The new Open Data Hub version 0.8 (ODH) release includes many new features, continuous integration (CI) additions, and documentation updates. For this release, we focused on enhancing JupyterHub image builds, enabling more mixing of Open Data Hub and Kubeflow components, and designing our comprehensive end-to-end continuous integration and continuous deployment and delivery (CI/CD) process. In this article, we introduce the highlights of this newest release.
Note: Open Data Hub is an open source project and a community Operator for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift.
Continue reading “Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8”
The main goal of Kubernetes is to reach the desired state: to deploy our pods, set up the network, and provide storage. This paradigm extends to Operators, which use custom resources to define the state. When the Operator picks up the custom resource, it will always try to get to the state defined by it. That means that if we modify a resource that is managed by the Operator, it will quickly replace it to match the desired state.
Continue reading Open Data Hub and Kubeflow installation customization
It is just a few short weeks since we released Open Data Hub (ODH) 0.6.0, bringing many changes to the underlying architecture and some new features. We found a few issues in this new version with the Kubeflow Operator and a few regressions that came in with the new JupyterHub updates. To make sure your experience with ODH 0.6 does not suffer because we wanted to release early, we offer a new (mostly) bugfix release: Open Data Hub 0.6.1.
Continue reading Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions