Let's Talk DevOps

Real-World DevOps, Real Solutions

Tag: Cloud-Native

  • How to Create a Pull Request Using GitHub Through VSCode

    Visual Studio Code (VSCode) has risen as a favorite among developers due to its extensibility and tight integration with many tools, including GitHub. In this tutorial, we’ll cover how to create a pull request (PR) on GitHub directly from VSCode. Given that our audience is highly technical, we’ll provide detailed steps along with screenshots and necessary code.

    Prerequisites:

    • VSCode Installed: If not already, download and install from VSCode’s official website.
    • GitHub Account: You’ll need a GitHub account to interact with repositories.
    • Git Installed: Ensure you have git installed on your machine.
    • GitHub Pull Requests and Issues Extension: Install it from the VSCode Marketplace.

    Steps:

    Clone Your Repository

    First, ensure you have the target repository cloned on your local machine. If not:

    git clone <repository-url>

    Open Repository in VSCode

    Navigate to the cloned directory:

    cd <repository-name>

    Launch VSCode in this directory:

    code .

    Create a New Branch

    Before making any changes, it’s best practice to create a new branch. In the bottom-left corner of VSCode, click on the current branch name (likely main or master). A top bar will appear. Click on + Create New Branch and give it a meaningful name related to your changes.

    Make Your Changes

    Once you’re on your new branch, make the necessary changes to the code or files. VSCode’s source control tab (represented by the branch icon on the sidebar) will list the changes made.

    Stage and Commit Changes

    Click on the + icon next to each changed file to stage the changes. Once all changes are staged, enter a commit message in the text box and click the checkmark at the top to commit.

    Push the Branch to GitHub

    Click on the cloud-upload icon in the bottom-left corner to push your branch to GitHub.

    Create a Pull Request

    With the GitHub Pull Requests and Issues Extension installed, you’ll see a GitHub icon in the sidebar. Clicking on this will reveal a section titled GitHub Pull Requests.

    Click on the + icon next to it. It’ll fetch the branch and present a UI to create a PR. Fill in the necessary details:

    • Title: Summarize the change in a short sentence.
    • Description: Provide a detailed description of what changes were made and why.
    • Base Repository: The repository to which you want to merge the changes.
    • Base: The branch (usually main or master) to which you want to merge the changes.
    • Head Repository: Your forked repository (if you’re working on a fork) or the original one.
    • Compare: Your feature/fix branch.

    Once filled, click Create.

    Review and Merge

    Your PR is now on GitHub. It can be reviewed, commented upon, and eventually merged by maintainers.

    Conclusion

    VSCode’s deep integration with GitHub makes it a breeze to handle Git operations, including creating PRs. By following this guide, you can streamline your Git workflow without ever leaving your favorite editor!

  • AI Workloads for Kubernetes


    Introduction

    In recent years, Kubernetes has emerged as the go-to solution for orchestrating containerized applications at scale. But when it comes to deploying AI workloads, does it offer the same level of efficiency and convenience? In this blog post, we delve into the types of AI workloads that are best suited for Kubernetes, and why you should consider it for your next AI project.

    Model Training and Development

    Batch Processing

    When working with large datasets, batch processing becomes a necessity. Kubernetes can efficiently manage batch processing tasks, leveraging its abilities to orchestrate and scale workloads dynamically.

    • Example: A machine learning pipeline that processes terabytes of data overnight, utilizing idle resources to the fullest.
    Hyperparameter Tuning

    Hyperparameter tuning involves running numerous training jobs with different parameters to find the optimal configuration. Kubernetes can streamline this process by managing multiple parallel jobs effortlessly.

    • Example: An AI application that automatically tunes hyperparameters over a grid of values, reducing the time required to find the best model.

    Model Deployment

    Rolling Updates and Rollbacks

    Deploying AI models into production environments requires a system that supports rolling updates and rollbacks. Kubernetes excels in this area, helping teams to maintain high availability even during updates.

    • Example: A recommendation system that undergoes frequent updates without experiencing downtime, ensuring a seamless user experience.
    Auto-Scaling

    AI applications often face variable traffic, requiring a system that can automatically scale resources. Kubernetes’ auto-scaling feature ensures that your application can handle spikes in usage without manual intervention.

    • Example: A voice recognition service that scales up during peak hours, accommodating a large number of simultaneous users without compromising on performance.
    Placeholder: Diagram showing the auto-scaling feature of Kubernetes

    Data Engineering

    Data Pipeline Orchestration

    Managing data pipelines efficiently is critical in AI projects. Kubernetes can orchestrate complex data pipelines, ensuring that each component interacts seamlessly.

