraded Tensorflow version
• Removed binaries
• Changed parameters for bazel build

This repository provides scripts for compiling and installing TensorFlow for C/C++ (headers + libraries) and CMake.

Maintainer: Jan Akhremchik

This repository provides TensorFlow libraries with the following specifications:

• Provided versions: 1.14 (Default)
• Supports Ubuntu 18.04 LTS (GCC >=7.4).
• Provides variants Nvidia GPU.
• GPU variants are built to support compute capabilities: 6.1, 7.0, 7.2, 7.5

NOTE: This repository does not include or bundle the source TensorFlow repository.

First clone this repository:

git clone https://github.com/janchk/tensorflow-cpp.git

To install the special version of Eigen requried by TensorFlow that we also bundle in this repository:

cd tensorflow/eigen
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=~/.local -DCMAKE_BUILD_TYPE=Release ..
make install -j{your core count}

NOTE: We recommend installing to ~/.local in order to prevent conflicts with other version of Eigen which may be installed via apt. Eigen exports its package during the build step, so CMake will default to finding the one we just installed unless a HINT is used or CMAKE_PREFIX_PATH is set to another location.

You could install protobuf by yourself following this instruction.

NOTE: You need to use version 3.7.1

As alternative you could use script build.sh in tensorflow-cpp/protobuf which installing protobuf system-wide

Use this repository for installation.

NOTE: Install abseil AFTER tensorflow

These are the options for using the TensorFlow CMake package:

Option 1 (Recommended): Installing into the (local) file system

cd tensorflow/tensorflow
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=~/.local -DCMAKE_BUILD_TYPE=Release ..
make install -j{your core count}

NOTE: The CMake will download all required sources for tensorflow and then build them.

Option 2 (Advanced): Create symbolic link to your target workspace directory:

ln -s /<SOURCE-PATH>/tensorflow/tensorflow <TARGET-PATH>/

TensorFlow CMake can be included in other projects either using the find_package command:

...
find_package(TensorFlow CONFIG REQUIRED)
...

or alternatively included directly into other projects using the add_subdirectory command

...
add_subdirectory(/<SOURCE-PATH>/tensorflow/tensorflow)
...

NOTE: By default the CMake package will select the CPU-only variant of a given library version and defining/setting the TF_USE_GPU option variable reverts to the GPU-enabled variant.

User targets such as executables and libraries can now include the TensorFlow::TensorFlow CMake target using the target_link_libraries command.

add_executable(tf_hello src/main.cpp)
target_link_libraries(tf_hello PUBLIC TensorFlow::TensorFlow)
target_compile_features(tf_hello PRIVATE cxx_std_14)

NOTE: For more information on using CMake targets please refer to this excellent article.

If a specialized build of TensorFlow (e.g. different verion of CUDA, NVIDIA Compute Capability, AVX etc) is required, then the following steps can be taken:

1. Follow the standard instructions for installing system dependencies.
   NOTE: For GPU-enabled systems, additional steps need to be taken.
2. View and/or modify our utility script for step-by-step instructions for building, extracting and packaging all headers and libraries generated by Bazel from building TensorFlow.
3. Set the TENSORFLOW_ROOT variable with the name of the resulting directory:

cmake -DTENSORFLOW_ROOT=~/.tensorflow/lib -DCMAKE_INSTALL_PREFIX=~/.local -DCMAKE_BUILD_TYPE=Release ..

• error: 'int128' does not name a type; did you mean 'uint128'? - Try to reinstall abseil
• libtensorflow_cc.so.1: cannot open shared object file: No such file or directory - Try sudo ldconfig

Apache License 2.0