This package contains helpers and examples for using GraphPipe with tensorflow. It contains a new plug-in operation for tensorflow that makes a call to a GraphPipe remote model from within a local tensorflow graph. The new operation is called remote_op and communicates with the remote model using libcurl and the GraphPipe protocol.

Additionaly, a new keras layer is included based on the remote operation. This allows you to include a layer in a keras model that makes a remote call.

Finally, various examples are included of serving tensorflow models in python. For production, a more performant server like graphpipe-tf is recommended, but the python server is useful for experimentation.

• Jupyter Notebook: serving and querying VGG with GraphPipe
• Complete client/server example
• Simple tensorflow model server
• Keras to GraphDef
• Using a remote operation
• Tensorflow graph to GraphDef

Building manually requires a few libraries to be installed, but the Makefile will happily run a build for you in a docker container.

  make build

See build_linux.sh for the additional headers besides libcurl that you will need to build the C library. (From tensorflow and flatbuffers)

If you've successfully built the C library, to build installation packages:

python setup.py bdist_wheel

Note that these are not manylinux wheels and depend on libcurl being installed