Integrate TensorFlow with CMake projects effortlessly
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README.md

tensorflow-cmake

Integrate TensorFlow with CMake projects effortlessly.

TensorFlow

TensorFlow is an amazing tool for machine learning and intelligence using computational graphs. TensorFlow includes APIs for both Python and C++, although the C++ API is slightly less documented. However, the most standard way to integrate C++ projects with TensorFlow is to build the project inside the TensorFlow repository, yielding a massive binary. Additionally, Bazel is the only certified way to build such projects. This document and the code in this repository will allow one to integrate TensorFlow with CMake projects without producing a large binary.

Note: The instructions here correspond to an Ubuntu Linux environment; although some commands may differ for other operating systems and distributions, the general ideas are identical.

Step 1: Install TensorFlow

Follow the instructions for installing Bazel. Install dependencies and clone TensorFlow from its git repository:

sudo apt-get install autoconf automake libtool curl make g++ unzip  # Protobuf Dependencies
sudo apt-get install python-numpy swig python-dev python-wheel      # TensorFlow Dependencies
git clone https://github.com/tensorflow/tensorflow                  # TensorFlow

Enter the cloned repository, and append the following to the tensorflow/BUILD file:

# Added build rule
cc_binary(
    name = "libtensorflow_all.so",
    linkshared = 1,
    linkopts = ["-Wl,--version-script=tensorflow/tf_version_script.lds"], # Remove this line if you are using MacOS
    deps = [
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:tensorflow",
        "//tensorflow/cc:cc_ops",
        "//tensorflow/cc:client_session",
        "//tensorflow/cc:scope",
        "//tensorflow/c:c_api",
    ],
)

This specifies a new build rule, producing libtensorflow_all.so, that includes all the required dependencies for integration with a C++ project. Build the shared library and copy it to /usr/local/lib as follows:

./configure      # Note that this requires user input
bazel build tensorflow:libtensorflow_all.so
sudo cp bazel-bin/tensorflow/libtensorflow_all.so /usr/local/lib

Copy the source to /usr/local/include/google and remove unneeded items:

sudo mkdir -p /usr/local/include/google/tensorflow
sudo cp -r tensorflow /usr/local/include/google/tensorflow/
sudo find /usr/local/include/google/tensorflow/tensorflow -type f  ! -name "*.h" -delete

Copy all generated files from bazel-genfiles:

sudo cp bazel-genfiles/tensorflow/core/framework/*.h  /usr/local/include/google/tensorflow/tensorflow/core/framework
sudo cp bazel-genfiles/tensorflow/core/kernels/*.h  /usr/local/include/google/tensorflow/tensorflow/core/kernels
sudo cp bazel-genfiles/tensorflow/core/lib/core/*.h  /usr/local/include/google/tensorflow/tensorflow/core/lib/core
sudo cp bazel-genfiles/tensorflow/core/protobuf/*.h  /usr/local/include/google/tensorflow/tensorflow/core/protobuf
sudo cp bazel-genfiles/tensorflow/core/util/*.h  /usr/local/include/google/tensorflow/tensorflow/core/util
sudo cp bazel-genfiles/tensorflow/cc/ops/*.h  /usr/local/include/google/tensorflow/tensorflow/cc/ops

Copy the third party directory:

sudo cp -r third_party /usr/local/include/google/tensorflow/
sudo rm -r /usr/local/include/google/tensorflow/third_party/py

# Note: newer versions of TensorFlow do not have the following directory
sudo rm -r /usr/local/include/google/tensorflow/third_party/avro

Step 2: Install Eigen and Protobuf

The TensorFlow runtime library requires both Protobuf and Eigen. However, specific versions are required, and these may clash with currently installed versions of either software. Therefore, two options are provided:

  • Install the packages to a directory on your computer, which will overwrite / clash with any previous versions installed in that directory (but allow multiple projects to reference them). The default directory is /usr/local, but any may be specified to avoid clashing. This is the recommended option.
  • Add the packages as external dependencies, allowing CMake to download and build them inside the project directory, not affecting any current versions. This will never result in clashing, but the build process of your project may be lengthened.

Choose the option that best fits your needs; you may mix these options as well, installing one to /usr/local, while keeping the other confined in the current project. In the following instructions, be sure to replace <EXECUTABLE_NAME> with the name of your executable. Additionally, all generated CMake files should generally be placed in your CMake modules directory, which is commonly <PROJECT_ROOT>/cmake/Modules.

