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


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 \

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.