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A Catkin-friendly package for utilizing the C++ API of Tensorflow.

Get Tensorflow C++ API into ROS as easy as

find_package(catkin REQUIRED COMPONENTS
  ... your other packages ...
  tensorflow_ros_cpp
)

See the usage example at https://github.com/tradr-project/tensorflow_ros_test .

Note: If you only intend to use Tensorflow from Python nodes, you don't need this package. Just add <run_depend>python-tensorflow-pip</run_depend> to your package.xml and that's it.

Multiple Tensorflow installation options

This package allows its users to "provide" Tensorflow in several ways, and the developer doesn't have to care about which option the user chooses:

• As a pip (Python) package: The easiest way on Ubuntu 14.04 / ROS Indigo. Just pip install tensorflow and that's it. GPU version supported! Can be used on newer Ubuntu versions, but with important limitations (see below).

• Using tensorflow_catkin package: Easily compile Tensorflow for your platform. A convenient way on newer systems. Supports GPU version.

• Using a custom build of Tensorflow built by bazel: The least comfortable, yet most powerful way. Compile Tensorflow yourself using bazel and tell this package where to find it.

A comprehensive list of compatible options for various OSes and ROS distros is available in the package README.

Detailed configuration options are available for each of the options.

Note for rosdep users

If you're managing dependencies via rosdep, it is likely that you do not want it to try to install the optional dependencies (currently python-tensorflow-pip and tensorflow_catkin). In such case, add the following to the rosdep call:

rosdep install ... --skip-keys=tensorflow_catkin --skip-keys=python-tensorflow-pip

C++ ABI difference problems

Ubuntu systems starting with 16.04 (Xenial) are using the new C++11 ABI for all system libraries. Until pip bug 1707002 gets resolved (if ever!), the pip-distributed Tensorflow is built againts an older C++ ABI, which is incompatible with the C++11 ABI. This means linking to the Tensorflow library will fail on such systems and there's no way around it.

The only "workaround" is to wrap all tensorflow code into a library that exposes its functions using C API (so no std::strings, std::vectors or Eigen types) and which does not link to any system library with a C++ API. Such library can then be built separately with -D_GLIBCXX_USE_CXX11_ABI=0. Then you can freely link the rest of your code using ROS, Eigen and so on to this library.

An example of this approach can be found at kinetic-devel branch of tensorflow_ros_test.

A library created this way is "backwards compatible" with older systems, so it can be used everywhere. However, separating all Tensorflow code can be tiresome, so we only suggest it as a last resort option. This is why searching for the pip-installed Tensorflow is disabled by default on systems using the new C++11 ABI (you can force it using -DFORCE_TF_PIP_SEARCH=ON).