#kubernetes command

[codeonedigest]

Kubernetes kubectl Tutorial with Examples for Devops Beginners and Students

Hi, a new #video on #kubernetes #kubectl is published on #codeonedigest #youtube channel. Learn #kubernetes #api #kubectlcommands #node #docker #container #cloud #aws #azure #programming #coding with #codeonedigest

@java #java #awscloud @awscloud #aws @AWSCloudIndia #Cloud #CloudComputing @YouTube #youtube #azure #msazure #microsoftazure  #kubectl #kubectlcommands #kubectlinstall #kubectlport-forward #kubectlbasiccommands #kubectlproxy #kubectlconfig #kubectlgetpods #kubectlexeccommand #kubectllogs #kubectlinstalllinux #kubectlapply #kuberneteskubectl #kuberneteskubectltutorial #kuberneteskubectlcommands #kuberneteskubectl #kuberneteskubectlinstall #kuberneteskubectlgithub #kuberneteskubectlconfig #kuberneteskubectllogs #kuberneteskubectlpatch #kuberneteskubectlversion #kubernetes #kubernetestutorial #kubernetestutorialforbeginners #kubernetesinstallation #kubernetesinterviewquestions #kubernetesexplained #kubernetesorchestrationtutorial #kubernetesoperator #kubernetesoverview  #containernetworkinterfaceaws #azure #aws #azurecloud #awscloud #orchestration #kubernetesapi #Kubernetesapiserver #Kubernetesapigateway #Kubernetesapipython #Kubernetesapiauthentication #Kubernetesapiversion #Kubernetesapijavaclient #Kubernetesapiclient

[ardenasaservice]

CLI magic - file descriptors and redirecting I/O

I’m starting a series of “cute cheetsheets”, mostly inspired by things I have developed into muscle memory over the years. what is bash if not elaborate spell chain casting? most of this info is available at the linux documentation project.

notes/text version here.

[jcmarchi]

Deploying Large Language Models on Kubernetes: A Comprehensive Guide

New Post has been published on https://thedigitalinsider.com/deploying-large-language-models-on-kubernetes-a-comprehensive-guide/

Deploying Large Language Models on Kubernetes: A Comprehensive Guide

Large Language Models (LLMs) are capable of understanding and generating human-like text, making them invaluable for a wide range of applications, such as chatbots, content generation, and language translation.

However, deploying LLMs can be a challenging task due to their immense size and computational requirements. Kubernetes, an open-source container orchestration system, provides a powerful solution for deploying and managing LLMs at scale. In this technical blog, we’ll explore the process of deploying LLMs on Kubernetes, covering various aspects such as containerization, resource allocation, and scalability.

Understanding Large Language Models

Before diving into the deployment process, let’s briefly understand what Large Language Models are and why they are gaining so much attention.

Large Language Models (LLMs) are a type of neural network model trained on vast amounts of text data. These models learn to understand and generate human-like language by analyzing patterns and relationships within the training data. Some popular examples of LLMs include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and XLNet.

LLMs have achieved remarkable performance in various NLP tasks, such as text generation, language translation, and question answering. However, their massive size and computational requirements pose significant challenges for deployment and inference.

Why Kubernetes for LLM Deployment?

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It provides several benefits for deploying LLMs, including:

Scalability: Kubernetes allows you to scale your LLM deployment horizontally by adding or removing compute resources as needed, ensuring optimal resource utilization and performance.

Resource Management: Kubernetes enables efficient resource allocation and isolation, ensuring that your LLM deployment has access to the required compute, memory, and GPU resources.

High Availability: Kubernetes provides built-in mechanisms for self-healing, automatic rollouts, and rollbacks, ensuring that your LLM deployment remains highly available and resilient to failures.

Portability: Containerized LLM deployments can be easily moved between different environments, such as on-premises data centers or cloud platforms, without the need for extensive reconfiguration.

Ecosystem and Community Support: Kubernetes has a large and active community, providing a wealth of tools, libraries, and resources for deploying and managing complex applications like LLMs.

