If you want to write Ansible playbooks, roles, modules, or collections, the first step is a good development environment.

This story was originally published at techbeatly.com.

The good news is that you have many options. Some are simple and run directly on your laptop. Others are better for teams or enterprise environments.

In this guide, we walk through practical ways to build an Ansible development and testing setup. These options work for Linux, macOS, and Windows users.

Many Windows users struggle to run Ansible locally. So we also cover the easiest ways for Windows users to build a working environment.

Let’s go through the options.

1. Local Linux or macOS Environment (Simple and Fast)

If you already use Linux or macOS, this is usually the easiest option.

You install Ansible and its tools directly on your system and start developing immediately.

This gives the best performance because everything runs locally.

Requirements

• Linux or macOS machine
• Python installed
• Ability to install packages (pip or system package manager)

Typical Setup

Install Ansible using pip:

python3 -m pip install --user ansible

Or use the package manager:

Ubuntu / Debian

sudo apt install ansible

Fedora

sudo dnf install ansible

Once installed, you can start writing playbooks, roles, and collections.

This setup works well if you already use Linux for daily work.

2. Virtual Machine Lab (Recommended for Windows Users)

If you use Windows or want a clean Linux environment, a virtual machine is a great choice.

You create a Linux VM and run Ansible inside it. Your laptop acts as the host, and the VM becomes your Ansible lab.

Popular VM tools:

• VirtualBox
• VMware Workstation
• Hyper-V

Typical Linux images used for Ansible labs:

• Ubuntu
• Fedora
• CentOS Stream
• Rocky Linux
• AlmaLinux

You can connect to the VM using:

• SSH
• VS Code Remote SSH
• Terminal

This approach keeps your host system clean and provides a consistent Linux environment.

Example Lab Guide

• Now Available: Updated Ansible Lab using Vagrant and VirtualBox!
• Setting up Lab Environment for Ansible | VirtualBox, Vagrant

3. Vagrant-Based Ansible Lab

If you regularly build and destroy labs, Vagrant is extremely useful.

Vagrant allows you to define your entire lab environment as code.

For example, you can automatically create:

• An Ansible control node
• Multiple managed nodes
• Preconfigured networking
• Preinstalled tools

With one command, the whole lab can start.

vagrant up

And you can destroy it just as easily.

vagrant destroy

This approach is great for:

• learning Ansible
• testing playbooks
• building repeatable labs
• training environments

Example setup:

• Now Available: Updated Ansible Lab using Vagrant and VirtualBox!

4. Windows Subsystem for Linux (WSL)

Ansible does not run natively on Windows, but Windows Subsystem for Linux (WSL) solves this problem by allowing you to run a full Linux environment directly inside Windows. From the user’s perspective, it behaves almost like a native Linux machine, which makes it a very practical way for Windows users to install and run Ansible without needing a full virtual machine.

Requirements

• Windows 10 or Windows 11
• WSL enabled
• A Linux distribution from the Microsoft Store

Common choices:

• Ubuntu
• Debian

Once installed, you can install Ansible normally inside WSL.

Example:

sudo apt update
sudo apt install ansible

Many developers combine WSL with VS Code for a very smooth workflow.

5. VS Code Remote Development

Another popular approach is to develop locally while running Ansible on a remote Linux server using the VS Code Remote SSH extension. In this setup, your code editor runs on your local machine, but the files and Ansible execution happen on the remote system, giving you the benefits of a powerful Linux environment without installing everything on your laptop.

Advantages:

• No need to install tools locally
• Works on Windows, Linux, and macOS
• Remote server provides consistent environment
• Easy collaboration for teams

This method is common in corporate environments where developers connect to shared Linux servers.

Detailed guide:

• Ansible and VSCode Remote Development

6. Cloud-Based Ansible Lab

If your laptop is limited or you want a disposable environment, a cloud lab is another good option.

You can create temporary infrastructure in a public cloud and run your Ansible control node there.

Typical setup:

• Ansible control node (EC2 instance or VM)
• Several test servers
• SSH connectivity

This lab can be destroyed after testing.

You can automate the entire lab creation using Terraform.

Example guides:

Create a free lab in public cloud:

• How to Create a FREE Ansible Lab in Public Cloud (AWS, GCP, Azure)
• Use Terraform to Create a FREE Ansible Lab in AWS

This approach is great for:

• testing infrastructure automation
• learning cloud automation
• CI/CD experiments

7. Container-Based Development (Advanced Option)

Another modern option is running your Ansible environment inside containers.

Tools often used:

• Podman
• Docker
• Dev Containers

This allows you to package the entire development environment as a container image.

Advantages:

• reproducible environment
• easy onboarding for teams
• same environment for everyone

Many teams use this method together with VS Code Dev Containers.

