AI/ML

Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions

Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions

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.

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Open Data Hub 0.6 brings component updates and Kubeflow architecture

Open Data Hub 0.6 brings component updates and Kubeflow architecture

Open Data Hub (ODH) is a blueprint for building an AI-as-a-service platform on Red Hat’s Kubernetes-based OpenShift 4.x. Version 0.6 of Open Data Hub comes with significant changes to the overall architecture as well as component updates and additions. In this article, we explore these changes.

From Ansible Operator to Kustomize

If you follow the Open Data Hub project closely, you might be aware that we have been working on a major design change for a few weeks now. Since we started working closer with the Kubeflow community to get Kubeflow running on OpenShift, we decided to leverage Kubeflow as the Open Data Hub upstream and adopt its deployment tools—namely KFdef manifests and Kustomize—for deployment manifest customization.

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Python wheels, AI/ML, and ABI compatibility

Python wheels, AI/ML, and ABI compatibility

Python has become a popular programming language in the AI/ML world. Projects like TensorFlow and PyTorch have Python bindings as the primary interface used by data scientists to write machine learning code. However, distributing AI/ML-related Python packages and ensuring application binary interface (ABI) compatibility between various Python packages and system libraries presents a unique set of challenges.

The manylinux standard (e.g., manylinux2014) for Python wheels provides a practical solution to these challenges, but it also introduces new challenges that the Python community and developers need to consider. Before we delve into these additional challenges, we’ll briefly look at the Python ecosystem for packaging and distribution.

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Red Hat Summit 2018 to focus on Modern App Development

Red Hat Summit 2018 to focus on Modern App Development

Update: May 15th 2018 – Watch over 100 Red Hat Summit 2018 session videos online

On behalf of the selection teams for Modern Application Development, I am pleased to share this exciting, dynamic, and diverse set of developer-related breakouts, workshops, BoFs, and labs for Red Hat Summit 2018.

With these 61+ sessions listed below, we believe that every attending application developer will come away with a strong understanding of where Red Hat is headed in this app dev space, and obtain a good foundation for tackling that next generation of apps. Encompassing various aspects of Modern App Dev, some sub-topics we’ve focused on are around microservices, service mesh, security and AI/ML, plus there is a large collection of complementary and related topics.

So…if you’re an application developer, we invite you to attend Red Hat Summit 2018 and experience the code first hand. There’s something for everyone and definitely something for you. Register today.

Great talks don’t happen without great speakers, and we feel really privileged to have these popular, high-in-demand speakers:

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