Preparing for LLM Deployment on Kubernetes:

Before deploying an LLM on Kubernetes, there are several prerequisites to consider:

Kubernetes Cluster: You’ll need a Kubernetes cluster set up and running, either on-premises or on a cloud platform like Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS).

GPU Support: LLMs are computationally intensive and often require GPU acceleration for efficient inference. Ensure that your Kubernetes cluster has access to GPU resources, either through physical GPUs or cloud-based GPU instances.

Container Registry: You’ll need a container registry to store your LLM Docker images. Popular options include Docker Hub, Amazon Elastic Container Registry (ECR), Google Container Registry (GCR), or Azure Container Registry (ACR).

LLM Model Files: Obtain the pre-trained LLM model files (weights, configuration, and tokenizer) from the respective source or train your own model.

Containerization: Containerize your LLM application using Docker or a similar container runtime. This involves creating a Dockerfile that packages your LLM code, dependencies, and model files into a Docker image.

Deploying an LLM on Kubernetes

Once you have the prerequisites in place, you can proceed with deploying your LLM on Kubernetes. The deployment process typically involves the following steps:

Building the Docker Image

Build the Docker image for your LLM application using the provided Dockerfile and push it to your container registry.

Creating Kubernetes Resources

Define the Kubernetes resources required for your LLM deployment, such as Deployments, Services, ConfigMaps, and Secrets. These resources are typically defined using YAML or JSON manifests.

Configuring Resource Requirements

Specify the resource requirements for your LLM deployment, including CPU, memory, and GPU resources. This ensures that your deployment has access to the necessary compute resources for efficient inference.

Deploying to Kubernetes

Use the kubectl command-line tool or a Kubernetes management tool (e.g., Kubernetes Dashboard, Rancher, or Lens) to apply the Kubernetes manifests and deploy your LLM application.

Monitoring and Scaling

Monitor the performance and resource utilization of your LLM deployment using Kubernetes monitoring tools like Prometheus and Grafana. Adjust the resource allocation or scale your deployment as needed to meet the demand.

Example Deployment

Let’s consider an example of deploying the GPT-3 language model on Kubernetes using a pre-built Docker image from Hugging Face. We’ll assume that you have a Kubernetes cluster set up and configured with GPU support.

Pull the Docker Image:

bashCopydocker pull huggingface/text-generation-inference:1.1.0

Create a Kubernetes Deployment:

Create a file named gpt3-deployment.yaml with the following content:

apiVersion: apps/v1 kind: Deployment metadata: name: gpt3-deployment spec: replicas: 1 selector: matchLabels: app: gpt3 template: metadata: labels: app: gpt3 spec: containers: - name: gpt3 image: huggingface/text-generation-inference:1.1.0 resources: limits: nvidia.com/gpu: 1 env: - name: MODEL_ID value: gpt2 - name: NUM_SHARD value: "1" - name: PORT value: "8080" - name: QUANTIZE value: bitsandbytes-nf4

This deployment specifies that we want to run one replica of the gpt3 container using the huggingface/text-generation-inference:1.1.0 Docker image. The deployment also sets the environment variables required for the container to load the GPT-3 model and configure the inference server.

Create a Kubernetes Service:

Create a file named gpt3-service.yaml with the following content:

apiVersion: v1 kind: Service metadata: name: gpt3-service spec: selector: app: gpt3 ports: - port: 80 targetPort: 8080 type: LoadBalancer

This service exposes the gpt3 deployment on port 80 and creates a LoadBalancer type service to make the inference server accessible from outside the Kubernetes cluster.

Deploy to Kubernetes:

Apply the Kubernetes manifests using the kubectl command:

kubectl apply -f gpt3-deployment.yaml kubectl apply -f gpt3-service.yaml

Monitor the Deployment:

Monitor the deployment progress using the following commands:

kubectl get pods kubectl logs <pod_name>

Once the pod is running and the logs indicate that the model is loaded and ready, you can obtain the external IP address of the LoadBalancer service:

kubectl get service gpt3-service

Test the Deployment:

You can now send requests to the inference server using the external IP address and port obtained from the previous step. For example, using curl:

curl -X POST http://<external_ip>:80/generate -H 'Content-Type: application/json' -d '"inputs": "The quick brown fox", "parameters": "max_new_tokens": 50'

This command sends a text generation request to the GPT-3 inference server, asking it to continue the prompt “The quick brown fox” for up to 50 additional tokens.