8. OpenShift Dev Spaces (Enterprise Teams)

Large teams often need a centralized and standardized development platform.

OpenShift Dev Spaces provides a web-based development environment running inside an OpenShift cluster.

The full development stack runs inside the cluster.

Developers only need:

• a browser
• access to the Dev Spaces environment

Benefits:

• identical environment for every developer
• no local setup required
• easier management for organizations
• integrated with enterprise security and identity systems

This option is common in large enterprise environments.

Final Thoughts

There is no single “best” Ansible development environment. The right choice depends on several factors including your operating system, available hardware resources, team requirements, and any enterprise policies you need to follow. What really matters is having a clean and repeatable environment where you can safely test playbooks and automation. Once that foundation is in place, developing Ansible content becomes much easier. If you are just starting out, the simplest approach is to use WSL or a small Linux virtual machine with Ansible installed, and later expand your setup into cloud labs, container-based environments, or enterprise platforms as your needs grow.

Best Ways to Set Up an Ansible Development Environment (Linux, Windows, macOS, and Windows WSL) was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

In every Kubernetes release cycle, the release candidates (RCs) play a big role. They help maintainers, contributors, platform engineers, and curious folks like us validate upcoming changes before the final release drops. Testing RC builds early helps catch regressions, check integrations, and update documentation or automation before production adopters hit the new version.

This article was originally published in techbeatly.

Kubernetes 1.35 is almost here, with general availability landing on 17 December 2025. The release cycle kicked off in mid-September and has already passed all major freeze milestones, and with the RC out now, it’s a good time to spin up a local cluster and try the new features before GA.

Prerequisites

• Minikube installed
• VirtualBox (or Podman/Docker)
• Basic Kubernetes knowledge
• Your user-installed minikube binary is located at: ~/.local/bin/minikube

Update Minikube First

Check the current version:

$ minikube update-check

If you see something like:

$ minikube update-check
CurrentVersion: v1.35.0
LatestVersion: v1.37.0

Update your user-local binary:

$ curl -Lo ~/.local/bin/minikube   https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
$ chmod +x ~/.local/bin/minikube

Verify:

$ minikube version

Start a Multi-Node Kubernetes v1.35.0-rc.0 Cluster (VirtualBox)

$ minikube start \
    --driver=podman \
    --nodes 3 \
    --cni calico \
    --cpus=2 \
    --memory=2g \
    --kubernetes-version=v1.35.0-rc.0 \
    --container-runtime=containerd \
    --profile k8s-1-35-rc

What these flags mean

• --nodes 3: three-node cluster, 1st node will become the controller/master node.
• --driver=podman: use Podman container as Kubernetes nodes. Use --driver=virtualbox if you want to use VirtualBox VMs as the backend
• --cni calico → networking via Calico
• --profile k8s-1-35-rc → keeps this cluster separate from others
• --kubernetes-version=v1.35.0-rc.0 → explicitly test the RC release

As Minikube starts, you’ll see logs confirming RC lookup, node creation, control plane boot, CNI setup, and worker node join workflow.

Verify the Cluster

$ kubectl get nodes
NAME              STATUS   ROLES           AGE     VERSION
k8s-1-35-rc       Ready    control-plane   2m34s   v1.35.0-rc.0
k8s-1-35-rc-m02   Ready    <none>          2m12s   v1.35.0-rc.0
k8s-1-35-rc-m03   Ready    <none>          117s    v1.35.0-rc.0

At this point you have a full multi-node Kubernetes v1.35 RC cluster running locally.

Managing Multiple Minikube Profiles

List all profiles:

$ minikube profile list

Switch profiles:

$ minikube profile k8s-1-35-rc
✅  minikube profile was successfully set to k8s-1-35-rc

Stop a cluster:

$ minikube stop --profile=k8s-1-35-rc

Start again if needed:

$ minikube start --profile=k8s-1-35-rc

Delete once testing finished

$ minikube delete --profile=k8s-1-35-rc

Wrap-up

You now have a repeatable way to test Kubernetes v1.35.0-rc.0 and other versions using Minikube, either with Podman or VirtualBox.This is the ideal setup for trying new features, checking compatibility, and preparing for the final GA release in December 2025.

This article was originally published in techbeatly.

Testing Kubernetes v1.35.0 with minikube: A Step-by-Step Guide for multi-node cluster was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Are you into Kubernetes, cloud native tech, or open source in general? Then mark your calendar: KubeCon + CloudNativeCon North America 2025 is happening from November 10–13, 2025 in Atlanta, Georgia — and it’s going to be big.

This article was originally published in techbeatly.

This is CNCF’s flagship event, bringing together adopters, contributors, and cloud native technologists from around the world. Whether you’re building platforms, running production workloads, contributing to open source, or just curious about what’s next — this is where it’s all happening.

