Latest commit 733b734 Feb 18, 2017 @migueldeicaza committed on GitHub Update README.md


TensorFlowSharp are .NET bindings to the TensorFlow library published here:


This surfaces the C API as a strongly-typed C# API.

The API binding is pretty much done, and at this point, I am polishing the API to make it more pleasant to use from C# and F# and resolving some of the kinks and TODO-items that I left while I was doing the work.

Getting Started

You need to get yourself a copy of the TensorFlow runtime, you can either build your own version (recommended, see the instructions below) or you can use a precompiled binary:

https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.0.0-rc0.tar.gz https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-darwin-x86_64-1.0.0-rc0.tar.gz https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.0.0-rc0.tar.gz https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.0.0-rc0.tar.gz

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.

It is recommended that you build your own, because these bindings of TensorFlow surface some features in the latest version of TensorFlow that are not available on the 1.0.0-rc0 builds above.

Work in Progress

These instructions reflect what you need to get up and running with the current code as I am working on it. In the long-term, we will just have NuGet packages that eliminate all the manual steps required here.

Building your own version

You will want to install Tensorflow from sources, follow the instructions for your platform here:


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, I believe this is the command I used:

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 this library 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 Xamarin Studio on a Mac to do the development, but this should work on Windows with VS and Linux with MonoDevelop, there is nothing Xamarin specific here.

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:

Open the project options (double click on the "SampleTest" project), then select Run/Default, go to the "Advanced" tab, and select "Execute in .NET runtime" and make sure that you select one that is 64-bit enabled.

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

Possible Contributions

Build More Tests

Would love to have more tests to ensure the proper operation of the framework.


The binding is pretty much complete, and at this point, I want to improve the API to be easier and more pleasant to use from both C# and F#. Creating samples that use Tensorflow is a good way of finding easy wins on the usability of the API, there are some here:



x86: It is not clear to me how to distribute the native libtensorflow to users, as it is designed to be compiled for your host system. I would like to figure out how we can distribute packages that have been compiled with the optimal set of optimizations for users to consume.

Mobile: we need to package the library for consumption on Android and iOS.

NuGet Package

Would love to have a NuGet package for all platforms.


I have logged some usability problems and bugs in Issues, feel free to take on one of those tasks.

Notes on OpDefs

Look at:

./tensorflow/core/ops/ops.pbtxt AvgPool3D and: ./tensorflow/core/ops/nn_ops.cc for the C++ implementation with type definitions

Docs on types: https://www.tensorflow.org/versions/r0.11/how_tos/adding_an_op/


Much of the online documentation comes from TensorFlow and is licensed under the terms of Apache 2 License, in particular all the generated documentation for the various operations that is generated by using the tensorflow reflection APIs.