ODH Operator image now includes a local copy of the release version of the ODH Core deployment manifests that can be referenced from any supported kfdef

Update to version v2.11

Update to version v1.0.0

Upgrade to Project CodeFlare v0.0.4

Default to the `rolling` OLM release channel and deprecate `stable`

Upgrade to v2.9

Upgrade to v0.2.0

Incubation of the Distributed Workloads stack supporting the CodeFlare SDK

Migration to the operator-sdk 1.24

Update to v0.11.0

Add Python 3.8 / 3.9 versions of all notebook images

Add intial UI support for the new Model Serving stack

Adds support for namespace isolation of Data Science Pipelines

Updates to the etcd and minio setups

Add UI support for admin configurations

Categorize existing components (as of ODH v1.3.0) into support Tiers for updates/deprecation and designate maintainers Tiered approach <br> - Tier 0 - ODH Core <br> - Tier 1 - Long Term community support with assigned MAINTAINERS and full test suite <br> - Tier 2 - Any components in the <a href="https://github.com/opendatahub-io-contrib" >opendatahub-io-contrib</a> org that are not included in the ODH Core deployment but maintain ODH integrations of interest to the community<br>

Admin - Bring your own notebooks+ Cluster Settings

Allow admins the flexibility to provide to users their own notebook images

Implementation of Model Mesh using Oauth

Includes ability to install Kubeflow v1.5.0 components on OpenShift <br> Support for the integration of Service Mesh with Kubeflow v1.5 on OpenShift

Add User Interface elements for users to enter new custom notebook images

Enable Kubeflow 1.3 on OCP 4.6/4.7/4.8 by resolving all issues.

Replace Istio stack with Red Hat Serivce Mesh in Kubeflow

Architect an integrated authentication solution for ODH and KF.

This includes updating the Kubeflow 1.3.1 openshift stack to work on OCP 4.4+

Update the Openshift Kubeflow distribution to Kubeflow version 1.3

Installation of Red Hat OpenShift Pipelines along with all of the required custom resources to enable a workflow supported by Tekton pipelines

Trino is a fast distributed SQL query engine that can integrate with multiple data sources such as S3, SQL databases, and NoSQL databases

Updates include new Spawner user interface,ability to customize and specify JupyterHub PostgreSQL parameters and new notebook images with JupyterLab.

Updates include support for Operator Level 4 (Deep Insights), new Prometheus metrics collection, new Grafana Dashboard and new status field to reflect operator installation status.

Integrated with Openshift Authentication to access the dashboard.

Migrate Open Data Hub Data Catalog component from Ansible to Kustomize.

Enable KFServing in Open Data Hub.

Enable Kubeflow Pipelines using Tekton in Open Data Hub.

Investigate and introduce ability to deploy ODH on disconnected OpenShift clusters.

Replace existing static HTML spawner UI with dynamic React base one which will allow for more customization and easier extensabiliy.

Add OpenShift stack in Kubeflow 1.2 to achieve release sync between ODH and KF

Investigate and introduce ability to deploy ODH on disconnected OpenShift clusters.

Continuation of the "UBI based ODH" expanding to Kubeflow project and looking at what does it take to move Kubeflow components to UBI.

Continuation of the of the effort to design and create a complete CI/CD process.

Add an Object Storage tool based on Rook-Ceph

Continuation of the effort to enable Prometheus enpoints in all Open Data Hub and Kubeflow components.

Team is currently creating an Open Data Hub dashboard that will be the entry point to all installed components.

Added a JupyterLab notebook image that includes Elyra. Elyra converts notebooks to Argo or Kubeflow pipelines.

Forked JupyterHub repos under Open Data Hub github repo for maintaining new changes. Added notebook images to Thoth Station for building. Added ability to launch JupyterLab images.

Added a JupyterLab notebook image that includes Elyra. Elyra converts notebooks to Argo or Kubeflow pipelines.

Added Pytorch operator to work with Open Data Hub components.

Added monitoring to Kubeflow components by enabling monitoring to Argo and adding Prometheus and Grafana to Kubeflow installation.

Added more tests to Open Data Hub components including Kafka and Superset and enhanced JupyterHub testing by adding Selenium for web portal testing

The main goal of this initiative is to verify Kubeflow 1.0 works on OpenShift and fix the issues we find. Another goal is to document and ideally automate some of the verification process to start enabling the CI for KF on OpenSHift.

Extending tests for all components, enabling CI for the operator repository.

Proving users can mix ODH and KF components, compiling a prioritized list of components to be verified and fixed, proving on the first component (probably TF Job or Pytorch Job)

Since ODH relies on S3 compatible object storage, add dependency on some minimal install of OpenShift Container Storage.

Data Catalog is the last component missing the conversion to Kustomize.

Use Kubeflow operator as a base for ODH.

Convert all ODH components to Kustomize to match Kubeflow deployment tooling.

Investicate and kickstart ODH CI based on OpenShift CI

Since the goal of this release is to get closer to KF community, we need to move to Github

Add Apache Airflow as a component into Open Data Hub