Highlights from the Links

  1. Kubernetes 1.10: Hidden Gems, Jetstack (https://blog.jetstack.io/blog/hidden-gems-1.10/)
    In the first release of 2018, Kubernetes 1.10 focuses on stabilizing features in storage, security, and networking. Matthew Bates (https://twitter.com/mattbates25) of Jetstack asked their team of engineers to share what 1.10 features they find particularly exciting. From Device Plugins and external custom metrics, to volume scheduling and local storage, check out which release gems you don’t want to miss.
  2. Creating Kubernetes Liveness and Readiness Probes, Opensource.com (https://opensource.com/article/18/3/kubernetes-liveness-readiness-probes)
    What happens if a container pod fails? In this article, Amjad Yaseen (https://twitter.com/ayaseen_) of STC Solutions explains how kubelet uses liveness probes to know when to restart a container and readiness probes to know when a container is ready to start accepting traffic. Learn how Kubernetes keeps your containers running by restarting them if they crash or if their liveness probes fail.
  3. Nvidia Embraces Kubernetes for Scalable Deep Learning, The New Stack (https://thenewstack.io/nvidia-embraces-kubernetes-for-scalable-deep-learning/)
    GPU manufacturer Nvidia has put the open source Kubernetes container orchestration engine at the center of its strategy to bring machine learning to the enterprise. In this article, Joab Jackson (https://twitter.com/Joab_Jackson) of The New Stack shares Nvidia’s recent demo of Kubernetes of Nvidia GPUs and their plans to contribute GPU-support code to Kubernetes.
  4. Six Tips for Running Scalable Workloads on Kubernetes, InfoQ (https://www.infoq.com/articles/tips-running-scalable-workloads-kubernetes)
    Running scalable workloads in Kubernetes is all about knowing what Kubernetes expects for workloads and how to make them as tolerant as possible. In this article, Joel Speed (https://twitter.com/joelaspeed) of Pusher explains how to ensure Kubernetes knows what is happening with your deployment including where to schedule it, knowing when it is ready to serve requests, and ensuring work is spread across as many nodes as possible.


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