S2I image is for running tensorflow model server on Openshift.
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tensorflow_model_server

README.md

Radanalytics Tensorflow Serving

This is a builder image for a tensorflow serving applications. It is meant to be used in an openshift project with tensorflow models.

The final image will have tensorflow model installed along with tensorflow_model_server binary, a startup script and associated utilities to start tensorflow prediction endpoint at port 6006.

Integration With OpenShift

To make it easier to deploy a tensorflow serving endpoint a template for OpenShift is also included. This can be loaded into your project using:

oc create -f https://raw.githubusercontent.com/radanalyticsio/tensorflow-serving-s2i/master/template.json

Once loaded, select the tensorflow-server template from the web console. The APPLICATION_NAME , SOURCE_REPOSITORY and SOURCE_DIRECTORYmust be specified.

OR

You can create from commandline.Just create a new application within OpenShift, pointing the S2I builder at the Git repository containing your tensorflow model files.

oc new-app --template=tensorflow-server \
    --param=APPLICATION_NAME=tf-reg \
    --param=SOURCE_REPOSITORY=https://github.com/sub-mod/mnist-models \
    --param=SOURCE_DIRECTORY=regression

To have any changes to your model automatically redeployed when changes are pushed back up to your Git repository, you can use the web hooks integration of OpenShift to create a link from your Git repository hosting service back to OpenShift.

Producing a build image

To produce a builder image:

$ make build

To print usage information for the builder image:

$ sudo docker run -t <id from the make>

To poke around inside the builder image:

$ sudo docker run -i -t <id from the make>
bash-4.2$ cd /opt/app-root # take a look around

To tag and push a builder image:

$ sudo make push

By default this will tag the image as radanalyticsio/tensorflow-serving-s2i, edit the Makefile and change PUSH_IMAGE to control this.

s2i bin files

S2i scripts are located at ./s2i/bin.