Eigen: Installing Locally

Execute the eigen.sh script as follows: sudo eigen.sh install <tensorflow-root> [<install-dir> <download-dir>]. The install command specifies that Eigen is to be installed to a directory. The <tensorflow-root> argument should be the root of the TensorFlow repository. The optional <install-dir> argument allows you to specify the installation directory; this defaults to /usr/local but may be changed to avoid other versions. The <download-dir argument specifies the directory where Eigen will be download and extracted; this defaults to the current directory.

To generate the needed CMake files for your project, execute the script as follows: eigen.sh generate installed <tensorflow-root> [<cmake-dir> <install-dir>]. The generate command specifies that the required CMake files are to be generated and placed in <cmake-dir> (this defaults to the current directory, but generally should your CMake modules directory). The optional <install-dir> argument specifies the directory Protobuf is installed to. This defaults to /usr/local and should directly correspond to the install directory specified when installing above. Two files will be copied to the specified directory: FindEigen.cmake and Eigen_VERSION.cmake. Add the following to your CMakeLists.txt:

# Eigen
find_package(Eigen REQUIRED)
include_directories(${Eigen_INCLUDE_DIRS})

Eigen: Adding as External Dependency

Execute the eigen.sh script as follows: eigen.sh generate external <tensorflow-root> [<cmake-dir>]. The external command specifies that Eigen is not installed, but rather should be treated as an external CMake dependency. The <tensorflow-root> argument again should be the root directory of the TensorFlow repository, and the optional <cmake-dir> argument is the location to copy the required CMake modules to (defaults to the current directory). Two files will be copied to the specified directory: Eigen.cmake and Eigen_VERSION.cmake. Add the following to your CMakeLists.txt:

# Eigen
include(Eigen)
add_dependencies(<EXECUTABLE_NAME> Eigen)

Protobuf: Installing Locally

Execute the protobuf.sh script as follows: sudo protobuf.sh install <tensorflow-root> [<install-dir> <download-dir>]. The arguments are identical to those described in the Eigen section above.

Generate the required files as follows: protobuf.sh generate installed <tensorflow-root> [<cmake-dir> <install-dir>]; the arguments are also identical to those above. Two files will be copied to the specified directory: FindProtobuf.cmake and Protobuf_VERSION.cmake. CMake provides us with a FindProtobuf.cmake module, but we will use our own, since we must specify the directory Protobuf was installed to. Add the following to your CMakeLists.txt:

# Protobuf
find_package(Protobuf REQUIRED)
include_directories(${Protobuf_INCLUDE_DIRS})
target_link_libraries(<EXECUTABLE_NAME> ${Protobuf_LIBRARIES})

Protobuf: Adding as External Dependency

Execute the protobuf.sh script as follows: protobuf.sh generate external <tensorflow-root> [<cmake-dir>]. The arguments are also identical to those described in the Eigen section above. Two files will be copied to the specified directory: Protobuf.cmake and Protobuf_VERSION.cmake. Add the following to your CMakeLists.txt:

# Protobuf
include(Protobuf)
add_dependencies(<EXECUTABLE_NAME> Protobuf)

Step 3: Configure the CMake Project

Copy the FindTensorflow.cmake file in this repository to your CMake modules directory. Next, edit your CMakeLists.txt to append your custom modules directory to the list of CMake modules (this is a common step in most CMake programs):

list(APPEND CMAKE_MODULE_PATH <CMAKE_MODULE_DIR>)
# Replace <CMAKE_MODULE_DIR> with your path
# The most common path is ${PROJECT_SOURCE_DIR}/cmake/Modules

If either Protobuf or Eigen was added as an external dependency, add the following to your CMakeLists.txt:

# set variables for external dependencies
set(EXTERNAL_DIR "${PROJECT_SOURCE_DIR}/external"
        CACHE PATH "Location where external dependencies will installed")
set(DOWNLOAD_LOCATION "${EXTERNAL_DIR}/src"
        CACHE PATH "Location where external projects will be downloaded")
mark_as_advanced(EXTERNAL_DIR DOWNLOAD_LOCATION)
include_directories(${EXTERNAL_DIR}/include)

The projects in the examples/ directory demonstrate the correct usage of these instructions.

Troubleshooting

Compiler Path Build Error

If Bazel fails to build the TensorFlow library, stating error: Could not find compiler "gcc" in PATH, you may have to execute the following:

bazel clean                                   # Clean project
export CC="/usr/bin/gcc"                      # Set location of C compiler
export CXX="/usr/bin/g++"                     # Set location of C++ compiler
bazel build tensorflow:libtensorflow_all.so   # Rebuild project