Here’s what to expect:

• Deep Dives & Learning Tracks: Kubernetes, containers, security, observability, platform engineering, AI, and more — all covered in technical sessions, workshops, and hands-on demos.
• Networking & Hiring: Meet peers, project maintainers, hiring managers, and leaders from the top cloud native companies.
• Keynotes from Industry Leaders: Hear directly from the folks building and shaping the cloud native ecosystem.
• Project Updates: Get the latest from CNCF’s Graduated, Incubating, and Sandbox Projects.
• Co-Located Events: Dive into specialized topics with focused events running alongside the main conference.

Special Offer: 30% Off Current Pricing!

techbeatly has an exclusive 30% discount for you. Just use promo code: TECHBEATLY when registering.

Important: This discount expires on August 26, 2025 — don’t wait too long.

Register now and lock in your ticket at a lower price!

Why You Shouldn’t Miss This:

• Stay ahead in the cloud native space
• Learn directly from contributors and industry experts
• Build your network and meet potential collaborators or employers
• Experience the energy of the global open source community

Whether you’re just getting started or knee-deep in production, this is the one event that’ll keep you informed, inspired, and connected.

This article was originally published in techbeatly.

Save 30% on KubeCon + CloudNativeCon North America 2025 Tickets! was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Kubernetes has defined the standard for container orchestration, and it enables organizations to manage applications at scale. However, monitoring and observability become increasingly important as the cluster size grows, in order to ensure performance, security, and reliability.

This article was originally published in techbeatly.com.

The following is a discussion of some of the most popular and successful tools for Kubernetes monitoring and observability. This list is updated constantly to show you the most current tools, features, and best practices — putting you on top in 2025 and beyond.

Metrics & Dashboards

Prometheus

Prometheus is the de facto open-source monitoring system in the Kubernetes ecosystem. It collects time-series metrics using a pull-based model and supports a powerful query language, PromQL. It’s often paired with tools like Grafana for visualization.

Highlights:

• Native Kubernetes integration.
• Huge ecosystem of exporters.
• Easily integrates with alerting tools.

Grafana

Grafana is an open-source visualization platform that turns your metrics into beautiful, customizable dashboards. It’s not just for Prometheus — you can plug in Elasticsearch, Loki, Tempo, and many others.

Highlights:

• User-friendly dashboards.
• Rich plugin ecosystem.
• Supports multiple data sources.

Tigera Calico (Enterprise Edition)

While Calico is widely known as a CNI for Kubernetes networking, the Tigera Calico Enterprise edition brings deep observability, troubleshooting, and security analytics. It leverages eBPF technology to provide flow logs, DNS logs, and security posture insights.

Highlights:

• Zero-trust workload security.
• Real-time Kubernetes service and network observability.
• Compliance reporting and forensics.

Security & Compliance Observability

Sysdig

Sysdig provides container and Kubernetes security with built-in monitoring and observability. Using eBPF, it gives runtime security, network visibility, and detailed forensics.

Highlights:

• Runtime security and compliance.
• Kubernetes-native troubleshooting.
• SaaS and self-managed options.

OpenCost

OpenCost is an open-source project for monitoring and allocating Kubernetes resource costs. It helps teams track spend by namespace, workload, and labels.

Highlights:

• Cost allocation at Kubernetes granularity.
• Can integrate with Prometheus and Grafana.

Commercial Monitoring Suites

Datadog

Datadog offers a cloud-native observability platform with Kubernetes support. It provides metrics, traces, logs, and security features, along with AI-powered alerts.

Highlights:

• Full-stack observability.
• Cloud integrations (AWS, Azure, GCP).
• Out-of-the-box Kubernetes dashboards.

New Relic

New Relic delivers observability for Kubernetes clusters, offering deep insights into application, infrastructure, and network performance.

Highlights:

• Single-pane-of-glass observability.
• AIOps and anomaly detection.
• Broad integrations.

Dynatrace

Dynatrace uses its OneAgent for automated discovery and monitoring of Kubernetes clusters. It also offers advanced Davis AI engine for proactive problem detection.

Highlights:

• Automatic observability without manual configuration.
• Advanced root cause analysis.
• Supports OpenTelemetry.

AppDynamics

Part of Cisco, AppDynamics offers Kubernetes observability focusing on business metrics and application performance monitoring (APM).

Highlights:

• Business transaction tracing.
• Kubernetes-aware application performance dashboards.
• AI-powered analytics.

Kubernetes-native Tools

Kubernetes Dashboard

The Kubernetes Dashboard is an official web UI that allows you to monitor and manage workloads, services, and cluster resources.

Highlights:

• Quick access to workloads and health.
• User-friendly cluster management.
• Extendable with plugins.

