* Make it easier to demo serving and run in Katacoda
* Allow the model path to be specified via environment variables so that
we could potentially load the model from PVC.
* Continue to bake the model into the image so that we don't need to train
in order to serve.
* Parameterize download_data.sh so we could potentially fetch different sources.
* Update the Makefile so that we can build and set the image for the serving
component.
* Fix lint.
* Update the serving docs.
* Rename issue_summarization.py to IssueSummarization.py
* The module name is supposed to be the same as the class name
* Fix the predict method signature
* Fix lint
* Add .pylintrc
* Resolve lint complaints in agents/trainer/task.py
* Resolve lint complaints with flask app.py
* Resolve linting issues
Remove duplicate seq2seq_utils.py from workflow/workspace/src
* Use python 3.5.2 with pylint to match prow
Put pybullet import back into agents/trainer/task.py with a pylint ignore statement
Use main(_) to ensure it works with tf.app.run
* Create a end-to-end kubeflow example using seq2seq model (4/n)
* Move from a custom tornado server to a seldon-core model
Related to #11
* Update to use gcr.io registry for serving image
Jeremy Lewi
Ankush Agarwal
Michelle Casbon