To get started, you should download the source code from Github, by following the instructions here (you’ll need Bazel and a recent version of GCC).
The C++ API (and the backend of the system) is in tensorflow/core
. Right now, only the C++ Session interface, and the C API are being supported. You can use either of these to execute TensorFlow graphs that have been built using the Python API and serialized to a GraphDef
protocol buffer. There is also an experimental feature for building graphs in C++, but this is currently not quite as full-featured as the Python API (e.g. no support for auto-differentiation at present). You can see an example program that builds a small graph in C++ here.
The second part of the C++ API is the API for adding a new OpKernel
, which is the class containing implementations of numerical kernels for CPU and GPU. There are numerous examples of how to build these in tensorflow/core/kernels
, as well as a tutorial for adding a new op in C++.