#The ikingssgc hub 0.8 how to
#The ikingssgc hub 0.8 install
#The ikingssgc hub 0.8 software
#The ikingssgc hub 0.8 code

You will find details about specific components in each component's GitHub section. Visit opendatahub.io for the complete Open Data Hub 0.8 documentation. The team is also continuing to investigate a continuous deployment and delivery system for a complete and dynamic CI/CD pipeline. We need to expand them to include more comprehensive tests such as the ones that we developed for the odh-manifest repository. At the moment, however, the tests only verify the manifests. We also added the ability to run these tests in the forked Kubeflow manifest repository in our CI pipeline. We added more tests to Open Data Hub components, including Apache Kafka and Superset, and we enhanced JupyterHub testing by adding Selenium for web portal testing. See the ODH documentation for more information about mixing components. The example also includes the tfjobs Kubeflow component, which was ported in ODH 0.7.

#The ikingssgc hub 0.8 install

To install the PyTorch Operator with ODH, please use the distributed-training example Kfdef. To install Kubeflow with monitoring, please use the Kubeflow-monitoring example Kfdef.Īs a continuation of our previous effort to provide more distributed-learning tools to ODH, the PyTorch Operator now works with ODH components. Two Kubeflow components that you can use for monitoring are tfjobs and pytorchjobs. As shown in Figure 1, Elyra is now included in a JupyterHub notebook image.įigure 3: Monitoring the Argo workflow in a Grafana dashboard.'> Elyra lets you execute this process from the JupyterLab portal with just a few clicks. The process of converting all of the work that a data scientist has created in notebooks to a production-level pipeline is cumbersome and usually manual. In an effort to allow data scientists to turn their notebooks into Argo Workflows or Kubeflow pipelines, we've added an exciting new tool called Elyra to Open Data Hub 0.8. We've also updated Open Data Hub 0.8 with Elyra, an AI toolkit that lets you launch JupyterLab images.

#The ikingssgc hub 0.8 how to

For more comprehensive information about Thoth and how to use it, visit Thoth Station.

#The ikingssgc hub 0.8 software

Notebook images are now built and maintained by Thoth Station, which is an artificial intelligence (AI) tool that analyzes and recommends software stacks for artificial intelligence applications. We also streamlined all of the images used by JupyterHub by pulling them from different private and public repositories into two repositories on Quay.io: odh-jupyterhub and thoth-station.

#The ikingssgc hub 0.8 code

Moving forward, all code changes and feature enhancements for JupyterHub will go under these two new repositories. In an effort to streamline code changes to JupyterHub, we forked two pivotal repositories under the opendatahub-io GitHub project: jupyterhub-quickstart and jupyterhub-odh. 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. In this article, we introduce the highlights of this newest release. 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. The new Open Data Hub version 0.8 (ODH) release includes many new features, continuous integration (CI) additions, and documentation updates.