    • Example: A data ingestion pipeline that collects, processes, and stores data from various sources, running smoothly with the help of Kubernetes orchestration.
    Stream Processing

    For real-time AI applications, stream processing is a crucial component. Kubernetes facilitates the deployment and management of stream processing workloads, ensuring high availability and fault tolerance.

    • Example: A fraud detection system that analyzes transactions in real-time, leveraging Kubernetes to maintain a steady flow of data processing.

    Conclusion

    Kubernetes offers a robust solution for deploying and managing AI workloads at scale. Its features like auto-scaling, rolling updates, and efficient batch processing make it an excellent choice for AI practitioners aiming to streamline their operations and bring their solutions to market swiftly and efficiently.

    Whether you are working on model training, deployment, or data engineering, Kubernetes provides the tools to orchestrate your workloads effectively, saving time and reducing complexity.

    To get started with Kubernetes for your AI projects, consider exploring the rich ecosystem of tools and communities available to support you on your journey.

  • Streamline Kubernetes Management through Automation

    Automation in managing Kubernetes clusters has burgeoned into an essential practice that enhances efficiency, security, and the seamless deployment of applications. With the exponential growth in containerized applications, automation has facilitated streamlined operations, reducing the room for human error while significantly saving time. Let’s delve deeper into the crucial role automation plays in managing Kubernetes clusters.

    Section 1: The Imperative of Automation in Kubernetes

    1.1 The Kubernetes Landscape

    Before delving into the nuances of automation, let’s briefly recapitulate the fundamental components of Kubernetes, encompassing pods, nodes, and clusters, and their symbiotic relationships facilitating a harmonious operational environment.

    1.2 The Need for Automation

    Automation emerges as a vanguard in managing complex environments effortlessly, fostering efficiency, reducing downtime, and ensuring the optimal utilization of resources.

    1.2.1 Efficiency and Scalability

    Automation in Kubernetes ensures that clusters can dynamically scale based on the workload, fostering efficiency, and resource optimization.

    1.2.2 Reduced Human Error

    Automating repetitive tasks curtails the scope of human error, facilitating seamless operations and mitigating security risks.

    1.2.3 Cost Optimization

    Through efficient resource management, automation aids in cost reduction by optimizing resource allocation dynamically.

    Section 2: Automation Tools and Processes

    2.1 CI/CD Pipelines

    Continuous Integration and Continuous Deployment (CI/CD) pipelines are at the helm of automation, fostering swift and efficient deployment cycles.

    pipeline:
      build:
        image: node:14
        commands:
          - npm install
          - npm test
      deploy:
        image: google/cloud-sdk
        commands:
          - gcloud container clusters get-credentials cluster-name --zone us-central1-a
          - kubectl apply -f k8s/
    

    Code snippet 1: A simple CI/CD pipeline example.

    2.2 Infrastructure as Code (IaC)

    IaC facilitates the programmable infrastructure, rendering a platform where systems and devices can be managed through code.

    apiVersion: v1
    kind: Pod
    metadata:
      name: mypod
    spec:
      containers:
      - name: mycontainer
        image: nginx
    

    Code snippet 2: Defining a Kubernetes pod using IaC.

    2.3 Configuration Management

    Tools like Ansible and Chef aid in configuration management, ensuring system uniformity and adherence to policies.

    - hosts: kubernetes_nodes
      tasks:
        - name: Ensure Kubelet is installed
          apt: 
            name: kubelet
            state: present
    

    Code snippet 3: Using Ansible for configuration management.

    Section 3: Automation Use Cases in Kubernetes

    3.1 Auto-scaling

    Auto-scaling facilitates automatic adjustments to the system’s computational resources, optimizing performance and curtailing costs.

    3.1.1 Horizontal Pod Autoscaler

    Kubernetes’ Horizontal Pod Autoscaler automatically adjusts the number of pod replicas in a replication controller, deployment, or replica set based on observed CPU utilization.

    apiVersion: autoscaling/v2beta2
    kind: HorizontalPodAutoscaler
    metadata:
      name: myapp-hpa
    spec:
      scaleTargetRef:
        apiVersion: apps/v1
        kind: Deployment
        name: myapp
      minReplicas: 1
      maxReplicas: 10
      metrics:
      - type: Resource
        resource:
          name: cpu
          target:
            type: Utilization
            averageUtilization: 50
    

    Code snippet 4: Defining a Horizontal Pod Autoscaler in Kubernetes.

    3.2 Automated Rollouts and Rollbacks

    Kubernetes aids in automated rollouts and rollbacks, ensuring application uptime and facilitating seamless updates and reversions.

    3.2.1 Deployment Strategies

    Deployment strategies such as blue-green and canary releases can be automated in Kubernetes, facilitating controlled and safe deployments.

