In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
Welcome to all Google Summer of Code (GSoC) participants
The Kubeflow community is delighted to be part of Google Summer of Code 2020. Community mentors look forward to working with students on their GSoC projects.
Visit the Kubeflow GSoC page to find handy information and links for GSoC students and mentors.
There are many ways to contribute! Join one of our communication channels, attend a community meeting, get to know the community, discuss updates, suggest exciting new integrations.
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 alternates weekly between US East/EMEA and US West/APAC friendly times. Joining the kubeflow-discuss mailing 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-discuss mailing list.
Slack community and channels
The Kubeflow Slack workspace offers several channels. Here are a few examples:
|Community meeting chat||#community|
|TF Operator (GitHub)||#tf-operator|
|Google Summer of Code (GSoC)||#gsoc|
The primary mailing list (email group) is kubeflow-discuss.
More detail about the Kubeflow mailing lists:
|TF Operator (GitHub)||tf-operator|
Who should consider contributing to Kubeflow?
- Folks who want to add support for other ML frameworks (e.g. PyTorch, XGBoost, scikit-learn)
- 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.
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