Merge remote-tracking branch 'upstream/master' into third-party

OWNERS

@@ -0,0 +1,21 @@
+# TODO(jlewi): We should probably have OWNERs files in subdirectories that 
+# list approvers for individual components (e.g. Seldon folks for Seldon component)
+approvers:
+  - ankushagarwal
+  - DjangoPeng
+  - gaocegege
+  - jlewi
+  - llunn
+  - ScorpioCPH
+reviewers:
+  - ankushagarwal
+  - DjangoPeng
+  - gaocegege
+  - Jimexist
+  - jlewi
+  - llunn
+  - nkashy1
+  - ScorpioCPH
+  - texasmichelle
+  - wbuchwalter
+  - zjj2wry

github_issue_summarization/README.md

@@ -26,4 +26,5 @@ By the end of this tutorial, you should learn how to:
 ## Steps:
 
 1.  [Setup a Kubeflow cluster](setup_a_kubeflow_cluster.md)
+1.  [Training the model](training_the_model.md)
 1.  [Teardown](teardown.md)

github_issue_summarization/setup_a_kubeflow_cluster.md

@@ -51,7 +51,7 @@ For this example, provision a `10GB` NFS mount with the name
 
 After the NFS is ready, delete the `tf-hub-0` pod so that it gets recreated and
 picks up the NFS mount. You can delete it by running `kubectl delete pod
-tf-hub-0 -n={NAMESPACE}`
+tf-hub-0 -n=${NAMESPACE}`
 
 At this point you should have a 10GB mount `/mnt/github-issues-data` in your
 Jupyter Notebook pod. Check this by running `!df` in your Jupyter Notebook.

issue_summarization_github_issues/training_the_model.md

@@ -0,0 +1,9 @@
+# Training the model
+
+By this point, you should have a Jupyter Notebook running at `http://127.0.0.1:8000`.
+
+Open the Jupyter Notebook interface and create a new Terminal by clicking on New -> Terminal. In the Terminal, clone this git repo by executing: `git clone https://github.com/kubeflow/examples.git`.
+
+Now you should have all the code required to complete this tutorial in the `examples/issue_summarization_github_isses/notebooks` folder. Navigate to this folder. Here you should see two files: `Tutorial.ipynb` and `seq2seq_utils.py`. Open `Tutorial.ipynb` - this contains a complete walk-through of how to go about downloading the training data, preprocessing it and training it.
+
+Next: [Serving the model](serving_the_model.md)