* Setup continuous building of Docker images and testing for GH Issue Summarization Example.
* This is the first step in setting up a continuously running CI test.
* Add support for building the Docker images using GCB; we will use GCB
to trigger the builds from our CI system.
* Make the Makefile top level (at root of GIS example) so that we can
easily access all the different resources.
* Add a .gitignore file to avoid checking in the build directory used by
the Makefile.
* Define an Argo workflow to use as the E2E test.
Related to #92: E2E test & CI for github issue summarization
* Trigger the test on pre & post submit
* Dockerfile.estimator don't install the data_download.sh script
* It doesn't look like we are currently using data_download.sh in the
DockerImage
* It looks like it only gets used vias the ksonnet job which mounts the
script via a config map
* Copying data_download.sh to the Docker image is currently weird
given the organization of the Dockerfile and context.
* Copy the test_data to the Docker images so that we can run the test
inside the images.
* Invoke the python unittest for training from our CI system.
* In a follow on PR we will update the test to emit a JUnit XML file to
report results to prow.
* Fix image build.
* Fix gh-demo.kubeflow.org and make it easy to setup.
* Our public demo of the GitHub issue summarization example
(gh-demo.kubeflow.org) is down. It was running in one of our dev
clusters and with the the churn in dev clusters it ended up getting deleted.
* To make it more stable lets move it to project kubecon-gh-demo-1
and create a separate cluster for running it.
This cluster can also serve as a readily available Kubeflow cluster
setup for giving demos.
* Create the directory demo within the github_issue_summarization example
to contain all the required files.
* Add a makefile to make building the image work.
* The ksonnet app for the public demo was previously stored here
https://github.com/kubeflow/testing/tree/master/deployment/ks-app
* Fix the uiservice account.
* Address comments.
* Add component parameters
Add model_url & port arguments to flask app
Add service_type, image, and model_url parameters to ui component
Fix problem argument in tensor2tensor component
* Fix broken UI component
Fix broken UI component structure by adding all, service, & deployment parts
Add parameter defaults for tfjob to resolve failures deploying other components
* Add missing imports in flask app
Fix syntax error in argument parsing
Remove underscores from parameter names to workaround ksonnet bug #554: https://github.com/ksonnet/ksonnet/issues/554
* Fix syntax errors in t2t instructions
Add CPU image build arg to docker build command for t2t-training
Fix link to ksonnet app dir
Correct param names for tensor2tensor component
Add missing params for tensor2tensor component
Fix apply command syntax
Swap out log view pod for t2t-master instead of tf-operator
Fix link to training with tfjob
* 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
* Add barebones frontend
Add instructions for querying the trained model via a simple frontend
deployed locally.
* Add instructions for running the ui in-cluster
TODO: Resolve ksonnet namespace collisions for deployed-service
prototype
* Remove reference to running trained model locally
Jeremy Lewi
Michelle Casbon
Lun-Kai Hsu
Ankush Agarwal