Sensu

Sensu is an open-source observability pipeline that supports Kubernetes and cloud-native environments. It excels in event-driven automation and checks.

Highlights:

• Flexible checks and handlers.
• Scalable agent-based model.
• Integrates with Prometheus, Grafana, and others.

Lens IDE

Lens is a Kubernetes desktop IDE that brings together observability, management, and troubleshooting into a single application. It supports multiple clusters, role-based access, and community plugins.

Highlights:

• Local desktop UI.
• Cluster metrics and dashboards.
• Supports plugins for extended observability.

Conclusion

Kubernetes observability is more than metrics — it’s about having the right insights, security, troubleshooting tools, and even cost control to keep your clusters healthy and your teams productive.

Depending on your needs, you might combine open-source tools like Prometheus and Grafana with commercial offerings like Sysdig or Datadog.
Tools like Tigera Calico Enterprise help bridge the gap between observability and security, while Lens IDE makes cluster management accessible even to beginners.

Suggested next steps:

• For metrics only? → Prometheus + Grafana.
• For all-in-one SaaS observability? → Dynatrace or Datadog.
• For security-first? → Sysdig or Tigera Calico.
• For lightweight UI? → Lens or Kubernetes Dashboard.

This article was originally published in techbeatly.com.

Best Kubernetes Monitoring & Observability Tools (Updated) was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Kubernetes has defined the standard for container orchestration, and it enables organizations to manage applications at scale. However, monitoring and observability become increasingly important as the cluster size grows, in order to ensure performance, security, and reliability.

This article was originally published in techbeatly.

The following is a discussion of some of the most popular and successful tools for Kubernetes monitoring and observability. This list is updated constantly to show you the most current tools, features, and best practices — putting you on top in 2025 and beyond.

Metrics & Dashboards

Prometheus

Prometheus is the de facto open-source monitoring system in the Kubernetes ecosystem. It collects time-series metrics using a pull-based model and supports a powerful query language, PromQL. It’s often paired with tools like Grafana for visualization.

Highlights:

• Native Kubernetes integration.
• Huge ecosystem of exporters.
• Easily integrates with alerting tools.

Grafana

Grafana is an open-source visualization platform that turns your metrics into beautiful, customizable dashboards. It’s not just for Prometheus — you can plug in Elasticsearch, Loki, Tempo, and many others.

Highlights:

• User-friendly dashboards.
• Rich plugin ecosystem.
• Supports multiple data sources.

Tigera Calico (Enterprise Edition)

While Calico is widely known as a CNI for Kubernetes networking, the Tigera Calico Enterprise edition brings deep observability, troubleshooting, and security analytics. It leverages eBPF technology to provide flow logs, DNS logs, and security posture insights.

Highlights:

• Zero-trust workload security.
• Real-time Kubernetes service and network observability.
• Compliance reporting and forensics.

Security & Compliance Observability

Sysdig

Sysdig provides container and Kubernetes security with built-in monitoring and observability. Using eBPF, it gives runtime security, network visibility, and detailed forensics.

Highlights:

• Runtime security and compliance.
• Kubernetes-native troubleshooting.
• SaaS and self-managed options.

OpenCost

OpenCost is an open-source project for monitoring and allocating Kubernetes resource costs. It helps teams track spend by namespace, workload, and labels.

Highlights:

• Cost allocation at Kubernetes granularity.
• Can integrate with Prometheus and Grafana.

Commercial Monitoring Suites

Datadog

Datadog offers a cloud-native observability platform with Kubernetes support. It provides metrics, traces, logs, and security features, along with AI-powered alerts.

Highlights:

• Full-stack observability.
• Cloud integrations (AWS, Azure, GCP).
• Out-of-the-box Kubernetes dashboards.

New Relic

New Relic delivers observability for Kubernetes clusters, offering deep insights into application, infrastructure, and network performance.

Highlights:

• Single-pane-of-glass observability.
• AIOps and anomaly detection.
• Broad integrations.

Dynatrace

Dynatrace uses its OneAgent for automated discovery and monitoring of Kubernetes clusters. It also offers advanced Davis AI engine for proactive problem detection.

Highlights:

• Automatic observability without manual configuration.
• Advanced root cause analysis.
• Supports OpenTelemetry.

AppDynamics

Part of Cisco, AppDynamics offers Kubernetes observability focusing on business metrics and application performance monitoring (APM).

Highlights:

• Business transaction tracing.
• Kubernetes-aware application performance dashboards.
• AI-powered analytics.

Kubernetes-native Tools

Kubernetes Dashboard

The Kubernetes Dashboard is an official web UI that allows you to monitor and manage workloads, services, and cluster resources.

Highlights:

• Quick access to workloads and health.
• User-friendly cluster management.
• Extendable with plugins.

Sensu

Sensu is an open-source observability pipeline that supports Kubernetes and cloud-native environments. It excels in event-driven automation and checks.

Highlights:

• Flexible checks and handlers.
• Scalable agent-based model.
• Integrates with Prometheus, Grafana, and others.

Lens IDE

Lens is a Kubernetes desktop IDE that brings together observability, management, and troubleshooting into a single application. It supports multiple clusters, role-based access, and community plugins.

Highlights:

• Local desktop UI.
• Cluster metrics and dashboards.
• Supports plugins for extended observability.

Conclusion

Kubernetes observability is more than metrics — it’s about having the right insights, security, troubleshooting tools, and even cost control to keep your clusters healthy and your teams productive.

Depending on your needs, you might combine open-source tools like Prometheus and Grafana with commercial offerings like Sysdig or Datadog.
Tools like Tigera Calico Enterprise help bridge the gap between observability and security, while Lens IDE makes cluster management accessible even to beginners.

Suggested next steps:

• For metrics only? → Prometheus + Grafana.
• For all-in-one SaaS observability? → Dynatrace or Datadog.
• For security-first? → Sysdig or Tigera Calico.
• For lightweight UI? → Lens or Kubernetes Dashboard.

This article was originally published in techbeatly.

Best Kubernetes Monitoring & Observability Tools (Updated) was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Luca Berton is a known personality in the community for his work on Ansible. He has written many best-selling books, such as Ansible for VMware by Examples, Ansible for Kubernetes by Examples, Red Hat Ansible Automation Platform, and Hands-on Ansible Automation. His experience in automation and Red Hat technologies is well reflected in this book.

This article was originally published in techbeatly

The book introduces the reader to the value of RHCE, exam methods, and curriculum, thus encouraging individuals to work more sincerely in the IT world. Although I always refer them to the official Red Hat training for their Red Hat certification exams, reading this book will definitely boost their confidence for the RHCE exam. Luca, in addition, gives a clear outline of different types of certifications with Red Hat and career tracks, which is useful for anyone thinking of pursuing a Red Hat learning path.

It covers some of the essential basics of Red Hat Enterprise Linux (RHEL), taking concepts from the core and then gradually building toward system administration, which was enjoyable. Chapters regarding logging and storage are really nicely structured and written.

The actual RHCE material starts with automation. Luca does a great job introducing Ansible, covering all the basic concepts along with practical guides. The book proceeds with writing Ansible playbooks and small automation tasks for Linux as well as Windows environments. The sections on Ansible continue with managing networks, storage, files, services, security, and more, all for automation-related tasks.

One of the best chapters of the book is the troubleshooting and performance optimization. These are important topics for any RHCE candidate, and Luca explains them in a way that is easy to understand and apply.

A key component of RHCE is its hands-on, performance-based testing. This book includes lab exercises that should not be skipped. Completing the labs based on the guides provided many times will significantly enhance the chances of success in the exam.

Big thanks to Luca Berton for creating such an excellent resource for the learning community. Thanks to Tushar and BPB Publications for this book copy.

Highly recommended for anyone preparing for RHCE!

Get your copy

This article was originally published in techbeatly

Book Review: Mastering the Red Hat Certified Engineer (RHCE) Exam was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Originally published in techbeatly

Also, check KubeCon + CloudNativeCon India 2024 details.

Whether you’re a developer, engineer, or simply curious about the latest trends in cloud native computing, this is an event you won’t want to miss. With a diverse range of CNCF Graduated and Incubating Projects, this conference offers everything you need to level up your knowledge and skills.

Here’s what you can expect:

• Educational Sessions: Learn from experts in Kubernetes, containers, security, and the latest cloud native innovations.
• Networking: Connect with industry leaders, tech professionals, and companies hiring in the cloud native space.
• Keynotes: Hear from top voices shaping the future of cloud native computing.
• Co-Located Events: Attend sessions on specialized topics like AI, Platform Engineering, and more.

Save Big with techbeatly‘s Exclusive Promo Codes!

If you missed the standard pricing deadline, don’t worry! We have some exciting news for you. Discounted rates are still available for the standard tier pricing. Use the following promo codes to get up to $430 off your registration:

All-Access Pass:

• Individual, All-Access: $899 (originally $1079) — Use code: TECHBEATLY_AAI
• Corporate, All-Access: $1,549 (originally $1979) — Use code: TECHBEATLY_AAC

KubeCon + CloudNativeCon Only Pass:

• Individual, KubeCon Only: $600 (originally $700) — Use code: TECHBEATLY_I
• Corporate, KubeCon Only: $1,250 (originally $1600) — Use code: TECHBEATLY_C

👉 Register here and secure your spot at KubeCon + CloudNativeCon North America 2024 before prices go up!

Why You Should Attend:

• Stay Ahead: Discover the latest trends and technologies in cloud native, Kubernetes, and open source.
• Unparalleled Learning: Attend hands-on sessions, gain insights, and deepen your expertise.
• Networking Opportunities: Meet peers, industry experts, and potential employers in the thriving cloud native ecosystem.

This is your chance to be part of the future of cloud native computing. Don’t miss out on this incredible event — get your tickets now using our special promo codes.

Originally published in techbeatly

Promo: Get Up to $430 Off on KubeCon + CloudNativeCon North America 2024 Tickets was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article was originally published in techbeatly.

What’s New in Kubernetes 1.31

Kubernetes 1.31 introduces several new features and improvements:

• Enhanced security with stricter default settings.
• Improved scalability and performance optimizations.
• New APIs and extended support for existing ones.
• Simplified network policy management.
• Better support for edge computing and IoT scenarios.

For a detailed overview, check out the Kubernetes 1.31 release notes.

Prerequisites

• minikube is installed on your machine.
• VirtualBox as the driver. (You can use Docker or Podman as alternatives)
• Basic understanding of Kubernetes and command-line interface.

Setting Up minikube with Kubernetes 1.31

First, ensure that you have the latest version of Minikube. Update it if necessary:

$ minikube updatep-check

Then, start your minikube cluster with the following command:

$ minikube start \
  --driver=virtualbox \
  --nodes 3 \
  --cni calico \
  --cpus=2 \
  --memory=2g \
  --kubernetes-version=v1.31.0 \
  --container-runtime=containerd \
  --profile=k8s-1-31

Here’s a breakdown of the command:

• --driver=virtualbox: Specifies VirtualBox as the driver.
• --nodes 3: Creates a 3-node cluster.
• --cni calico: Uses Calico for the Container Network Interface.
• --cpus=2: Allocates 2 CPUs to each node.
• --memory=2g: Allocates 2GB of RAM to each node.
• --kubernetes-version=v1.31.0: Specifies Kubernetes version 1.31.0.
• --container-runtime=containerd: Uses containerd as the container runtime.
• --profile=k8s-1-31: Give each cluster a unique name (to avoid disturbing other minikube clusters if you have any)

You will see the logs from minikube, where you can see the Kubernetes cluster nodes are created one by one.

😄  [k8s-1-31] minikube v1.33.1 on Fedora 40
❗  Specified Kubernetes version 1.31.0 is newer than the newest supported version: v1.30.0. Use `minikube config defaults kubernetes-version` for details.
❗  Specified Kubernetes version 1.31.0 not found in Kubernetes version list
🤔  Searching the internet for Kubernetes version...
✅  Kubernetes version 1.31.0 found in GitHub version list
✨  Using the virtualbox driver based on user configuration
👍  Starting "k8s-1-31" primary control-plane node in "k8s-1-31" cluster
🔥  Creating virtualbox VM (CPUs=2, Memory=2048MB, Disk=20000MB) ...
📦  Preparing Kubernetes v1.31.0 on containerd 1.7.15 ...
    ▪ Generating certificates and keys ...
    ▪ Booting up control plane ...
    ▪ Configuring RBAC rules ...
🔗  Configuring Calico (Container Networking Interface) ...
    ▪ Using image gcr.io/k8s-minikube/storage-provisioner:v5
🌟  Enabled addons: storage-provisioner, default-storageclass
🔎  Verifying Kubernetes components...
👍  Starting "k8s-1-31-m02" worker node in "k8s-1-31" cluster
...<removed for brevity>...
👍  Starting "k8s-1-31-m03" worker node in "k8s-1-31" cluster
...<removed for brevity>...
🏄  Done! kubectl is now configured to use "k8s-1-31" cluster and "default" namespace by default

Verifying the brand-new Kubernetes 1.31 cluster

After starting minikube, verify the status of your cluster:

$ kubectl get nodes
NAME           STATUS     ROLES           AGE     VERSION
k8s-1-31       Ready      control-plane   2m27s   v1.31.0
k8s-1-31-m02   Ready      <none>          86s     v1.31.0
k8s-1-31-m03   NotReady   <none>          28s     v1.31.0

You should see three nodes listed, each in the Ready state. This confirms that your 3-node cluster is up and running with Kubernetes 1.31.

Once you finish testing, you stop or delete the minikube cluster by mentioning the correct minikube profile as follows.

$ minikube stop --profile k8s-1-31
✋ Stopping node "k8s-1-31-m03" …
✋ Stopping node "k8s-1-31-m02" …
✋ Stopping node "k8s-1-31" …
🛑 3 nodes stopped.

