Red Hat Announces Schedule and Speaker Line-Up for OpenShift Commons Gathering March 28th in Berlin

Red Hat Announces Schedule and Speaker Line-Up for OpenShift Commons Gathering March 28th in Berlin

The OpenShift Commons Gathering will bring together the brightest technical minds to discuss the future of OpenShift and its related upstream open source projects. With OpenShift Container Platform quickly gaining adoption around the world, the OpenShift Commons Gathering will feature talks from upstream project leads and case studies from users like Red Hat, Google, Microsoft Azure, Amadeus, T-Systems, Volvo, Weave, CNCF and more. This event will also include face-to-face meetings for all the OpenShift Commons Special Interest Groups and allow ample time for peer-to-peer networking.

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Announcing Easy Interactive OpenShift Tutorials for Developers

Announcing Easy Interactive OpenShift Tutorials for Developers

The OpenShift Developer Evangelist team is happy to release the first iteration of our work with Katacoda – interactive OpenShift tutorials! The idea with these tutorials is that you get your own individual OpenShift environment with instructions right next to it. You can work through the instructions at your own pace but you are using a fully-functioning OpenShift environment with working URLs and all.

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Performance Metrics (APM) for Spring Boot Microservices on OpenShift

Performance Metrics (APM) for Spring Boot Microservices on OpenShift

OpenShift provides a built-in monitoring tool called Hawkular. That tool is in charge of collecting metrics from Docker containers through the Kubernetes interface and storing, aggregating, and visualizing them. The metrics collected are CPU, Memory, Disk, and Network usage. Hawkular offers a “black-box” view of container performance but does not deal with application metrics like service performance or distribution of response time through application layers. For this specific case, the Hawkular community is working on another module called Hawkular APM that provides insight into the way an application executes across multiple (micro) services in a distributed (e.g. cloud) environment.

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Goodbye OpenShift All-In-One VM, Hello Minishift

Goodbye OpenShift All-In-One VM, Hello Minishift

After almost 100,000 downloads, the time has come to retire the OpenShift All-In-One VM. The intent of the VM was to give developers a simple and easy way to bring up OpenShift on their local machine for development purposes. In the meantime, there was movement within the Kubernetes community to create MiniKube – a means to run a Kubernetes “cluster” on your local machine. Jimmi Dyson saw this work and started MiniShift which built off MiniKube except for OpenShift. It fulfills all the original use cases we had for the All-In-One with the added bonus of actually having an engineering team maintaining it!

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Using Dynamic Provisioning and StorageClasses

Using Dynamic Provisioning and StorageClasses

OpenShift can integrate with underlying infrastructure, enabling OpenShift to dynamically interact with infrastructure and extend its functionality. Specifically, this can allow us to set up OpenShift to process a PersistentVolumeClaim and then allocate that storage dynamically.

I am going to cover what is needed to get started with dynamically provisioning storage, including cloud provider configuration, StorageClasses, and the Default StorageClass.

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Intro to Machine Learning using Tensorflow – Part 1

Intro to Machine Learning using Tensorflow - Part 1

Tensorflow is an open-source software library created by Google for Machine Intelligence. And Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text with others. Throughout this series, we’ll be using these two applications primarily, but we’ll also venture into other popular frameworks as well. By the end of this post, you’ll be able to run a linear regression (the “hello world” of ML) inside a container you built running in a cloud.

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OpenShift Commons Briefing #58: Open Source Application Segmentation with Aporeto’s Trireme

OpenShift Commons Briefing #58: Open Source Application Segmentation with Aporeto's Trireme

In this briefing, Dimitri Stiliadis, CEO, Co-Founder of Aporeto gives an introduction to the Trireme open source project. Trireme takes a different approach to application segmentation by treating the problem as what it is: an authentication and authorization problem. Every application component, such as process, a container, a Kubernetes POD, has an identity. A segmentation function is a simple policy that defines identities of the endpoints that are allowed to communicate with each other.

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OpenShift Container Platform Reference Architecture Implementation Guides

OpenShift Container Platform Reference Architecture Implementation Guides

We’ve got a design for your next cloud-based container deployment.

An inordinate amount of time can be spent researching and debating architectural decisions, tooling, parameters, or a required sequence of tasks when trying to deploy a project to the cloud. Start your project on the right foot and take advantage of the Red Hat OpenShift Container Platform Reference Architecture implementation guides!

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