How to Install CRD on Kubernetes: A Comprehensive Guide

Introduction

Custom Resource Definitions (CRDs) are a powerful feature in Kubernetes that allow you to extend the Kubernetes API to create your own custom resources. This guide will walk you through the process How to Install CRD on Kubernetes cluster, from basic concepts to advanced usage.

What is a CRD in Kubernetes?

A Custom Resource Definition (CRD) allows you to define custom resources in Kubernetes. These resources are extensions of the Kubernetes API that can be managed through kubectl and used alongside built-in resources like Pods and Services.

Why Use CRDs?

CRDs are essential for extending Kubernetes capabilities without modifying the core code. They enable you to create custom workflows and automate complex processes, enhancing the functionality of your Kubernetes environment.

Pre-requisites

Before installing CRDs, ensure you have the following:

  • A running Kubernetes cluster
  • kubectl configured to interact with your cluster
  • Basic understanding of Kubernetes concepts

Step-by-Step Guide: How to Install CRD on Kubernetes

Method 1: Using kubectl

This method involves creating and applying a CRD manifest using kubectl.

Create a CRD Manifest:

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
  name: myresources.mycompany.com
spec:
  group: mycompany.com
  versions:
    - name: v1
      served: true
      storage: true
      schema:
        openAPIV3Schema:
          type: object
          properties:
            spec:
              type: object
              properties:
                field1:
                  type: string
                field2:
                  type: integer
  scope: Namespaced
  names:
    plural: myresources
    singular: myresource
    kind: MyResource
    shortNames:
      - mr

Apply the CRD Manifest:

kubectl apply -f crd.yaml

Verify the CRD Installation:

kubectl get crds

Method 2: Using a Helm Chart

Helm charts can simplify the installation and management of CRDs.

  1. Create a Helm Chart Directory Structure:
    • helm create my-crd-chart
  2. Add CRD Manifest to the Helm Chart: Place your CRD manifest in the crds directory of your Helm chart.
  3. Install the Helm Chart:
    • helm install my-crd-release ./my-crd-chart
  4. Verify the Installation:
    • kubectl get crds

Advanced CRD Configurations

Versioning CRDs

Versioning allows you to manage multiple versions of your custom resources.

Add Version Information to the CRD:

versions:
  - name: v1
    served: true
    storage: true
  - name: v2
    served: true
    storage: false

Upgrade the CRD:

kubectl apply -f crd-v2.yaml

Validation with OpenAPIv3 Schemas

Schema validation ensures that custom resources conform to a specified structure.

Define an OpenAPIv3 Schema in the CRD:

schema:
  openAPIV3Schema:
    type: object
    properties:
      spec:
        type: object
        properties:
          field1:
            type: string
          field2:
            type: integer

Apply the Updated CRD:

kubectl apply -f crd-with-schema.yaml

Custom Controllers

Custom controllers automate the management of custom resources.

  1. Write a Custom Controller: Use a framework like Kubebuilder or Operator SDK to create a controller.
  2. Deploy the Controller:
    • kubectl apply -f controller-deployment.yaml
  3. Monitor the Controller:
    • kubectl logs -f deployment/my-controller

Troubleshooting CRDs

If your CRDs are not working as expected, follow these steps:

  1. Check CRD Status:
    • kubectl get crds
  2. Inspect Resource Instances:
    • kubectl get myresources.mycompany.com
  3. Review Controller Logs:
    • kubectl logs -f deployment/my-controller

Frequently Asked Questions

How do I update an existing CRD?

To update a CRD, modify the manifest, and apply it using kubectl apply -f crd.yaml

Can I delete a CRD?

Yes, you can delete a CRD using kubectl delete crd myresources.mycompany.com

How do I handle CRD versioning?

You can manage CRD versions by adding multiple versions in the CRD manifest and specifying which versions are served and stored.

Conclusion

Installing and managing CRDs on Kubernetes is a powerful way to extend the platform’s functionality. By following this comprehensive guide, you can create, configure, and troubleshoot CRDs effectively. Whether you are using Kubectl or Helm, this guide provides you with the necessary steps to enhance your Kubernetes environment with custom resources.

Enhance your Kubernetes capabilities today by installing CRDs and automating complex workflows with custom controllers. Thank you for reading the DevopsRoles page!

Step-by-Step Guide to Merging Feature Branches in Git From feature/xxx_2020 to develop/xxx

Introduction

Merging Feature Branches in Git, allowing developers to integrate changes from one branch into another. This process is essential for maintaining a clean and organized codebase, especially in collaborative projects where multiple developers work on different features simultaneously. In this guide, we will walk you through the step-by-step process of merging the feature/xxx_2020 branch into the develop/xxx branch. We will cover everything from setting up your working environment to resolving conflicts, ensuring that your merge is smooth and error-free. Whether you’re new to Git or looking to refine your merging skills, this guide has you covered.

Merging Feature Branches in Git From feature/xxx_2020 to develop/xxx

Step 1: Create a Working Directory

Before starting the merge process, it’s essential to create a separate working directory to prevent any unintended changes in your current workspace.

$ mkdir ${Working directory}
$ cd ${Working directory}

Step 2: Clone the Git Repository

Next, clone the Git repository to your local machine to ensure that you have the latest code base.

$ git clone https://domain.com/XXX/xxx.git
$ cd xxx

Step 3: Check the Remote and Fetch the Data

To ensure you are working with the most up-to-date branches, check the remote repository and fetch the latest data.

$ git remote show origin
$ git fetch origin

Step 4: Checkout the Merge Source Branch

Now, switch to the source branch feature/xxx_2020 that you plan to merge from.

$ git checkout feature/xxx_2020

Step 5: Perform a Fast-Forward Merge on the Source Branch

To ensure the source branch is up-to-date with its remote counterpart, perform a fast-forward merge.

$ git merge --ff origin/feature/xxx_2020

Step 6: Checkout the Merge Destination Branch

After updating the source branch, switch to the destination branch develop/xxx.

$ git checkout develop/xxx

Step 7: Perform a Fast-Forward Merge on the Destination Branch

Update the destination branch by performing a fast-forward merge to ensure it is current.

