How to serve retrained Inception model using Tensorflow Serving?

Question

So I have trained inception model to recognize flowers according to this guide. https://www.tensorflow.org/versions/r0.8/how_tos/image_retraining/index.html

bazel build tensorflow/examples/image_retraining:retrain
bazel-bin/tensorflow/examples/image_retraining/retrain --image_dir ~/flower_photos

To classify the image via command line, I can do this:

bazel build tensorflow/examples/label_image:label_image && \
bazel-bin/tensorflow/examples/label_image/label_image \
--graph=/tmp/output_graph.pb --labels=/tmp/output_labels.txt \
--output_layer=final_result \
--image=$HOME/flower_photos/daisy/21652746_cc379e0eea_m.jpg

But how do I serve this graph via Tensorflow serving?

The guide about setting up Tensorflow serving (https://tensorflow.github.io/serving/serving_basic) does not tell how to incorporate the graph (output_graph.pb). The server expects the different format of file:

$>ls /tmp/mnist_model/00000001
checkpoint export-00000-of-00001 export.meta

Answer 1

To serve the graph after you have trained it, you would need to export it using this api: https://www.tensorflow.org/versions/r0.8/api_docs/python/train.html#export_meta_graph

That api generates the metagraph def that is needed by the serving code ( this will generate that .meta file you are asking about)

Also, you need to restore a checkpoint using Saver.save() which is the Saver class https://www.tensorflow.org/versions/r0.8/api_docs/python/train.html#Saver

Once you have done this, you will both the metagraph def and the checkpoint files that are needed to restore the graph.