Congratulations! You’ve successfully set up and tested Kubernetes 1.31 with minikube and VirtuBox on a 3-node cluster.

Let us set up a multi-node Kubernetes cluster with Podman and minikube

If you prefer Podman or Docker as the driver for minikube, then it is simple as follows.

$ minikube start \
    --driver=podman \
    --nodes 3 \
    --cni calico \
    --cpus=2 \
    --memory=2g \
    --kubernetes-version=v1.31.0 \
    --container-runtime=containerd \
    --profile k8s-1-31-podman

Please note, I have used –driver=podman and a new minikube profile (--profile k8s-1-31-podman) for my new cluster.

If I check my nodes now, I can see the nodes are running as follows.

$ kubectl get nodes
NAME                  STATUS   ROLES           AGE     VERSION
k8s-1-31-podman       Ready    control-plane   2m34s   v1.31.0
k8s-1-31-podman-m02   Ready    <none>          2m16s   v1.31.0
k8s-1-31-podman-m03   Ready    <none>          2m3s    v1.31.0

Since you used different profiles for different clusters, you can always switch back to the other minikube cluster anytime.

Let us list the cluster profiles.

$ minikube profile list
|-----------------|------------|------------|----------------|------|---------|---------|-------|----------------|--------------------|
|     Profile     | VM Driver  |  Runtime   |       IP       | Port | Version | Status  | Nodes | Active Profile | Active Kubecontext |
|-----------------|------------|------------|----------------|------|---------|---------|-------|----------------|--------------------|
| cluster2-podman | podman     | containerd |                | 8443 | v1.30.0 | Unknown |     1 |                |                    |
| k8s-1-31        | virtualbox | containerd | 192.168.59.176 | 8443 | v1.31.0 | Stopped |     3 |                |                    |
| k8s-1-31-podman | podman     | containerd | 10.88.0.4      | 8443 | v1.31.0 | Running |     3 | *              | *                  |
|-----------------|------------|------------|----------------|------|---------|---------|-------|----------------|--------------------|

If I want to switch to my minikube + VirtualBox cluster based on Kubernetes 1.31, I can use the following command.

$ minikube profile k8s-1-31
✅  minikube profile was successfully set to k8s-1-31

Also, remember to start/stop/delete unused minikube profiles and clusters to save your computing resources.

$ minikube stop --profile=k8s-1-31-podman

# Switch to other minikube profile
$ minikube profile k8s-1-31
✅  minikube profile was successfully set to k8s-1-31

# Start the cluster if its stopped; ignore otherwise.
$ minikube start --profile k8s-1-31

# Check cluster nodes
$ $  kubectl get nodes
NAME           STATUS   ROLES           AGE   VERSION
k8s-1-31       Ready    control-plane   17m   v1.31.0
k8s-1-31-m02   Ready    <none>          64s   v1.31.0
k8s-1-31-m03   Ready    <none>          22s   v1.31.0

These environments are perfect for experimenting with the latest features and enhancements in Kubernetes 1.31.

This article was originally published in techbeatly.

Testing Kubernetes 1.31 with minikube: A Step-by-Step Guide for multi-node cluster was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Having previously enjoyed the authors’ “Machine Learning on Kubernetes,” I dove into “MLOps with Red Hat OpenShift” with high expectations, and it certainly delivered. Ross Brigoli and Faisal Masood offer a comprehensive guide that expertly bridges the gap between Machine Learning (ML) and Operations (Ops) within the OpenShift platform.

This article was originally published in techbeatly

Strengths:

• Solid Foundation: The book starts with a clear introduction to MLOps concepts, ensuring readers with even limited ML experience can grasp the fundamentals.
• OpenShift Focus: The chapters specifically delve into how to leverage OpenShift AI (formerly Red Hat OpenShift Data Science — RHODS) on various platforms like Red Hat OpenShift Service on AWS (ROSA). This targeted approach is valuable for those committed to the OpenShift ecosystem.
• Unique Insights: The content on GPU configurations is particularly noteworthy, addressing a topic often underrepresented in online resources.
• Practical Solutions: My existing doubts regarding ML model training, deployment, and operation were effectively addressed, providing a clear path to implementation.

Target Audience:

This book is ideal for anyone seeking a structured approach to kickstarting their Machine Learning journey with OpenShift. It caters well to developers, data scientists, and DevOps professionals looking to integrate MLOps practices into their OpenShift environment.

Overall Impression:

“MLOps with Red Hat OpenShift” lives up to its predecessor’s quality. It is a well-structured, informative resource that empowers readers to confidently navigate the complexities of deploying and managing ML models within OpenShift. My thanks to the authors for creating such valuable content and to Vinishka and Packt Publishing for providing the review copy.