$ git merge --ff origin/develop/xxx

Step 8: Merge the Source Branch into the Destination Branch

Now, merge the feature/xxx_2020 branch into develop/xxx. Use the --no-commit and --no-ff options to ensure that you have control over the commit process and can handle any conflicts that arise.

$ git merge --no-commit --no-ff feature/xxx_2020

Step 9: Check Git Status for Conflicts

Before finalizing the merge, check the status of your Git repository to identify any conflicts that need to be resolved.

$ git status

Step 10: Resolve Merge Conflicts (If Any)

If there are conflicts, you’ll need to manually resolve them. Open the conflicted files, edit them as necessary, and then mark them as resolved.

Step 11: Commit the Merge with Conflict Resolution

Once all conflicts have been resolved, commit the merge with a detailed message describing the conflicts and how they were resolved.

$ git commit -F- <<EOM
>Merge branch 'feature/xxx_2020' into 'develop/xxx'
>Auto-merging src/main/java/H6789.java
>CONFLICT (content): Merge conflict in src/main/java/F6789.java
>Auto-merging src/main/etc/Test_message.properties
>EOM

Merge conflict resolution FAQs

What should I do if I encounter a merge conflict?

If you encounter a merge conflict, carefully review the conflicting files, resolve the conflicts, and then commit the changes. Use Git’s conflict markers (<<<<<<<, =======, >>>>>>>) to identify the differences between the branches.

Why should I use --no-commit and --no-ff during a merge?

Using --no-commit and --no-ff gives you more control over the merge process. It allows you to review the changes before finalizing the merge and ensures that a merge commit is created even if the merge could be resolved as a fast-forward.

How can I avoid merge conflicts?

To minimize the chances of conflicts, regularly pull the latest changes from the remote repository into your local branches and communicate with your team to coordinate when and how merges are performed.

Conclusion

Merging branches in Git is a critical skill for developers, particularly when working on collaborative projects. By following the steps outlined in this guide, you can successfully merge the feature/xxx_2020 branch into the develop/xxx branch, resolve any conflicts, and maintain a clean and functional codebase. Thank you for reading the DevopsRoles page!

References

How to Install CoreDNS on Kubernetes: A Comprehensive Guide

Introduction

CoreDNS is a flexible and extensible DNS server that can serve as the DNS server for Kubernetes clusters. As Kubernetes relies heavily on DNS for service discovery, having a robust DNS solution like CoreDNS is essential. This guide will walk you through the process of how to install CoreDNS on Kubernetes cluster, from basic setup to advanced configurations.

What is CoreDNS?

CoreDNS is a DNS server that is flexible, extensible, and written in Go. It is used for service discovery in Kubernetes clusters and can serve as the cluster DNS, allowing pods to resolve services by name.

Why Use CoreDNS in Kubernetes?

CoreDNS provides a more flexible and scalable DNS solution compared to traditional DNS servers. It is designed to work seamlessly with Kubernetes, offering features such as:

  • Service Discovery: Automatically resolves services within the cluster.
  • Scalability: Handles large-scale DNS queries efficiently.
  • Customization: Easily configurable through its Corefile.

Pre-requisites

Before installing CoreDNS, ensure you have the following:

  • A running Kubernetes cluster
  • kubectl configured to interact with your cluster
  • Basic understanding of Kubernetes and DNS

Step-by-Step Guide to Install CoreDNS on Kubernetes

Method 1: Using kubeadm

Kubeadm is a tool that simplifies the process of setting up a Kubernetes cluster. It can automatically deploy CoreDNS during the cluster setup.

  1. Initialize the Cluster:
    • kubeadm init --pod-network-cidr=10.244.0.0/16
  2. Install a Pod Network Add-on:
    • kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml
  3. Verify CoreDNS Deployment:
    • kubectl get pods -n kube-system -l k8s-app=kube-dns

Method 2: Installing Manually

If you are not using kubeadm, you can manually deploy CoreDNS using Kubernetes manifests.

  1. Download CoreDNS Deployment Manifests:
    • wget https://raw.githubusercontent.com/coredns/deployment/master/kubernetes/coredns.yaml.sed
  2. Apply the Manifests:
    • kubectl apply -f coredns.yaml.sed
  3. Verify Deployment:
    • kubectl get pods -n kube-system -l k8s-app=kube-dns

Advanced CoreDNS Configuration

Customizing the Corefile

The Corefile is the configuration file for CoreDNS, and it defines how DNS queries are handled.

Edit the Corefile ConfigMap:

kubectl -n kube-system edit configmap coredns

Add Custom Entries:

.:53 {
    errors
    health
    kubernetes cluster.local in-addr.arpa ip6.arpa {
        pods insecure
        fallthrough in-addr.arpa ip6.arpa
    }
    prometheus :9153
    forward . /etc/resolv.conf
    cache 30
    loop
    reload
    loadbalance
}

Integrating with External DNS

To forward queries to external DNS servers, modify the forward directive in the Corefile.

  1. Update the Corefile:
    • forward . 8.8.8.8 8.8.4.4
  2. Apply the Changes:
    • kubectl -n kube-system rollout restart deployment coredns

Enabling Logging

Enable logging for debugging purposes.

Add the Log Plugin to the Corefile:

.:53 {
    errors
    log
    ...
}

Apply the Changes: kubectl -n kube-system rollout restart deployment coredns

Troubleshooting CoreDNS

If CoreDNS is not functioning as expected, follow these troubleshooting steps:

  1. Check Pod Status:
    • kubectl get pods -n kube-system -l k8s-app=kube-dns
  2. Inspect Logs:
    • kubectl logs -n kube-system -l k8s-app=kube-dns
  3. Verify ConfigMap:
    • kubectl -n kube-system get configmap coredns -o yaml

Frequently Asked Questions

How do I check if CoreDNS is working?

You can use the kubectl exec command to run a DNS query from within a pod:

kubectl exec -it <pod-name> -- nslookup kubernetes.default

Can I customize the DNS settings for specific namespaces?

Yes, you can use the kubernetes plugin in the Corefile to specify different DNS settings for different namespaces.

What are the recommended DNS servers to forward queries to?

Google DNS servers (8.8.8.8, 8.8.4.4) are commonly used, but you can use any reliable external DNS servers.

