Graphpipe helpers for TensorFlow remote ops
GraphPipe helpers for TensorFlow
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
recommended, but the python server is useful for experimentation.
List Of Examples
- 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.
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
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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