This article was originally published in techbeatly

Book Review: MLOps with Red Hat OpenShift was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article was originally published in techbeatly.

Have you ever received a one-word message like “Hi” in a group chat or forum? You reply, eager to connect, but then… silence. Frustrating, right?

A Typical Conversation

Someone in the Ansible group: “Does anyone know about the Ansible Jinja2 template?”

(Me to myself: What the….! The group name is Ansible, what else we are discussing here then?)

Me/other group members: “Please ask your question ?” (See, still we have patience and we asked nicely)

The same someone: “Anyone can help me on Jinja2 loops?” (Again!!!)

Me/other group members: “Please post your question here!”

Do you think this is effective communication?

Let Us Try Something Better

What if the person asks the question as follows?

Hello, my name is John. I am trying to develop a Jinja2 template in which I want to loop multiple dictionary values (as given in the following screenshots). I facing some issues when I use the dictionary values and I am not getting the correct output. Please see the playbook, template and sample variables below. Could anyone assist with this?”

Wow! If I read that chat, I will spend 5 minutes reading and understanding the issue and reply to whatever I know!

Everyone saves the time!

Asking Technical Questions — Best Practices

1. Avoid These Questions

Instead of openers like “Can anyone help with X?” or “Anyone here with Y experience?”, get straight to the point. Briefly describe your issue and the technology involved. Try to avoid the following introductions and questions as those are a waste of time, be straight to the point.

• “Can anyone help with the ABC issue?”
• “Anyone here with XYZ experience?”
• “Anyone worked on DEF stack?”
• “Hello, how are you?”
• “Any Kubernetes experts here?”

Example (Good): “Hello, I’m working on an Ansible playbook that uses Jinja2 templates. I’m facing trouble iterating through dictionary values. Could someone advise on the best approach?”

2. Search First, Ask Later

Before posting, utilize search engines like Google to see if your question has already been answered. Learning to find solutions independently is a valuable skill!.

3. Mind Your Language; Use Appropriate Pronouns

Avoid casual greetings like “Bro” or “Sis” unless you know the community culture.

4. Use Plain English

Use plain English for broad understanding. Local languages are okay if the group caters to a specific region.

5. Descriptive Titles

If possible, add a title to your question that summarizes your problem. This helps others understand your issue and saves everyone time.

Example (Good): “How to Install Ansible Automation Platform in a Disconnected Environment?”

6. Clarity Is Key; Phrase Your Questions Clearly

Formulate your question clearly and read it before posting for any inconsistencies. Include essential details about your setup (e.g., operating system, version) before or after the question itself.

Hello, this is Sarah. I have deployed the Kubernetes cluster on my laptop (Fedora 35) using VirtualBox and Vagrant. I can spin up the VMs and clusters but I am not able to connect to the cluster API from my host machine (which is my laptop). I have googled and tried some configurations but nothing helped to solve the issue.

Below is my Vagranfile and the guide/document I followed to deploy the Kubernetes cluster. Also, see the error message (screenshot) shared for reference. Could you please help with this?

For the best result,

• Avoid too much jargon as the group members may not be aware of such.
• Read the question carefully before you send it.
• Make sure the details are included in the question — before or after the message.

7. Show Your Efforts; Include The Reference Links

If you’ve already tried troubleshooting steps, mention them. This avoids duplicate suggestions and saves everyone time. If you followed a guide or encountered issues at a specific step, mentioning it helps others pinpoint the problem.

8. Code Snippets

• Code: Include relevant code snippets, playbooks, configurations, or error messages.
• Formatting: When using messaging platforms like WhatsApp or Telegram, ensure proper formatting for mobile readability.

9. Screenshot Or Outputs — Mandatory

Cannot copy-paste the code snippets or error messages? Take a screenshot and post it! DO NOT crop the screenshots so tight as the reading will not be easy on mobile devices. Also, make sure the screenshots are clear enough for the readers to read and understand.

Warning: Remember to mask the text if there is any sensitive information such as IP address, passwords or tokens.

10. Respect The Community And Privacy

When someone helps, continue the conversation publicly so others can benefit. Don’t direct message for help unless explicitly allowed.

11. Finally, Show Appreciation; Please Respond!

Respond to those who offer clarification or solutions. This lets them know their efforts were valuable.

12. Last But Not Least; Professional Branding

Use a clear and recognizable username across platforms for easy identification.

Takeaway

Instead of generic greetings, focus on providing clear and detailed information about your technical questions. This fosters a more productive and helpful online community experience for everyone.

Check some of the similar initiatives here.

• nohello.net
• dontasktoask.com
• stackoverflow.com/help/how-to-ask

This article was originally published in techbeatly.

How To Ask Technical Questions was originally published in techbeatly on Medium, where people are continuing the conversation by highlighting and responding to this story.