Welcome to the KServe community!
This is the starting point for becoming a contributor - improving code, improving docs, giving talks, etc.
KServe is a highly scalable and standards based Model Inference Platform on Kubernetes for Trusted AI. It is hosted as incubation project in LFAI & Data Foundation.
Visit kserve.io for in-depth information about using KServe.
KServe is an open source project with an active development community. The project was started by teams from Google, IBM, Bloomberg and NVIDIA.
We have public biweekly community meetings at 9am US/Pacific. Map that to your local time.
You can also find them on the community calendar, along with other major community events.
Meeting agendas and notes can be accessed in the working doc.
To get involved in our community you can ask questions, be a part of the conversation, or contribute to KServe's growth. KServe is an open source project that is driven by the participation of users and contributors. Join in!
- Familiar youself with the KServe contribution guideline.
- The KServe developer guideline, doc contributor guideline is the starting point for contributors to make code or doc contributions.
- To dig deeper, check out the KServe user guide and read some design docs.
- Participate in the KServe working group.
If you're looking for something to do to get your feet wet working on KServe, look for GitHub issues marked with the Help Wanted label:
Of course, even if there's not an issue opened for it, you can always do more testing throughout the platform. Similarly, you can always contribute more docs, richer docs, insightful docs. Or maybe a cool blog post?
If you've got questions or issues with using KServe, please use KServe Slack or KServe discussions to ask questions, use KServe issues to report bugs and request features where there's always someone ready to lend a hand.