Create a end-to-end kubeflow example using seq2seq model (3/n)

github_issue_summarization/README.md

@@ -21,10 +21,11 @@ By the end of this tutorial, you should learn how to:
     datasets
 *   Train a Sequence-to-Sequence model using TensorFlow on the cluster using
     GPUs
-*   Serve the model using TensorFlow Serving
+*   Serve the model using a Tornado Server
 
 ## Steps:
 
 1.  [Setup a Kubeflow cluster](setup_a_kubeflow_cluster.md)
 1.  [Training the model](training_the_model.md)
+1.  [Serving the model](serving_the_model.md)
 1.  [Teardown](teardown.md)

github_issue_summarization/notebooks/server.py

@@ -0,0 +1,54 @@
+from __future__ import print_function
+
+import logging
+
+import tornado.web
+from tornado import gen
+from tornado.options import define, options, parse_command_line
+from keras.models import load_model
+import dill as dpickle
+from seq2seq_utils import Seq2Seq_Inference
+
+define("port", default=8888, help="run on the given port", type=int)
+define("instances_key", default='instances', help="requested instances json object key")
+
+
+class PredictHandler(tornado.web.RequestHandler):
+    @gen.coroutine
+    def post(self):
+        request_key = self.settings['request_key']
+        request_data = tornado.escape.json_decode(self.request.body)
+        model = self.settings['model']
+        predictions = [model.generate_issue_title(body)[1] for body in request_data[request_key]]
+        self.write(dict(predictions=predictions))
+
+
+class IndexHandler(tornado.web.RequestHandler):
+    def get(self):
+        self.write('Hello World')
+
+
+def main():
+    parse_command_line()
+    with open('body_pp.dpkl', 'rb') as f:
+        body_pp = dpickle.load(f)
+    with open('title_pp.dpkl', 'rb') as f:
+        title_pp = dpickle.load(f)
+        model = Seq2Seq_Inference(encoder_preprocessor=body_pp,
+                                  decoder_preprocessor=title_pp,
+                                  seq2seq_model=load_model('seq2seq_model_tutorial.h5'))
+    app = tornado.web.Application(
+        [
+            (r"/predict", PredictHandler),
+            (r"/", IndexHandler),
+        ],
+        xsrf_cookies=False,
+        request_key=options.instances_key,
+        model=model)
+    app.listen(options.port)
+    logging.info('running at http://localhost:%s' % options.port)
+    tornado.ioloop.IOLoop.current().start()
+
+
+if __name__ == "__main__":
+    main()

github_issue_summarization/serving_the_model.md

@@ -0,0 +1,21 @@
+# Serving the model
+
+We are going to use a simple tornado server to serve the model. The [server.py](notebooks/server.py) contains the server code.
+
+Start the server using `python server.py --port=8888`.
+
+> The model is written in Keras and when exported as a TensorFlow model seems to be incompatible with TensorFlow Serving. So we're using our own webserver to serve this model. More details [here](https://github.com/kubeflow/examples/issues/11#issuecomment-371005885).
+
+## Sample request
+
+```
+curl -X POST -H 'Content-Type: application/json' -d '{"instances": ["issue overview add a new property to disable detection of image stream files those ended with -is.yml from target directory. expected behaviour by default cube should not process image stream files if user does not set it. current behaviour cube always try to execute -is.yml files which can cause some problems in most of cases, for example if you are using kuberentes instead of openshift or if you use together fabric8 maven plugin with cube"]}' http://localhost:8888/predict
+```
+
+## Sample response
+
+```
+{"predictions": ["add a new property to disable detection"]}
+```
+
+Next: [Teardown](teardown.md)