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: myapp
    spec:
      strategy:
        type: RollingUpdate
        rollingUpdate:
          maxSurge: 25%
          maxUnavailable: 25%
      selector:
        matchLabels:
          app: myapp
      template:
        metadata:
          labels:
            app: myapp
        spec:
          containers:
          - name: myapp
            image: myapp:v2
    

    Code snippet 5: Configuring a rolling update strategy in a Kubernetes deployment.

    Conclusion: The Future of Kubernetes with Automation

    As Kubernetes continues to be the frontrunner in orchestrating containerized applications, the automation integral to its ecosystem fosters efficiency, security, and scalability. Through a plethora of tools and evolving best practices, automation stands central in leveraging Kubernetes to its fullest potential, orchestrating seamless operations, and steering towards an era of self-healing systems and zero-downtime deployments.

    In conclusion, the ever-evolving landscape of Kubernetes managed through automation guarantees a future where complex deployments are handled with increased efficiency and reduced manual intervention. Leveraging automation tools and practices ensures that Kubernetes clusters not only meet the current requirements but are also future-ready, paving the way for a robust, scalable, and secure operational environment.


    References:

    1. Kubernetes Official Documentation. Retrieved from https://kubernetes.io/docs/
    2. Jenkins, CI/CD, and Kubernetes: Integrating CI/CD with Kubernetes (2021). Retrieved from https://www.jenkins.io/doc/book/
    3. Infrastructure as Code (IaC) Explained (2021).
    4. Understanding Kubernetes Operators (2021).
  • DevOps and the Möbius Loop

    Harnessing the Möbius Loop for a Revolutionary DevOps Process

    In the world of DevOps, continual improvement and iteration are the name of the game. The Möbius loop, with its one-sided, one-boundary surface, can serve as a vivid metaphor and blueprint for establishing a DevOps process that is both unified and infinitely adaptable. Let’s delve into the Möbius loop concept and see how it beautifully intertwines with the principles of DevOps.

    Understanding the Möbius Loop

    The Möbius loop or Möbius strip is a remarkable mathematical concept — a surface with only one side and one boundary created through a half-twist of a strip of paper that then has its ends joined. This one-sided surface represents a continuous, never-ending cycle, illustrating an ever-continuous pathway that can epitomize the unceasing cycle of development in DevOps.

    Reference: Möbius Strip – Wikipedia

    The Möbius Loop and DevOps: A Perfect Harmony

    In the ecosystem of DevOps, the Möbius loop signifies a continuous cycle where one phase naturally transitions into the next, establishing a seamless feedback loop that fosters continuous growth and development. This philosophy lies at the heart of DevOps, promoting an environment of collaboration and iterative progress.

    Reference: DevOps and Möbius Loop — A Journey to Continuous Improvement

    Crafting a Möbius Loop-Foundation DevOps Process

    Building a DevOps process based on the Möbius loop principle means initiating a workflow where each development phase fuels the next, constituting a feedback loop that constantly evolves. Here is a step-by-step guide to create this iterative and robust system:

    1. Define Objectives

    • Business Objectives: Set clear business goals and metrics.
    • User Objectives: Align the goals with user expectations.

    2. Identify Outcomes

    • Expected Outcomes: Envision the desired outcomes for business and users.
    • Metrics: Design metrics to measure the effectiveness of strategies.

    3. Discovery and Framing

    • Research: Invest time in understanding user preferences and pain points.
    • Hypothesis: Develop hypotheses to meet business and user objectives.

    4. Develop and Deliver

    • Build: Employ agile methodologies to build solutions incrementally.
    • Deploy: Use CI/CD pipelines for continuous deployment.

    Reference: Utilizing Agile Methodologies in DevOps

    5. Operate and Observe

    • Monitor: Utilize monitoring tools to collect data on system performance.
    • Feedback Loop: Establish channels to receive user feedback.

    6. Learning and Iteration

    • Analyze: Scrutinize data and feedback from the operate and observe phase.
    • Learn: Adapt based on the insights acquired and enhance the solution.

    7. Feedback and Adjust

    • Feedback: Facilitate feedback from all stakeholders.
    • Adjust: Revise goals, metrics, or the solution based on the feedback received.

    8. Loop Back

    • Iterative Process: Reiterate the process, informed by the learning from previous cycles.
    • Continuous Improvement: Encourage a mindset of perpetual growth and improvement.

    Tools to Embark on Your Möbius Loop Journey

    Leveraging advanced tools and technologies is vital to facilitate this Möbius loop-founded DevOps process. Incorporate the following tools to set a strong foundation:

    • Version Control: Git for source code management.
    • CI/CD: Jenkins, Gitlab, or ArgoCD for automating deployment.
    • Containerization and Orchestration: Podman and Kubernetes to handle the orchestration of containers.
    • Monitoring and Logging: Tools like Prometheus for real-time monitoring.
    • Collaboration Tools: Slack or Rocket.Chat to foster communication and collaboration.

