#MiniKube

[johngai]

Running Local Docker Images in Minikube: A Quick Guide

Minikube allows you to run a single-node Kubernetes cluster locally, making it ideal for testing and development. However, Kubernetes typically pulls images from remote registries, which can be cumbersome when working with local Docker images. This guide explores two efficient methods to use your local Docker images within a Minikube cluster. Method 1: Load Local Docker Images into Minikube If…

[dgruploads]

AWS EKS | Episode 12 | Minikube and Kubectl | Introduction | Setup | hands-on demo

[minhphong306]

[TIL] Fix lỗi khi chạy minikube: Error making standard the default storage class: Error listing StorageClasses

Mình chạy minikube trên Fedora, gặp lỗi này cứ lay hoay mãi Continue reading [TIL] Fix lỗi khi chạy minikube: Error making standard the default storage class: Error listing StorageClasses

[nile-bits]

Jenkins on Kubernetes: Complete Setup and Configuration

Putting Jenkins on Kubernetes may be a game-changer for any development team looking to maximize their pipeline for continuous integration and delivery. Jenkins can manage several builds and tests in parallel by utilizing Kubernetes’ scalability and flexibility, which lowers bottlenecks and speeds up deployment times. You will be guided through every stage of the setup process by this all-inclusive guide, which will guarantee that your Jenkins setup on Kubernetes is reliable and effective...

Learn more here:

https://www.nilebits.com/blog/2024/07/jenkins-kubernetes-configuration/

[virtualizationhowto]

Docker Desktop Alternative: 5 Container Management Solutions Pros and Cons

Docker Desktop Alternative: 5 Container Management Solutions Pros and Cons - Learn about other solutions to managing containers on a desktop platform #docker #dockercontainers #modernapplications #kubernetes #minikube #podman #rancherdesktop #homelab

If you are like me and using containers for more and more solutions, both self-hosting and DevOps processes, I am always on the lookout for better tools and alternatives. Docker Desktop is a solution that many use to run Docker containers on a desktop environment like Windows clients. Let’s look at 5 container solutions that are Docker Desktop alternatives you should consider. Table of…

[kumpulanjurnalmakalah-blog]

[solved] minikube service --url command not working

[solved] minikube service --url command not working

When running a WordPress application on Minikube, the “minikube service –url” command is used to retrieve the URL for the WordPress service running in the cluster. This command is an essential part of the process for accessing the WordPress application, as it provides the application URL that users can use to interact with the application. However, there may be instances where the “minikube…

[jamercado]

Kubernetes: Control Plane and Workers

In Kubernetes, the control plane and worker nodes are two key components that together form the foundation of a Kubernetes cluster. They play distinct roles in managing and running containerized applications. Here’s an explanation of each component along with examples and YAML configurations where relevant: Control Plane The control plane is the brain of the Kubernetes cluster. It manages the…

[techdirectarchive]

(via How to Install and Use Minikube on a Linux System)

[coredgeblogs]

Getting Started with Kubeflow: Machine Learning on Kubernetes Made Easy

In today’s data-driven world, organizations are increasingly investing in scalable, reproducible, and automated machine learning (ML) workflows. But deploying ML models from research to production remains a complex, resource-intensive challenge. Enter Kubeflow, a powerful open-source platform designed to streamline machine learning operations (MLOps) on Kubernetes. Kubeflow abstracts much of the complexity involved in orchestrating ML workflows, bringing DevOps best practices to the ML lifecycle.

Whether you're a data scientist, ML engineer, or DevOps professional, this guide will help you understand Kubeflow’s architecture, key components, and how to get started.

What is Kubeflow?

Kubeflow is an end-to-end machine learning toolkit built on top of Kubernetes, the de facto container orchestration system. Originally developed by Google, Kubeflow was designed to support ML workflows that run on Kubernetes, making it easy to deploy scalable and portable ML pipelines.

At its core, Kubeflow offers a collection of interoperable components covering the full ML lifecycle:

Data exploration

Model training and tuning

Pipeline orchestration

Model serving

Monitoring and metadata tracking

By leveraging Kubernetes, Kubeflow ensures your ML workloads are portable, scalable, and cloud-agnostic.

Why Use Kubeflow?

Traditional ML workflows often involve disparate tools and manual handoffs, making them hard to scale, reproduce, or deploy. Kubeflow simplifies this by:

Standardizing ML workflows across teams

Automating pipeline execution and parameter tuning

Scaling training jobs dynamically on Kubernetes clusters

Monitoring model performance with integrated logging and metrics

Supporting hybrid and multi-cloud environments

Essentially, Kubeflow brings the principles of CI/CD and infrastructure-as-code into the ML domain—enabling robust MLOps.