Conclusion

Installing CoreDNS on Kubernetes is a crucial step in ensuring efficient and reliable DNS resolution within your cluster. By following this comprehensive guide, you can set up CoreDNS, customize its configuration, and troubleshoot common issues. CoreDNS’s flexibility and scalability make it an excellent choice for managing DNS in Kubernetes environments.

Enhance your Kubernetes cluster’s DNS capabilities today by installing and configuring CoreDNS, and enjoy seamless service discovery and network performance. Thank you for reading the DevopsRoles page!

Top 9 MLOps Platforms to Learn in 2024

Introduction

MLOps, a compound of “Machine Learning” and “Operations,” is rapidly becoming a crucial part of the AI lifecycle. As businesses increasingly deploy machine learning models in production environments, the need for robust MLOps platforms has grown. These platforms enable seamless collaboration between data scientists, ML engineers, and operations teams, ensuring that models are reliable, scalable, and maintainable. In 2024, mastering the right MLOps platform could be your ticket to a thriving career in AI. This article will guide you through the Top 9 MLOps Platforms to Learn in 2024, ranging from beginner-friendly to advanced tools.

1. Kubeflow

What is Kubeflow?

Kubeflow is an open-source platform designed to make deploying machine learning (ML) models on Kubernetes simple, portable, and scalable. It provides a comprehensive suite of tools that can manage the entire ML lifecycle, from training to deployment.

Key Features

  • Scalability: Leverages Kubernetes for scaling.
  • End-to-End Workflow: Supports the entire ML lifecycle.
  • Flexibility: Works with popular frameworks like TensorFlow, PyTorch, and more.

Why Learn Kubeflow in 2024?

Kubeflow is ideal for those who already have experience with Kubernetes and want to extend their skills into MLOps. It’s a powerful platform that’s gaining traction in enterprises globally, making it a valuable tool to add to your skill set.

2. MLflow

What is MLflow?

MLflow is an open-source platform designed to manage the ML lifecycle, including experimentation, reproducibility, and deployment. It’s framework-agnostic, making it a versatile choice for any ML project.

Key Features

  • Experiment Tracking: Logs parameters, code, and results.
  • Model Management: Facilitates model packaging and deployment.
  • Scalability: Works with large-scale models and datasets.

Why Learn MLflow in 2024?

MLflow’s ease of integration with existing ML pipelines makes it a popular choice for both beginners and experts. If you’re looking for a platform that offers simplicity without compromising on functionality, MLflow is a strong contender.

3. Azure Machine Learning

What is Azure Machine Learning?

Azure Machine Learning is a cloud-based platform provided by Microsoft, designed to help data scientists and ML engineers build, deploy, and manage high-quality models faster and with greater efficiency.

Key Features

  • Automated ML: Speeds up model development.
  • Integration with Azure Ecosystem: Seamless integration with other Azure services.
  • Security: Enterprise-grade security and compliance.

Why Learn Azure Machine Learning in 2024?

For professionals working in cloud environments, Azure Machine Learning offers robust tools and integrations that streamline the MLOps process. Its strong focus on automation and collaboration makes it an excellent choice for enterprise applications.

4. Google Vertex AI

What is Google Vertex AI?

Vertex AI is Google Cloud’s unified platform for machine learning. It integrates the best of Google Cloud’s AI tools into a single environment, making it easier to build, deploy, and scale ML models.

Key Features

  • AutoML: Automated machine learning model training.
  • End-to-End ML Workflows: Manages data preparation, training, and deployment.
  • Scalability: Built to scale with Google Cloud.

Why Learn Google Vertex AI in 2024?

Google Vertex AI is designed to simplify the MLOps process while maintaining the flexibility to handle complex ML tasks. It’s a powerful platform for those looking to leverage Google’s cutting-edge AI technology in their projects.

5. Amazon SageMaker

What is Amazon SageMaker?

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Key Features

  • Integrated Development Environment: Offers Jupyter notebooks for easy development.
  • Hyperparameter Tuning: Automatically finds the best version of a model.
  • Model Monitoring: Continuously monitors deployed models.

Why Learn Amazon SageMaker in 2024?

Amazon SageMaker is a market leader in the MLOps space, particularly for those already within the AWS ecosystem. Its comprehensive tools and resources make it an essential platform for any ML professional.

6. DVC (Data Version Control)

What is DVC?

DVC is an open-source version control system for machine learning projects. It enables teams to version control datasets, manage experiments, and share results effectively.

Key Features

  • Data Management: Version controls large datasets.
  • Experiment Tracking: Keeps track of various model iterations.
  • Integration with Git: Works seamlessly with Git repositories.

Why Learn DVC in 2024?

DVC is gaining popularity for its ability to integrate smoothly into existing development workflows. It’s especially useful for teams that need to collaborate on large-scale ML projects with complex data dependencies.

7. Weights & Biases

What is Weights & Biases?

Weights & Biases (W&B) is a platform that provides tools for tracking experiments, visualizing metrics, and managing machine learning models.

Key Features

  • Real-Time Experiment Tracking: Monitor experiments as they run.
  • Collaboration Tools: Share results and collaborate with teams easily.
  • Integration: Works with most ML frameworks.

Why Learn Weights & Biases in 2024?

W&B is widely used in both academia and industry, making it a versatile tool for any ML professional. It’s particularly useful for teams that require detailed experiment tracking and model monitoring.

8. Pachyderm

What is Pachyderm?

Pachyderm is a data engineering platform that integrates with Kubernetes, providing data versioning and pipeline automation capabilities for ML projects.

Key Features

  • Data Lineage: Tracks data changes throughout the pipeline.
  • Pipeline Automation: Automates complex ML pipelines.
  • Scalability: Scales effortlessly with Kubernetes.

Why Learn Pachyderm in 2024?

Pachyderm is perfect for those looking to manage complex data pipelines and ensure reproducibility in their ML workflows. Its strong data lineage features make it a standout choice for data-intensive projects.

9. Allegro AI

What is Allegro AI?

Allegro AI is a specialized MLOps platform designed for deep learning and computer vision projects. It offers tools for dataset management, model training, and deployment.

Key Features

  • Dataset Management: Efficiently manages large-scale datasets.
  • Model Training: Optimized for deep learning workflows.
  • Deployment: Streamlines the deployment of complex models.

Why Learn Allegro AI in 2024?

