By Dan Sun and Animesh Singh on behalf of the Kubeflow Serving Working Group

KFServing is now KServe

We are excited to announce the next chapter for KFServing. In coordination with the Kubeflow Project Steering Group, the KFServing GitHub repository has now been transferred to an independent KServe GitHub organization under the stewardship of the Kubeflow Serving Working Group leads.

The project has been rebranded from KFServing to KServe, and we are planning to graduate the project from Kubeflow Project later this year.

Developed collaboratively by Google, IBM, Bloomberg, NVIDIA, and Seldon in 2019, KFServing was published as open source in early 2019. The project sets out to provide the following features:

  • A simple, yet powerful, Kubernetes Custom Resource for deploying machine learning (ML) models on production across ML frameworks.
  • Provide performant, standardized inference protocol.
  • Serverless inference according to live traffic patterns, supporting “Scale-to-zero” on both CPUs and GPUs.
  • Complete story for production ML Model Serving including prediction, pre/post-processing, explainability, and monitoring.
  • Support for deploying thousands of models at scale and inference graph capability for multiple models.

KFServing was created to address the challenges of deploying and monitoring machine learning models on production for organizations. After publishing the open source project, we’ve seen an explosion in demand for the software, leading to strong adoption and community growth. The scope of the project has since increased, and we have developed multiple components along the way, including our own growing body of documentation that needs it’s own website and independent GitHub organization.

What’s Next

Over the coming weeks, we will be releasing KServe 0.7 outside of the Kubeflow Project and will provide more details on how to migrate from KFServing to KServe with minimal disruptions. KFServing 0.5.x/0.6.x releases are still supported in next six months after KServe 0.7 release. We are also working on integrating core Kubeflow APIs and standards for the conformance program.

For contributors, please follow the KServe developer and doc contribution guide to make code or doc contributions. We are excited to work with you to make KServe better and promote its adoption by more and more users!

Contributor Acknowledgement

We’d like to thank all the KServe contributors for this transition work!