Support

This page describes the Kubeflow resources and support options that you can explore when you encounter a problem, have a question, or want to make a suggestion about Kubeflow.

Levels of support

Kubeflow applications offer various levels of support, based on the application status. To see the status of each application, refer to the Kubeflow application matrix on the version policies page.

The following table describes the level of support that you can expect based on the status of an application:

Support from the Kubeflow community

Kubeflow has an active and helpful community of users and contributors.

The Kubeflow community offers support on a best-effort basis for stable and beta applications. Best-effort support means that there’s no formal agreement or commitment to solve a problem but the community appreciates the importance of addressing the problem as soon as possible. The community commits to helping you diagnose and address the problem if all the following are true:

• The cause falls within the technical framework that Kubeflow controls. For example, the Kubeflow community may not be able to help if the problem is caused by a specific network configuration within your organization.
• Community members can reproduce the problem.
• The reporter of the problem can help with further diagnosis and troubleshooting.

You can ask questions and make suggestions in the following places:

• Slack for online chat and messaging. See details of Kubeflow’s Slack workspace and channels.

• Kubeflow discuss for email-based group discussion. Join the kubeflow-discuss group.

• Kubeflow documentation for overviews and how-to guides. In particular, refer to the following documents when troubleshooting a problem:

  • Kubeflow installation and setup
  • Kubeflow components
  • Further setup and troubleshooting

• Kubeflow issue trackers for known issues, questions, and feature requests. Search the open issues to see if someone else has already logged the problem that you’re encountering and learn about any workarounds to date. If no one has logged your problem, create a new issue to describe the problem.

  Each Kubeflow application has its own issue tracker within the Kubeflow organization on GitHub. To get you started, here are the primary issue trackers:

  • Kubeflow core
  • kfctl command-line tool
  • Kustomize manifests
  • Kubeflow Pipelines
  • Katib hyperparameter tuning
  • Metadata
  • Fairing notebook SDK
  • TensorFlow training (TFJob)
  • PyTorch training (PyTorchJob)
  • KFServing
  • Examples
  • Documentation

Support from providers in the Kubeflow ecosystem

The following organizations offer advice and support for Kubeflow deployments:

Support from a cloud or platform provider

If you’re using the services of a cloud provider to host Kubeflow, the cloud provider may be able to help you diagnose and solve a problem.

Consult the support page for the cloud service or platform that you’re using:

• Amazon Web Services (AWS)
• Canonical Ubuntu
• Google Cloud Platform (GCP)
• IBM Cloud
• Microsoft Azure
• Red Hat OpenShift

Other places to ask questions

You can also try searching for answers or asking a question on Stack Overflow. See the questions tagged with “kubeflow”.

Getting involved in the Kubeflow community

You can get involved with Kubeflow in many ways. For example, you can contribute to the Kubeflow code or documentation. You can join the community meetings to talk to maintainers about a specific topic. See the Kubeflow community page for further information.

Following the news

Keep up with Kubeflow news:

• The Kubeflow blog is the primary channel for announcement of new releases, events, and technical walkthroughs.

• Follow Kubeflow on Twitter for shared technical tips.

• The release notes give details of the latest updates for each Kubeflow application.

  Each Kubeflow application has its own repository within the Kubeflow organization on GitHub. Some of the applications publish release notes. To get you started, here are the release notes for the primary applications:

  • Kubeflow core
  • Kubeflow Pipelines
  • Katib hyperparameter tuning
  • Metadata
  • Fairing notebook SDK
  • TensorFlow training (TFJob)
  • PyTorch training (PyTorchJob)
  • KFServing