For professionals working in deep learning and computer vision, Allegro AI provides the specialized tools needed to manage and optimize these complex projects. It’s a platform worth mastering if you’re focused on these areas.

Frequently Asked Questions (FAQs)

1. What is MLOps?

MLOps is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the process of deploying and maintaining machine learning models in production.

2. Why is MLOps important?

MLOps is crucial because it ensures that ML models are scalable, reliable, and maintainable in production environments. It also fosters collaboration between data scientists, ML engineers, and operations teams.

3. Which MLOps platform is best for beginners?

Platforms like MLflow and Weights & Biases are often recommended for beginners due to their ease of use and strong community support.

4. What skills are needed to excel in MLOps?

To excel in MLOps, one needs a strong foundation in machine learning, familiarity with DevOps practices, and proficiency in tools like Kubernetes, Docker, and version control systems.

5. Is learning MLOps platforms worth it in 2024?

Absolutely! As more companies invest in AI and ML, the demand for professionals with MLOps skills is set to rise, making it a valuable area of expertise.

Conclusion

Mastering MLOps platforms in 2024 is essential for anyone looking to stay competitive in the AI and ML industries. Whether you’re a beginner or an experienced professional, these platforms offer a range of tools and capabilities that can help you manage the ML lifecycle more effectively. Start with a platform that aligns with your current skill set and gradually explore more advanced options as you gain experience. Investing time in learning these platforms will pay off as businesses continue to rely on machine learning to drive innovation and growth. Thank you for reading the DevopsRoles page!

How to Install tcpdump in Kubernetes Pod: A Comprehensive Guide

Introduction

Kubernetes has become the go-to solution for deploying, scaling, and managing containerized applications. However, troubleshooting network issues within Kubernetes can be challenging. That’s where tools like tcpdump come in handy. How to Install tcpdump in Kubernetes Pod, providing you with the necessary steps and tips to enhance your network troubleshooting capabilities.

What is tcpdump?

Tcpdump is a powerful command-line packet analyzer tool that allows users to capture and analyze network traffic. It is widely used for network troubleshooting and security auditing.

Why Use tcpdump in Kubernetes?

Kubernetes abstracts away much of the underlying network complexity, making it harder to diagnose network issues. Using tcpdump in a Kubernetes pod helps in capturing network traffic, analyzing communication between pods, and identifying potential issues.

Pre-requisites

Before you begin, ensure you have the following:

  • A running Kubernetes cluster
  • kubectl configured to interact with your cluster
  • Basic understanding of Kubernetes and Docker

Step-by-Step Guide How to install tcpdump in kubernetes pod

Method 1: Using kubectl exec

This method involves installing tcpdump directly in a running pod using kubectl exec.

  1. Identify the Pod: kubectl get pods
  2. Install tcpdump:
    • kubectl exec -it <pod-name> -- apt-get update
    • kubectl exec -it <pod-name> -- apt-get install -y tcpdump
  3. Verify Installation:
    • kubectl exec -it <pod-name> -- tcpdump --version

Method 2: Building a Custom Docker Image

This method involves creating a custom Docker image with tcpdump pre-installed.

Create a Dockerfile:

FROM ubuntu:latest
RUN apt-get update && apt-get install -y tcpdump

Build the Docker Image:

docker build -t my-tcpdump-image .

Deploy the Pod Using the Custom Image:

apiVersion: v1
kind: Pod
metadata:
  name: tcpdump-pod
spec:
  containers:
    - name: tcpdump-container
      image: my-tcpdump-image
      command: ["/bin/sh"]
      args: ["-c", "while true; do sleep 30; done;"]

kubectl apply -f tcpdump-pod.yaml

Method 3: Using Init Containers

This method uses init containers to install tcpdump before the main container starts.

Define the Pod with Init Container:

apiVersion: v1
kind: Pod
metadata:
  name: tcpdump-pod
spec:
  initContainers:
  - name: install-tcpdump
    image: ubuntu:latest
    command:
    - sh
    - -c
    - apt-get update && apt-get install -y tcpdump
    volumeMounts:
    - name: tcpdump-bin
      mountPath: /tcpdump
  containers:
  - name: main-container
    image: your-application-image
    volumeMounts:
    - name: tcpdump-bin
      mountPath: /usr/local/bin
      subPath: tcpdump
  volumes:
  - name: tcpdump-bin
    emptyDir: {}

Deploy the Pod: kubectl apply -f tcpdump-pod.yaml

Advanced Usage of tcpdump in Kubernetes

Filtering Packets

To capture specific packets, use filters:

 kubectl exec -it <pod-name> -- tcpdump -i eth0 port 80

Saving and Reading Captured Data

Save captured packets to a file:

 kubectl exec -it <pod-name> -- tcpdump -i eth0 -w /tmp/capture.pcap

Read the saved capture file:

kubectl cp <pod-name>:/tmp/capture.pcap ./capture.pcap 
tcpdump -r capture.pcap

Analyzing Captured Data

Use tools like Wireshark for detailed analysis of the captured data:

 wireshark capture.pcap

Frequently Asked Questions

Can I install tcpdump on all pods in a namespace?

Yes, you can use a DaemonSet to deploy tcpdump across all nodes, ensuring all pods can have tcpdump available.

Is it safe to run tcpdump in a production environment?

Running tcpdump can consume resources and potentially expose sensitive data. It’s advisable to use it in a controlled and monitored manner.

How do I capture traffic between two specific pods?

You can use tcpdump to filter traffic between specific IP addresses or ports:

 kubectl exec -it <pod-name> -- tcpdump -i eth0 src <source-ip> and dst <destination-ip>

Conclusion

Installing tcpdump in a Kubernetes pod is a powerful way to troubleshoot and analyze network traffic within your cluster. Whether you choose to use kubectl exec, build a custom Docker image, or utilize init containers, each method has its own advantages. By following the steps outlined in this guide, you can ensure that you have the necessary tools to diagnose and resolve network issues efficiently.

Enhance your Kubernetes troubleshooting toolkit today by installing tcpdump and gain deeper insights into your network traffic. Thank you for reading the DevopsRoles page!

How to Install Prometheus on Kubernetes

Introduction

Monitoring Kubernetes clusters efficiently is crucial for maintaining the health and performance of your applications. Prometheus, a powerful open-source monitoring and alerting toolkit, is the go-to solution for many Kubernetes administrators. In this guide, we will walk you through how to install Prometheus on Kubernetes. We will start with the basics and then move on to more advanced configurations.

