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About the Kubeflow Community
In the interest of fostering an open and welcoming environment, we ascontributors and maintainers pledge to make participation in our project andour community a harassment-free experience for everyone, regardless of age, bodysize, disability, ethnicity, gender identity and expression, level ofexperience, education, socio-economic status, nationality, personal appearance,race, religion, or sexual identity and orientation.
The Kubeflow community is guided by our Code ofConduct,which we encourage everybody to read before participating. We hold our leadersaccountable for the guidelines inthis document.
Community discussions
There are many ways to contribute! Join one of our communication channels,attend a community meeting, get to know the community, discuss updates, suggestexciting new integrations.
Community meetings
Meeting calendar (iCal version).
If your group has a regular meeting, talk to@ewilderj about getting it added to the calendar.
Kubeflow community call
The project team holds a weekly community call on Tuesdays. This call alternatesweekly between US East/EMEA and US West/APAC friendly times. Joining thekubeflow-discussmailing list will automatically send you calendar invitations for the meetings, or you can subscribe to the community meeting calendar above.
Agenda, notes, and a reminder of the next call are sent to the kubeflow-discussmailing list.
Forums and mailing groups
Summary:
More detail:
| Topic | Mailing list | Slack |
|---|---|---|
| General discussion | kubeflow-discuss | #general |
| TF Operator (GitHub) | tf-operator | #tf-operator |
| Community meeting chat | n/a | #community |
Slack server: kubeflow.slack.com
Who should consider contributing to Kubeflow?
- Folks who want to add support for other ML frameworks (e.g. PyTorch, XGBoost, scikit-learn, etc…)
- Folks who want to bring more Kubernetes magic to ML (e.g. ISTIO integration for prediction)
- Folks who want to make Kubeflow a richer ML platform (e.g. support for ML pipelines, hyperparameter tuning)
- Folks who want to tune Kubeflow for their particular Kubernetes distribution or Cloud
- Folks who want to write tutorials or blog posts showing how to use Kubeflow to solve ML problems
For details on contributing please look at the contributor’s guide.
