Book Image

IoT Edge Computing with MicroK8s

By : Karthikeyan Shanmugam
Book Image

IoT Edge Computing with MicroK8s

By: Karthikeyan Shanmugam

Overview of this book

Are you facing challenges with developing, deploying, monitoring, clustering, storing, securing, and managing Kubernetes in production environments as you're not familiar with infrastructure technologies? MicroK8s - a zero-ops, lightweight, and CNCF-compliant Kubernetes with a small footprint is the apt solution for you. This book gets you up and running with production-grade, highly available (HA) Kubernetes clusters on MicroK8s using best practices and examples based on IoT and edge computing. Beginning with an introduction to Kubernetes, MicroK8s, and IoT and edge computing architectures, this book shows you how to install, deploy sample apps, and enable add-ons (like DNS and dashboard) on the MicroK8s platform. You’ll work with multi-node Kubernetes clusters on Raspberry Pi and networking plugins (such as Calico and Cilium) and implement service mesh, load balancing with MetalLB and Ingress, and AI/ML workloads on MicroK8s. You’ll also understand how to secure containers, monitor infrastructure and apps with Prometheus, Grafana, and the ELK stack, manage storage replication with OpenEBS, resist component failure using a HA cluster, and more, as well as take a sneak peek into future trends. By the end of this book, you’ll be able to use MicroK8 to build and implement scenarios for IoT and edge computing workloads in a production environment.
Table of Contents (24 chapters)
1
Part 1: Foundations of Kubernetes and MicroK8s
4
Part 2: Kubernetes as the Preferred Platform for IOT and Edge Computing
7
Part 3: Running Applications on MicroK8s
14
Part 4: Deploying and Managing Applications on MicroK8s
21
Frequently Asked Questions About MicroK8s

Accessing the Kubeflow dashboard

The Kubeflow dashboard gives you easy access to all the Kubeflow components installed on the cluster. Point your browser to http://10.64.140.43.nip.io (the URL that we set earlier) to be taken to the login screen, where we can input admin as the username and admin as the password (we set these components up previously).

The Welcome page should appear. Clicking Start Setup will lead you to the Create namespace screen. When you enter the namespace and click the Finish button, the dashboard will appear, as shown in the following screenshot:

Figure 9.17 – Kubeflow dashboard

Great! We have just installed Kubeflow.

Now that Kubeflow has been installed and is operational, let’s learn how to translate an ML model into a Kubeflow pipeline.