Prerequisites

Before we begin, ensure you have the following:

  • A Kubernetes cluster up and running
  • kubectl command-line tool configured to communicate with your cluster
  • Basic understanding of Kubernetes concepts

Step 1: Setting Up Prometheus using Helm

What is Helm?

Helm is a package manager for Kubernetes that helps in managing Kubernetes applications. It uses a packaging format called charts, which are a collection of files that describe a related set of Kubernetes resources.

Installing Helm

First, you need to install Helm. Follow the instructions for your operating system from the official Helm documentation.

Adding the Prometheus Community Helm Repository

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update

Installing Prometheus

helm install prometheus prometheus-community/prometheus

This command will deploy Prometheus in your Kubernetes cluster with the default configuration. You can check the status of the deployment using:

kubectl get pods -l "release=prometheus"

Step 2: Configuring Prometheus

Customizing the Prometheus Configuration

Helm allows you to customize the installation using values files. Create a values.yaml file to specify your custom configurations.

# values.yaml
alertmanager:
enabled: true
server:
persistentVolume:
enabled: true
size: 10Gi

Applying the Custom Configuration

helm upgrade --install prometheus prometheus-community/prometheus -f values.yaml

Step 3: Exposing Prometheus

Using a NodePort Service

To access Prometheus from outside the cluster, you can use a NodePort service.

# prometheus-service.yaml
apiVersion: v1
kind: Service
metadata:
name: prometheus-service
spec:
type: NodePort
ports:
- port: 9090
targetPort: 9090
nodePort: 30000
selector:
app: prometheus

Apply the service:

kubectl apply -f prometheus-service.yaml

Using Ingress

Alternatively, you can expose Prometheus using an Ingress resource.

# prometheus-ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: prometheus-ingress
spec:
rules:
- host: prometheus.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: prometheus-service
port:
number: 9090

Apply the ingress:

kubectl apply -f prometheus-ingress.yaml

Ensure you have an Ingress controller running in your cluster and update your DNS settings to point to the Ingress controller’s external IP.

Step 4: Monitoring Kubernetes with Prometheus

Deploying the Kubernetes Metrics Server

Prometheus uses metrics exposed by the Kubernetes Metrics Server for monitoring.

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

Configuring Prometheus to Scrape Metrics

Edit the Prometheus configuration to scrape the Kubernetes metrics:

# values.yaml
serverFiles:
prometheus.yml:
scrape_configs:
- job_name: 'kubernetes-nodes'
kubernetes_sd_configs:
- role: node

Apply the configuration:

helm upgrade --install prometheus prometheus-community/prometheus -f values.yaml

Step 5: Setting Up Alerts

Configuring Alertmanager

Alertmanager handles alerts sent by Prometheus. Configure Alertmanager in the values.yaml file:

alertmanager:
config:
global:
resolve_timeout: 5m
route:
receiver: 'email'
receivers:
- name: 'email'
email_configs:
- to: 'your-email@example.com'
from: 'prometheus@example.com'

Apply the configuration:

helm upgrade --install prometheus prometheus-community/prometheus -f values.yaml

Step 6: Advanced Prometheus Configurations

Using Persistent Storage

Prometheus requires persistent storage to retain data across restarts. Configure a persistent volume in values.yaml:

server:
persistentVolume:
enabled: true
size: 50Gi
storageClass: standard

Apply the configuration:

helm upgrade --install prometheus prometheus-community/prometheus -f values.yaml

Securing Prometheus

Enable authentication and HTTPS for Prometheus using an Ingress controller with TLS:

# prometheus-ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: prometheus-ingress
spec:
tls:
- hosts:
- prometheus.example.com
secretName: prometheus-tls
rules:
- host: prometheus.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: prometheus-service
port:
number: 9090

Apply the configuration:

kubectl apply -f prometheus-ingress.yaml

Frequently Asked Questions

What is Prometheus?

Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. It is designed for reliability and scalability.

Why use Prometheus with Kubernetes?

Prometheus integrates seamlessly with Kubernetes, offering a powerful solution for monitoring and alerting on the performance and health of Kubernetes clusters.

How do I upgrade Prometheus in Kubernetes?

You can upgrade Prometheus using Helm with the following command:

helm upgrade prometheus prometheus-community/prometheus

How do I view Prometheus metrics?

Access the Prometheus UI via the service or Ingress URL configured earlier (e.g., http://prometheus.example.com)

Can I use Prometheus with Grafana?

Yes, Grafana is commonly used with Prometheus for visualizing metrics. You can add Prometheus as a data source in Grafana.

Conclusion

Installing Prometheus on Kubernetes is a straightforward process with Helm. By following this guide, you should have Prometheus up and running, monitoring your Kubernetes cluster effectively. With advanced configurations, you can tailor Prometheus to meet your specific needs, ensuring your cluster’s health and performance are always in check. Thank you for reading the DevopsRoles page!

How This Company Transformed Their ML Workflow with MLOps

Introduction

Machine learning (ML) has become a cornerstone for businesses looking to harness data-driven insights. However, managing ML workflows can be challenging, requiring robust systems to handle data pipelines, model training, deployment, and monitoring. This case study explores how one company successfully transformed their ML Workflow with MLOps, achieving significant improvements in efficiency and scalability.

Understanding MLOps

What is MLOps?

MLOps, or Machine Learning Operations, is the practice of combining machine learning development and operations (DevOps) to automate and streamline the end-to-end ML lifecycle. This includes data preprocessing, model training, deployment, monitoring, and management.

Benefits of MLOps

  • Scalability: Easily scale ML models and workflows to handle large datasets and complex algorithms.
  • Efficiency: Automate repetitive tasks, reducing the time and effort required for model development and deployment.
  • Consistency: Ensure consistent and reproducible results across different environments and team members.
  • Collaboration: Foster better collaboration between data scientists, ML engineers, and operations teams.

Company Background

The company in focus is a global leader in the e-commerce industry, dealing with millions of transactions daily. With a dedicated team of data scientists and engineers, they aimed to enhance their ML workflow to handle growing data volumes and complex models.

