Check out the related post: Loading TensorFlow graphs from Node.js (using the C API).

The current documentation around loading a graph with C++ is pretty sparse so I spent some time setting up a barebones example. In the TensorFlow repo there are more involved examples, such as building a graph in C++. However, the C++ API for constructing graphs is not as complete as the Python API. Many features (including automatic gradient computation) are not available from C++ yet. Another example in the repo demonstrates defining your own operations but most users will never need this. I imagine the most common use case for the C++ API is for loading pre-trained graphs to be standalone or embedded in other applications.

Be aware, there are some caveats to this approach that I’ll cover at the end.

Requirements

Install Bazel: Google’s build tool used to compile things for TensorFlow.

Clone the TensorFlow repo. Be sure to include submodules using the recursive flag (thanks to @kristophergiesing for catching this):

Creating the graph

Let’s start by creating a minimal TensorFlow graph and write it out as a protobuf file. Make sure to assign names to your inputs and operations so they’re easier to assign when we execute the graph later. The node’s do have default names but they aren’t very useful: Variable_1 or Mul_3. Here’s an example created with Jupyter:

Make sure to assign names to your inputs and operations so you can reference them later.

Creating a simple binary or shared library

Let’s create a new folder like tensorflow/tensorflow/<my project name> for your binary or library to live. I’m going to call the project loader since it will be loading a graph.

Inside this project folder we’ll create a new file called <my project name>.cc (e.g. loader.cc). If you’re curious, the .cc extension is essentially the same as .cpp but is preferred by Google’s code guidelines.

Inside loader.cc we’re going to do a few things:

Initialize a TensorFlow session. Read in the graph we exported above. Add the graph to the session. Setup our inputs and outputs. Run the graph, populating the outputs. Read values from the outputs. Close the session to release resources.

Now we create a BUILD file for our project. This tells Bazel what to compile. Inside we want to define a cc_binary for our program. You can also use the linkshared option on the binary to produce a shared library or the cc_library rule if you’re going to link it using Bazel.

Here’s the final directory structure:

tensorflow/tensorflow/loader/

tensorflow/tensorflow/loader/loader.cc

tensorflow/tensorflow/loader/BUILD

Compile & Run

From the root of the tensorflow repo, run ./configure

From inside the project folder call bazel build :loader

From the repository root, go into bazel-bin/tensorflow/loader

Copy the graph protobuf to models/graph.pb

Then run ./loader and check the output!

You could also call bazel run :loader to run the executable directly, however the working directory for bazel run is buried in a temporary folder and ReadBinaryProto looks in the current working directory for relative paths.

And that should be all we need to do to compile and run C++ code for TensorFlow.

The last thing to cover are the caveats I mentioned:

The build is huge, coming in at 103MB, even for this simple example. Much of this is for TensorFlow, CUDA support and numerous dependencies we never use. This is especially true since the C++ API doesn’t support much functionality right now, as a large portion of the TensorFlow API is Python-only. There is probably a better way of linking to TensorFlow (e.g. shared library) but I haven’t gotten it working yet. There doesn’t seem to be a straightforward way of building this outside of the TensorFlow repo because of Bazel (many of the modules needed to link to are marked as internal). Again, there is probably a solution to this, it’s just non-obvious.

Conclusion

Hopefully someone can shed some light on these last points so we can begin to embed TensorFlow graphs in applications. If you are that person, message me on Twitter or email. We also do applied research to solve machine learning challenges.