    Reference: Top Tools for DevOps

    Conclusion

    Embracing the Möbius loop in DevOps unveils a path to continuous improvement, aligning with the inherent nature of the development-operations ecosystem. It not only represents a physical manifestation of the infinite loop of innovation but also fosters a system that is robust, adaptable, and user-centric. As you craft your DevOps process rooted in the Möbius loop principle, remember that you are promoting a culture characterized by unending evolution and growth, bringing closer to your objectives with each cycle.

    Feel inspired to set your Möbius loop DevOps process in motion? Share your thoughts and experiences in the comments below!

  • 90 days to success in DevOps

    Starting a new role? Maybe this is the first foray into DevOps or Platform Engineering? What is needed to “hit the ground running” in a new role? Leaders in high positions of a company typically have a “100 day rule” to prove themselves. Let’s round it out with 3 months of progress for success.

    In most enterprises on boarding new talent is typically left to the new employee. This is very unfortunate because the first 90 days of a new role will impact not only the new employee, but their immersion into the culture and their view of the company. Bottom line, in most cases it is up to the new employee to “learn the ropes” in navigating their new position.

    The first 30 days

    This month is usually the most important for everyone. The first thing a new employee needs to do is find a good mentor especially if they are not assigned one. Seek out those with institutional knowledge who knows how to navigate the company politics. Find someone who knows how the systems work, how to gain the access needed to be successful in the role. The mentor would have knowledge of “how things work” and what is seen as best practice for accomplishing the tasks at hand.

    Some things to know:

    • Who’s who in the organization? – an org chart
    • How mature are they as a development organization?
    • What are the processes to put code into production?
    • Are the processes manual or automated?
    • What is the expectation of you on a day to day basis?

    There is plenty more to uncover, but this will help to get started. Once the processes are understood and access is granted to perform the role, find some quick wins. Listen closely to where the frustrations may lie within your organization. Maybe the previous employee in this role didn’t automate certain tasks…submit a small PR to help.

    It’s important to find some quick wins for many reasons. First it helps “break the ice”. It also shows strengths. Maybe there’s a way to improve some docs. There may be some ideas brought in from previous experience to help with a particular pain point.

    The first 30 days is important to uncover the expectations of the team. Talking to stakeholders and “the customer” is important to get a big picture of what works and what doesn’t in order to find quick wins to make an impact early.

    Days 30-60

    The first 4 weeks are usually greeted with firehose sessions daily. Take a bit to digest everything. Review notes, brainstorm ideas, understand how the team and the company works. Armed with the broader knowledge about the organization, the team, and how things work at a high level it’s time to dig deeper into where the biggest impacts can be achieved.

    In this 30 day block uncover:

    • The maturity of the team?
    • What is the approval process for delivering code to production?
    • What steps are needed to approve PRs?
    • How does code flow through the various systems?
    • What amount of QA is performed?

    Find ways to help the team be more efficient. Listen to the complaints and see where possible improvements could be made. Again, quick wins are key at this stage. As a fresh face, a lot of times gaining access to otherwise inaccessible groups within the organization is usually fairly easy. Keep an ear to the ground to find ways to create impactful suggestions

    It is important to remember as people get to know a new employee the interactions have lasting impacts. Ensure there is adequate listening and relevant questions to get underneath a complaint. Avoid making off hand suggestions, but rather find some common issues. Start to tackle the common issues and socialize improvements. The key here to to avoid “calling the baby ugly”.

    Days 60-90

    This is where a new employee’s impact can accelerate. At this stage having the access needed to be successful would be complete. Hopefully there’s been a few quick wins, new co-workers are impressed, and there’s been positive impact on the team.

    Regular interaction with your leader would have been established. A solid understanding of what is expected is created and the mentor has made an impact. Knowing where to go to get answers if there is a roadblock and knowing how to avoid the “potholes in the road” is key.

    This stage is where the “rubber hits the road”. Gaining traction in the day to day and making regular impact to the business is routine at this point. This is where all of the knowledge gained in the first 60 days can be parlayed into a winning hand.

    What success looks like

    The first 3 months of any new position sets the stage for every new employee. Creating a positive impression on the team helps build credibility within the broader organization and is key to instilling the confidence needed to being successful overall.

    It may take far more than 90 days to feel comfortable with the role and that is okay. As long as there is a consistent method for learning and mistakes are not repeated the impact new employees make is usually sustainable for a long time. Make the best of it and keep track of the wins and losses for the inevitable review with “the boss”.

    You got this. Go.