Key Components of Kubeflow

Kubeflow’s modular architecture allows you to use only the components you need. Here are the most critical ones to know:

1. Kubeflow Pipelines

This is the heart of Kubeflow. It allows you to define, schedule, and monitor complex ML workflows as Directed Acyclic Graphs (DAGs). Pipelines support versioning, experiment tracking, and visualization of workflow runs.

2. Katib

An AutoML component that handles hyperparameter tuning using state-of-the-art algorithms like Bayesian optimization, grid search, and more. Katib can run large-scale tuning jobs across clusters.

3. KFServing (now KServe)

A robust model serving component for deploying trained models with support for REST/gRPC, autoscaling (including scale-to-zero), and multi-framework compatibility (TensorFlow, PyTorch, ONNX, XGBoost, etc.).

4. JupyterHub

Provides multi-user Jupyter notebooks directly within your Kubernetes environment. Great for data exploration, feature engineering, and prototyping.

5. ML Metadata (MLMD)

Tracks lineage and metadata about datasets, models, pipeline runs, and experiments, enabling reproducibility and governance.

Setting Up Kubeflow: A High-Level Walkthrough

Getting Kubeflow up and running can be daunting due to its complexity and the requirements of Kubernetes infrastructure. Here’s a high-level roadmap to guide your setup.

Step 1: Prepare Your Kubernetes Cluster

Kubeflow runs on Kubernetes, so you’ll need a Kubernetes cluster ready—either locally (via Minikube or KIND), on-premises, or in the cloud (GKE, EKS, AKS, etc.). Ensure you have:

Kubernetes ≥ v1.21

Sufficient CPU/memory resources

kubectl CLI configured

Step 2: Choose a Kubeflow Distribution

You can install Kubeflow using one of the following options:

Kubeflow Manifests: Official YAML manifests for production-grade installs

MiniKF: A local, single-node VM version ideal for development

Kfctl: Deprecated but still used in legacy environments

Kubeflow Operator: For declarative installs using CRDs

For most users, Kubeflow Manifests or MiniKF are the best starting points.

Step 3: Deploy Kubeflow

Assuming you’re using Kubeflow Manifests:

# Clone the manifests repo

git clone https://github.com/kubeflow/manifests.git

cd manifests

# Deploy using kustomize

while ! kustomize build example | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done

The installation process may take several minutes. Once complete, access the dashboard via a port-forward or ingress route.

The installation process may take several minutes. Once complete, access the dashboard via a port-forward or ingress route.

Step 4: Access the Kubeflow Central Dashboard

You can now access the Kubeflow UI, where you can create experiments, launch notebooks, manage pipelines, and deploy models—all from a unified interface.

Best Practices for Working with Kubeflow

To make the most of Kubeflow in production, consider the following:

Namespace Isolation: Use namespaces to separate teams and workflows securely.

Pipeline Versioning: Always version your pipeline components for reproducibility.

Storage Integration: Integrate with cloud-native or on-prem storage solutions (e.g., S3, GCS, NFS).

Security: Configure Role-Based Access Control (RBAC) and authentication using Istio and Dex.

Monitoring: Use Prometheus, Grafana, and ELK for observability and logging.

Common Challenges and How to Overcome Them

Kubeflow is powerful, but it comes with its share of complexity:

Challenge

Solution

Steep learning curve

Start with MiniKF or managed services like GCP Vertex AI Pipelines

Complex deployment

Use Helm charts or managed Kubernetes to abstract infra setup

RBAC and security

Leverage Kubeflow Profiles and Istio AuthPolicies for fine-grained control

Storage configuration

Use pre-integrated cloud-native storage classes or persistent volumes

Final Thoughts

Kubeflow brings enterprise-grade scalability, reproducibility, and automation to the machine learning lifecycle by marrying ML workflows with Kubernetes infrastructure. While it can be challenging to deploy and manage, the long-term benefits for production-grade MLOps are substantial.

For teams serious about operationalizing machine learning, Kubeflow is not just a tool—it’s a paradigm shift.

[sysadminxpert]

Create Kubernetes Pod with kubectl & YAML | Minikube Demo

���️ Create Pod using kubectl ✔️ Create Pod using YAML file 🔥 Dry-run validation trick 🔥 YAML formatting essentials ✔️ Real Minikube demo on Rocky Linux 9.5

[johngai]

Troubleshooting Kubernetes Service Exposure with Minikube

Introduction In the world of Kubernetes, managing and exposing services can sometimes be a challenging task, especially when dealing with deprecated commands and network configurations. Recently, I encountered an issue while following instructions from the book Kubernetes in Action. The command kubectl run kubia --image=luksa/kubia --port=8080 --generator=run/v1��did not work due to its…

[codezup]

Deploy Flask Apps to Production with Docker and Kubernetes

To deploy your Flask application using Docker and Kubernetes, follow this organized and step-by-step approach. This guide will walk you through containerizing your Flask app, setting it up in a Kubernetes cluster, and ensuring it’s production-ready. Step-by-Step Guide Prerequisites Install Docker, Minikube, and kubectl on your machine. Ensure you have a Flask application ready. Containerizing…

[hawkstack]

A Practical Guide to CKA/CKAD Preparation in 2025

The Certified Kubernetes Administrator (CKA) and Certified Kubernetes Application Developer (CKAD) certifications are highly sought-after credentials in the cloud-native ecosystem. These certifications validate your skills and knowledge in managing and developing applications on Kubernetes. This guide provides a practical roadmap for preparing for these exams in 2025.

