Deployment of the hubs is managed with continuous integration (TravisCI), rather than through use of the command-line kubernetes tools. Pull requests to the master branch trigger travis, as does merging a pull request. The Travis steps are scripted using python rather than calling kubectl and helm directly.
kubectl
helm
When making changes to a hub (including both Deploying New Hubs and Manage, Modify or Remove a Hub), the expected workflow is:
change the required settings in this repo (e.g. the helm charts in hub-charts, or the Dockerfiles in user-images)
hub-charts
user-images
create a pull request with your changes - this will initiate a travis run that tests the builds of the docker image(s) and the docs. This does not push to Dockerhub, and does not deploy the hub. More specifically, this build skips the before_deploy and deploy sections in .travis.yml.
before_deploy
deploy
.travis.yml
after the PR is merged, travis builds the docker image(s), pushes them to dockerhub, and deploys the hub(s). This build runs the before_deploy and deploy sections in .travis.yml.
Before making changes to a production hub (i.e. one being used for a course), test your changes on staginghub and only when that deploys correctly, copy these changes to the production hub.
staginghub
The deploy.py script at the top-level of this repo manages docker builds and deployments. The expectation is that this is only called by travis, not directly by the user (see the workflow, below).
deploy.py
Basic usage is python deploy.py chartname. The following options are available:
python deploy.py chartname
--no-setup : run without setup procedures (authentication to GCloud and docker, helm / tiller status)
--no-setup
--build : build docker images; see Customize Your Hub for setting these up
--build
--push : push images to dockerhub (option ignored if build==False)
--push
--deploy : deploy the hub
--deploy
These are the commands that deploy.py runs when deploying a hub :
helm dependency update <chartname> helm upgrade --install --namespace <chartname> <chartname> <chart_dir> --force --wait --timeout 600 --cleanup-on-fail -f <secret>
Then, for each deployments found with kubectl -n <chartname> get deployments -o name:
kubectl -n <chartname> get deployments -o name
kubectl rollout status --namespace <chartname> --watch <deployment>
Warning
If you do run deploy.py locally, use the --no-setup flag to avoid authenticating to gcloud as the travis user and borking your local access to the cluster. If that does happen, you can delete your .kube directory and re-authenticate to gcloud.
.kube