CONTRIBUTING.md

TensorFlowSharp are bindings to the native TensorFlow library.

You can either use the TensorFlow C-library release binaries, or build your own from source. Here are some pre-built TensorFlow binaries you can use for each platform:

• Linux
  • CPU-only: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.1.0.tar.gz
  • GPU-enabled: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.1.0.tar.gz
• Mac: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.1.0.tar.gz
• Windows: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-1.2.0-rc0.zip

Unpack the above .tar.gz suitable for your system on a prefix that your system's dynamic linker can use, for example, go to /usr/local and unpack there.

Mac note: the package contains a .so file, you will need to rename this to .dylib for it to work.

Once you do that, you need to open the solution file on the top level directory and build. This will produce both the TensorFlowSharp library as well as compile the tests and samples.

Building your own native TensorFlow library

You will wan to use Visual Studio 2017 or Visual Studio for Mac to build.

To build the TensorFlow C library from source, follow these instructions.

This includes checking out the Tensorflow sources, installing Bazel, and building the core.

Once you do that, you will need to build the shared library. First, in the tensorflow directory, run:

./configure    

and answer the various prompts about your build. Important: building with CUDA support provides better runtime performance but has additional dependencies as discussed in the Tensorflow installation Web page.

Once configured, run:

bazel build -c opt //tensorflow:libtensorflow.so

If you want debug symbols for Tensorflow, while debugging the binding:

bazel build -c dbg --strip=never //tensorflow:libtensorflow.so

You will need the generated library (libtensorflow.so) to be installed in a system accessible location like /usr/local/lib

On Linux:

sudo cp bazel-bin/tensorflow/libtensorflow.so /usr/local/lib/

On MacOS:

sudo cp bazel-bin/tensorflow/libtensorflow.so /usr/local/lib/libtensorflow.dylib

Running the test

I am currently using Visual Studio for Mac to do the development, but this should work on Windows with VS and Linux with MonoDevelop.

Before the solution will run you will need the shared library generated to be on a location accessibly by the Mono runtime (for example /usr/local/lib).

While Tensorflow builds a library with the extension .so, you will need to make sure that it has the proper name for your platform (tensorflow.dll on Windows, tensorflow.dylib on Mac) and copy that there.

Tensorflow is a 64-bit library, so you will need to use a 64-bit Mono to run, at home (where I am doing this work), I have a copy of 64-bit Mono on /mono, so you will want to set that in your project configuration, to do this:

Ensure that your Build/Compiler settings set "Platform Target" to "x64".

Open the solution file in the top directory, and when you hit run, this will run the API test.