Skip to content

JupyterHubs for use by Berkeley enrolled students

License

Notifications You must be signed in to change notification settings

STAT-89A/datahub

 
 

Repository files navigation

Berkeley JupyterHubs

Contains a fully reproducible configuration for JupyterHub on datahub.berkeley.edu, as well as its single user image.

Branches

The staging branch always reflects the state of the staging JupyterHub, and the prod branch reflects the state of the production JupyterHub.

Procedure

When developing for this deployment, always work in a fork of this repo. You should also make sure that your repo is up-to-date with this one prior to making changes. This is because other contributors may have pushed changes after you last synced with this repo but before you upstreamed your changes. When you are ready, create a pull request. The choice for base in the GitHub PR user interface should be the staging branch of this repo while the choice for head is your fork.

Once this is complete and if there are no problems, you can request that someone review the PR before merging, or you can merge yourself if you are confident. This merge will trigger a CircleCI process which upgrades the helm deployment on the staging site. When this is complete, test your changes there. For example if you updated a library, make sure that a new user server instance has the new version. If you spot any problems you can revert your change. You should test the changes soon after the merge since we do not want unverified changes to linger in staging.

If staging fails, never update production. Revert your change or call in help if necessary. If your change is successful, you will need to merge the change from staging branch to production. Create another PR, this time with the base set to prod and the head set to staging. This PR will trigger a similar Travis process. Test your change on production for good measure.

About

JupyterHubs for use by Berkeley enrolled students

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 41.8%
  • Dockerfile 36.5%
  • R 13.4%
  • Shell 8.3%