1. Understand the Exam Objectives

CKA: Focuses on the skills required to administer a Kubernetes cluster. Key areas include cluster architecture, installation, configuration, networking, storage, security, and troubleshooting.

CKAD: Focuses on the skills required to design, build, and deploy cloud-native applications on Kubernetes. Key areas include application design, deployment, configuration, monitoring, and troubleshooting.

Refer to the official CNCF (Cloud Native Computing Foundation) websites for the latest exam curriculum and updates.

2. Build a Strong Foundation

Linux Fundamentals: A solid understanding of Linux command-line tools and concepts is essential for both exams.

Containerization Concepts: Learn about containerization technologies like Docker, including images, containers, and registries.

Kubernetes Fundamentals: Understand core Kubernetes concepts like pods, deployments, services, namespaces, and controllers.

3. Hands-on Practice is Key

Set up a Kubernetes Cluster: Use Minikube, Kind, or a cloud-based Kubernetes service to create a local or remote cluster for practice.

Practice with kubectl: Master the kubectl command-line tool, which is essential for interacting with Kubernetes clusters.

Solve Practice Exercises: Use online resources, practice exams, and mock tests to reinforce your learning and identify areas for improvement.

4. Utilize Effective Learning Resources

Official CNCF Documentation: The official Kubernetes documentation is a comprehensive resource for learning about Kubernetes concepts and features.

Online Courses: Platforms like Udemy, Coursera, and edX offer CKA/CKAD preparation courses with video lectures, hands-on labs, and practice exams.

Books and Study Guides: Several books and study guides are available to help you prepare for the exams.

Community Resources: Engage with the Kubernetes community through forums, Slack channels, and meetups to learn from others and get your questions answered.

5. Exam-Specific Tips

CKA:

Focus on cluster administration tasks like installation, upgrades, and troubleshooting.

Practice managing cluster resources, security, and networking.

Be comfortable with etcd and control plane components.

CKAD:

Focus on application development and deployment tasks.

Practice writing YAML manifests for Kubernetes resources.

Understand application lifecycle management and troubleshooting.

6. Time Management and Exam Strategy

Allocate Sufficient Time: Dedicate enough time for preparation, considering your current knowledge and experience.

Create a Study Plan: Develop a structured study plan with clear goals and timelines.

Practice Time Management: During practice exams, simulate the exam environment and practice managing your time effectively.

Familiarize Yourself with the Exam Environment: The CKA/CKAD exams are online, proctored exams with a command-line interface. Familiarize yourself with the exam environment and tools beforehand.

7. Stay Updated

Kubernetes is constantly evolving. Stay updated with the latest releases, features, and best practices.

Follow the CNCF and Kubernetes community for announcements and updates.

For more information www.hawkstack.com

[tutorialwithexample]

Master Kubernetes Basics: The Ultimate Beginner’s Tutorial

Kubernetes has become a buzzword in the world of containerized applications. But what exactly is Kubernetes, and how can beginners start using it? In simple terms, Kubernetes is a powerful open-source platform designed to manage and scale containerized applications effortlessly.

Why Learn Kubernetes? As businesses shift towards modern software development practices, Kubernetes simplifies the deployment, scaling, and management of applications. It ensures your apps run smoothly across multiple environments, whether in the cloud or on-premises.

How Does Kubernetes Work? Kubernetes organizes applications into containers and manages these containers using Pods. Pods are the smallest units in Kubernetes, where one or more containers work together. Kubernetes automates tasks like load balancing, scaling up or down based on traffic, and ensuring applications stay available even during failures.

Getting Started with Kubernetes

Understand the Basics: Learn about containers (like Docker), clusters, and nodes. These are the building blocks of Kubernetes.

Set Up a Kubernetes Environment: Use platforms like Minikube or Kubernetes on cloud providers like AWS or Google Cloud for practice.

Explore Key Concepts: Focus on terms like Pods, Deployments, Services, and ConfigMaps.

Experiment and Learn: Deploy sample applications to understand how Kubernetes works in action.

Kubernetes might seem complex initially, but with consistent practice, you'll master it. Ready to dive deeper into Kubernetes? Check out this detailed guide in the Kubernetes Tutorial.

[ericvanderburg]

Building a Sample Kubernetes Operator on Minikube: A Step-by-Step Guide

http://securitytc.com/THHGnP