The Challenge

Initial Workflow Issues

  • Manual Processes: The company relied heavily on manual processes for data preprocessing, model training, and deployment, leading to inefficiencies.
  • Lack of Automation: The absence of automated pipelines resulted in longer development cycles and delayed deployment.
  • Scalability Concerns: Handling large datasets and complex models was becoming increasingly difficult, affecting model performance and accuracy.

The Transformation with MLOps

Step 1: Establishing Data Pipelines

The first step was to automate data preprocessing and feature engineering using robust data pipelines.

Tools and Technologies

  • Apache Airflow: For orchestrating complex data workflows.
  • Kubernetes: To manage containerized data processing tasks.

Benefits

  • Automated Data Ingestion: Streamlined data ingestion from various sources.
  • Consistent Data Processing: Ensured consistent preprocessing and feature engineering across all datasets.

Step 2: Automating Model Training

The next phase involved automating model training to reduce manual intervention and accelerate the training process.

Tools and Technologies

  • Kubeflow: For managing ML workflows on Kubernetes.
  • TensorFlow Extended (TFX): To build scalable and reproducible ML pipelines.

Benefits

  • Automated Training Pipelines: Enabled automated model training and hyperparameter tuning.
  • Reduced Development Time: Significantly decreased the time required to train and validate models.

Step 3: Streamlining Model Deployment

The company then focused on automating the deployment process to ensure models were deployed quickly and reliably.

Tools and Technologies

  • MLflow: For managing the entire ML lifecycle, including experiment tracking and model registry.
  • Docker: To containerize ML models for consistent deployment across different environments.

Benefits

  • Continuous Deployment: Enabled continuous integration and deployment of ML models.
  • Improved Reliability: Ensured models were deployed consistently with minimal downtime.

Step 4: Monitoring and Maintenance

Monitoring model performance and maintaining models in production was the final step in their MLOps transformation.

Tools and Technologies

  • Prometheus and Grafana: For monitoring model performance and system metrics.
  • Alerting Systems: To detect and respond to anomalies in real-time.

Benefits

  • Real-time Monitoring: Provided real-time insights into model performance and health.
  • Proactive Maintenance: Enabled proactive identification and resolution of issues.

Results and Impact

Enhanced Productivity

The automation of data pipelines, model training, and deployment led to a significant increase in productivity. Data scientists could focus more on developing innovative models rather than managing workflows.

Scalability Achieved

The company successfully scaled their ML workflows to handle larger datasets and more complex models, improving the overall performance and accuracy of their ML solutions.

Consistent and Reliable Deployments

Automated deployment pipelines ensured that models were consistently and reliably deployed, reducing downtime and improving the reliability of ML applications.

Improved Collaboration

Better collaboration between data scientists, ML engineers, and operations teams was achieved, leading to more cohesive and efficient ML development cycles.

Frequently Asked Questions

What are the main components of MLOps?

The main components of MLOps include automated data pipelines, model training, deployment, monitoring, and maintenance.

How does MLOps improve scalability?

MLOps improves scalability by automating workflows and using scalable technologies like Kubernetes and Apache Airflow to handle large datasets and complex models.

What tools are commonly used in MLOps?

Common tools include Apache Airflow, Kubeflow, TensorFlow Extended (TFX), MLflow, Docker, Prometheus, and Grafana.

Can MLOps be applied to any industry?

Yes, MLOps can be applied to any industry that leverages machine learning, including finance, healthcare, retail, and more.

How long does it take to implement MLOps?

The implementation timeline for MLOps varies based on the complexity of the existing ML workflows and the level of automation desired. It can take from a few months to over a year.

Conclusion ML Workflow with MLOps

The transformation of this company’s ML workflow using MLOps demonstrates the immense benefits of adopting automated and scalable ML practices. By streamlining data pipelines, automating model training and deployment, and implementing robust monitoring systems, the company achieved significant improvements in productivity, scalability, and model performance. This case study highlights the potential of MLOps to revolutionize ML workflows and drive business success. Thank you for reading the DevopsRoles page!

Comprehensive Guide to Show Users in MySQL Database on Linux

Introduction

Managing users in a MySQL database is a fundamental task for database administrators. Whether you’re granting privileges, ensuring security, or auditing your database environment, knowing how to list users is essential. This article will guide you through the process of Show Users in MySQL Database on Linux system, providing examples ranging from basic to advanced.

Basic Command to Show Users

To show the users in a MySQL database, you can use the following basic command:

SELECT user FROM mysql.user;

This command retrieves a list of all users from the mysql.user table.

Step-by-Step Guide

Open MySQL Command Line: Access your MySQL database using the MySQL command line client:

mysql -u root -p

Enter your root password when prompted.

Run the Query

SELECT user FROM mysql.user;
This will display a list of all users in your MySQL database.

Advanced User Listing Techniques

Filtering User Lists

To filter the user list based on specific criteria, you can use the WHERE clause. For example, to list users with a specific host, you can use:

SELECT user, host 
FROM mysql.user 
WHERE host = 'localhost';

Using Information Schema

The INFORMATION_SCHEMA is another useful way to list users. This method is especially helpful for more detailed information about user privileges and roles.

SELECT user, host 
FROM INFORMATION_SCHEMA.USER_PRIVILEGES;

This query provides detailed information about user privileges.

Practical Examples

Listing Users with Specific Privileges

To find users with specific privileges, such as SELECT privileges, use the following query:

SELECT grantee, privilege_type 
FROM INFORMATION_SCHEMA.USER_PRIVILEGES 
WHERE privilege_type = 'SELECT';

Exporting User List to a File

To export the list of users to a file for auditing purposes, follow these steps:

Open MySQL Command Line:

mysql -u root -p

Run the Query and Export:

SELECT user, host 
INTO OUTFILE '/path/to/exported_users.csv' 
FIELDS TERMINATED BY ',' 
LINES TERMINATED BY '\n' 
FROM mysql.user;

Ensure the MySQL server has the appropriate permissions to write to the specified path.

Common Issues and Troubleshooting

When listing users, you might encounter some common issues:

Permissions Issues

Ensure you have the necessary permissions to view the mysql.user table:

SHOW GRANTS FOR 'your_username'@'your_host';

No Results Returned

If no results are returned, check the MySQL version and ensure the mysql.user table exists:

SELECT VERSION();
SHOW TABLES FROM mysql;

FAQs

How do I list all users in MySQL?

Use the query:

SELECT user 
FROM mysql.user;

How can I list users with specific privileges?

Use:

SELECT grantee, privilege_type 
FROM INFORMATION_SCHEMA.USER_PRIVILEGES 
WHERE privilege_type = 'SELECT';

Can I export the user list to a file?

Yes, use:

SELECT user, host 
INTO OUTFILE '/path/to/exported_users.csv' 
FIELDS TERMINATED BY ',' 
LINES TERMINATED BY '\n' 
FROM mysql.user;

What should I do if I encounter permissions issues?

Check your grants with:

SHOW GRANTS FOR 'your_username'@'your_host';

Conclusion

Managing and auditing users in a MySQL database is a critical task for database administrators. By following the steps and examples provided, you can effectively list users in a MySQL database on a Linux system, whether you’re performing basic queries or advanced filtering. Remember to ensure you have the necessary permissions and to handle any common issues promptly. This guide aims to provide you with a comprehensive understanding and practical approach to listing users in MySQL, enhancing your database management skills. Thank you for reading the DevopsRoles page!

How to Install ArgoCD on Kubernetes

Introduction

ArgoCD is a powerful continuous delivery tool for Kubernetes, enabling developers to automate the deployment of their applications. This guide will walk you through the steps to install ArgoCD on Kubernetes, covering basic installation to advanced configurations. By the end of this tutorial, you’ll have a fully functional ArgoCD instance running on your Kubernetes cluster.

What is ArgoCD?

ArgoCD is a declarative, GitOps continuous delivery tool for Kubernetes. It automates the deployment of the desired application state as defined in a Git repository. ArgoCD continuously monitors running applications and compares the current, live state against the desired target state. When the live state deviates from the target state, ArgoCD can automatically or manually synchronize it.

Prerequisites

Before we begin, ensure you have the following prerequisites:

  • A running Kubernetes cluster (v1.18+)
  • kubectl installed and configured to interact with your cluster
  • Helm installed on your local machine

Step 1: Install ArgoCD

1.1 Create a Namespace for ArgoCD

First, create a namespace for ArgoCD to keep its resources isolated:

kubectl create namespace argocd

1.2 Install ArgoCD Using Kubectl

You can install ArgoCD by applying the official ArgoCD manifests:

kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml

1.3 Verify the Installation

Check if all ArgoCD pods are running:

kubectl get pods -n argocd

You should see something like this:

NAME                                      READY   STATUS    RESTARTS   AGE
argocd-application-controller-0 1/1 Running 0 2m
argocd-dex-server-847f8bc98c-dkj5d 1/1 Running 0 2m
argocd-redis-64c69757cf-jdksl 1/1 Running 0 2m
argocd-repo-server-6b6d9d8d85-rfkl9 1/1 Running 0 2m
argocd-server-5b6d9d8d85-vn9ms 1/1 Running 0 2m

Step 2: Access the ArgoCD UI

2.1 Expose the ArgoCD API Server

To access the ArgoCD UI, you’ll need to expose the ArgoCD API server using a service type that suits your needs. For simplicity, we’ll use a LoadBalancer service:

kubectl patch svc argocd-server -n argocd -p '{"spec": {"type": "LoadBalancer"}}'

2.2 Get the Initial Admin Password

Retrieve the initial admin password, which is auto-generated and stored in a Kubernetes secret:

kubectl get secret argocd-initial-admin-secret -n argocd -o jsonpath="{.data.password}" | base64 --decode

2.3 Open the ArgoCD UI

Find the external IP address of the ArgoCD API server:

kubectl get svc -n argocd

Look for the argocd-server service and note the EXTERNAL-IP. Open your browser and navigate to https://<EXTERNAL-IP>. Log in with the username admin and the password you retrieved in the previous step.

Step 3: Configure ArgoCD

3.1 Connect ArgoCD to a Git Repository

ArgoCD uses Git repositories as the source of truth for application definitions. To connect ArgoCD to your Git repository, follow these steps:

  1. Navigate to the ArgoCD UI.
  2. Click on Settings > Repositories > Connect Repo.
  3. Enter the repository URL and, if necessary, provide authentication details.

3.2 Create an Application in ArgoCD

To create an application:

  1. Click on New App.
  2. Fill in the application details:
    • Application Name: Name of your application.
    • Project: Default.
    • Sync Policy: Automatic or Manual.
    • Repository URL: URL of your Git repository.
    • Revision: Branch to track (e.g., main).
    • Path: Path within the repository where the Kubernetes manifests are located.
    • Cluster: The Kubernetes cluster where the application will be deployed.
    • Namespace: Namespace in the cluster where the application will be deployed.
  3. Click Create.

Advanced Configurations

4.1 Set Up RBAC

ArgoCD supports Role-Based Access Control (RBAC) to restrict access to certain features and resources. To configure RBAC:

  1. Create a ConfigMap named argocd-rbac-cm in the argocd namespace.
  2. Define roles and policies in the ConfigMap:
apiVersion: v1
kind: ConfigMap
metadata:
name: argocd-rbac-cm
namespace: argocd
data:
policy.csv: |
g, admin, role:admin
g, developer, role:developer
policy.default: role:readonly
scopes: '[groups]'

4.2 Customize the UI

You can customize the ArgoCD UI by modifying the argocd-cm ConfigMap:

apiVersion: v1
kind: ConfigMap
metadata:
name: argocd-cm
namespace: argocd
data:
ui.banner: "Welcome to ArgoCD!"
ui.theme: "dark"

Apply the changes:

kubectl apply -f argocd-cm.yaml

4.3 Enable SSO

ArgoCD supports Single Sign-On (SSO) with various identity providers like OAuth2, OIDC, SAML, and LDAP. To enable SSO:

  1. Create a secret with your identity provider’s credentials.
  2. Update the argocd-cm ConfigMap with the SSO configuration.

For example, to configure OIDC:

apiVersion: v1
kind: ConfigMap
metadata:
name: argocd-cm
namespace: argocd
data:
oidc.config: |
name: Okta
issuer: https://<your-okta-domain>/oauth2/default
clientID: <your-client-id>
clientSecret: $oidc.clientSecret

Create the secret with the client’s secret:

kubectl create secret generic argocd-secret -n argocd --from-literal=oidc.clientSecret=<your-client-secret>

Troubleshooting

5.1 Common Issues and Solutions

  • Issue: Unable to access the ArgoCD UI.
    • Solution: Ensure the argocd-server service is of type LoadBalancer and has an external IP address.
  • Issue: Application sync failures.
    • Solution: Check the application logs and ensure that the repository path and branch are correct.
  • Issue: Authentication failures.
    • Solution: Verify the credentials and configuration for the Git repository and identity provider.

FAQs

What is ArgoCD?

ArgoCD is a declarative, GitOps continuous delivery tool for Kubernetes that automates the deployment of applications.

How do I install ArgoCD on Kubernetes?

You can install ArgoCD by applying the official manifests provided by ArgoCD, creating a namespace for it, and verifying the installation with kubectl.

How do I access the ArgoCD UI?

You can access the ArgoCD UI by exposing the argocd-server service as a LoadBalancer and navigating to the external IP address in your browser.

How do I connect ArgoCD to my Git repository?

You can connect ArgoCD to your Git repository by navigating to the ArgoCD UI, adding the repository under settings, and providing the necessary authentication details.

Conclusion

Installing ArgoCD on Kubernetes is a straightforward process that involves creating a namespace, applying the installation manifests, and configuring access to the UI. With ArgoCD, you can automate the deployment of your applications, ensuring a consistent and reliable delivery process. By following this guide, you should now have a functional ArgoCD setup and be ready to leverage its powerful features to manage your Kubernetes applications effectively. Thank you for reading the DevopsRoles page!

How to Check if Kubernetes is Installed

Introduction

How to Check if Kubernetes is Installed. Kubernetes, an open-source platform for automating deployment, scaling, and operations of application containers across clusters of hosts, has become a cornerstone for modern cloud-native applications. Whether you’re a seasoned DevOps engineer or just starting, ensuring Kubernetes is correctly installed on your system is crucial.

This guide will walk you through various methods to check if Kubernetes is installed, from basic commands to more advanced techniques. By the end of this article, you’ll be well-equipped to verify Kubernetes installations on different operating systems and environments.

Basic Checks

What is Kubernetes?

Kubernetes is a powerful container orchestration system designed to simplify the deployment, management, and scaling of containerized applications. It supports various platforms, making it versatile for developers and IT operations teams.

Why Check if Kubernetes is Installed?

Ensuring Kubernetes is correctly installed is crucial for:

  • Running containerized applications efficiently.
  • Managing clusters and workloads.
  • Utilizing Kubernetes features like scaling and self-healing.

Checking Kubernetes Installation on Different Operating Systems

Linux

Using Command Line

  1. Open Terminal: Launch the terminal.
  2. Run kubectl version: kubectl version --client
    • This command checks the client version of Kubernetes installed on your system.
  3. Check kubectl: kubectl get nodes
    • If Kubernetes is installed and running, it will return the nodes in the cluster.

Using Package Manager

  1. Debian/Ubuntu: dpkg -l | grep -i kubectl
  2. Red Hat/CentOS: rpm -qa | grep -i kubectl

Windows

Using Command Prompt or PowerShell

  1. Open Command Prompt or PowerShell.
  2. Run kubectl version:powershellCopy codekubectl version --client
    • This will display the version information if Kubernetes is installed.

Using Windows Subsystem for Linux (WSL)

  1. Open WSL Terminal.
  2. Run kubectl version: kubectl version --client

MacOS

Using Command Line

  1. Open Terminal.
  2. Run kubectl version: kubectl version --client
    • This will check if the Kubernetes client is installed.

Using Homebrew

  1. Check Homebrew Installation: brew list | grep -i kubernetes
    • This will list Kubernetes-related packages installed via Homebrew.

Advanced Verification Techniques

Using kubectl

The kubectl command-line tool is essential for interacting with Kubernetes clusters. Here are some advanced techniques to verify your installation.

Check Kubernetes Version

kubectl version --short
  • This command provides a brief overview of the Kubernetes version, including both client and server versions.

Verify Cluster Information

kubectl cluster-info
  • Displays information about the Kubernetes cluster, confirming if it is correctly set up.

Checking Kubernetes Services

List All Services

kubectl get services
  • Lists all services running in the cluster, indicating that Kubernetes is actively managing them.

Describe a Service

kubectl describe service <service-name>
  • Provides detailed information about a specific service, useful for troubleshooting.

Verifying Kubernetes Components

List All Pods

kubectl get pods --all-namespaces
  • Lists all pods across all namespaces, showing active deployments and applications.

Describe a Pod

kubectl describe pod <pod-name> -n <namespace>
  • Gives detailed information about a specific pod, including events and resource usage.

Check Node Status

kubectl get nodes
  • Lists all nodes in the cluster along with their status.

Describe a Node

kubectl describe node <node-name>
  • Provides detailed information about a specific node, including resource allocation and conditions.

Frequently Asked Questions

How do I install Kubernetes on my system?

You can install Kubernetes using various methods depending on your operating system. For detailed instructions, refer to the official Kubernetes documentation.

What is the difference between kubectl and kubelet?

kubectl is a command-line tool for interacting with Kubernetes clusters, while kubelet is an agent that runs on each node in the cluster to ensure containers are running.

How do I update Kubernetes?

Updating Kubernetes involves updating the control plane and the nodes. Refer to the official Kubernetes upgrade guide for step-by-step instructions.

What should I do if Kubernetes is not installed?

If Kubernetes is not installed, you can follow the installation guides provided by the Kubernetes website for your specific operating system.

How do I check the Kubernetes dashboard?

To check the Kubernetes dashboard, you can start it using:

kubectl proxy

Then, access it via

http://localhost:8001/api/v1/namespaces/kube-system/services/https:kubernetes-dashboard:/proxy/

Conclusion

Checking if Kubernetes is installed on your system is a critical step before managing containerized applications. This guide has provided you with both basic and advanced methods to verify the installation across different operating systems. By following these steps, you can ensure that your Kubernetes environment is correctly set up and ready for use. If you encounter any issues, the detailed commands and instructions in this article will help you troubleshoot and resolve them efficiently. Thank you for reading the DevopsRoles page!

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