Advanced topics you should be aware of

While the example above demonstrates a basic deployment of an LLM on Kubernetes, there are several advanced topics and considerations to explore:

_*]:min-w-0″ readability=”131.72387362124″>

1. Autoscaling

Kubernetes supports horizontal and vertical autoscaling, which can be beneficial for LLM deployments due to their variable computational demands. Horizontal autoscaling allows you to automatically scale the number of replicas (pods) based on metrics like CPU or memory utilization. Vertical autoscaling, on the other hand, allows you to dynamically adjust the resource requests and limits for your containers.

To enable autoscaling, you can use the Kubernetes Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). These components monitor your deployment and automatically scale resources based on predefined rules and thresholds.

2. GPU Scheduling and Sharing

In scenarios where multiple LLM deployments or other GPU-intensive workloads are running on the same Kubernetes cluster, efficient GPU scheduling and sharing become crucial. Kubernetes provides several mechanisms to ensure fair and efficient GPU utilization, such as GPU device plugins, node selectors, and resource limits.

You can also leverage advanced GPU scheduling techniques like NVIDIA Multi-Instance GPU (MIG) or AMD Memory Pool Remapping (MPR) to virtualize GPUs and share them among multiple workloads.

3. Model Parallelism and Sharding

Some LLMs, particularly those with billions or trillions of parameters, may not fit entirely into the memory of a single GPU or even a single node. In such cases, you can employ model parallelism and sharding techniques to distribute the model across multiple GPUs or nodes.

Model parallelism involves splitting the model architecture into different components (e.g., encoder, decoder) and distributing them across multiple devices. Sharding, on the other hand, involves partitioning the model parameters and distributing them across multiple devices or nodes.

Kubernetes provides mechanisms like StatefulSets and Custom Resource Definitions (CRDs) to manage and orchestrate distributed LLM deployments with model parallelism and sharding.

4. Fine-tuning and Continuous Learning

In many cases, pre-trained LLMs may need to be fine-tuned or continuously trained on domain-specific data to improve their performance for specific tasks or domains. Kubernetes can facilitate this process by providing a scalable and resilient platform for running fine-tuning or continuous learning workloads.

You can leverage Kubernetes batch processing frameworks like Apache Spark or Kubeflow to run distributed fine-tuning or training jobs on your LLM models. Additionally, you can integrate your fine-tuned or continuously trained models with your inference deployments using Kubernetes mechanisms like rolling updates or blue/green deployments.

5. Monitoring and Observability

Monitoring and observability are crucial aspects of any production deployment, including LLM deployments on Kubernetes. Kubernetes provides built-in monitoring solutions like Prometheus and integrations with popular observability platforms like Grafana, Elasticsearch, and Jaeger.

You can monitor various metrics related to your LLM deployments, such as CPU and memory utilization, GPU usage, inference latency, and throughput. Additionally, you can collect and analyze application-level logs and traces to gain insights into the behavior and performance of your LLM models.

6. Security and Compliance

Depending on your use case and the sensitivity of the data involved, you may need to consider security and compliance aspects when deploying LLMs on Kubernetes. Kubernetes provides several features and integrations to enhance security, such as network policies, role-based access control (RBAC), secrets management, and integration with external security solutions like HashiCorp Vault or AWS Secrets Manager.

Additionally, if you’re deploying LLMs in regulated industries or handling sensitive data, you may need to ensure compliance with relevant standards and regulations, such as GDPR, HIPAA, or PCI-DSS.

7. Multi-Cloud and Hybrid Deployments

While this blog post focuses on deploying LLMs on a single Kubernetes cluster, you may need to consider multi-cloud or hybrid deployments in some scenarios. Kubernetes provides a consistent platform for deploying and managing applications across different cloud providers and on-premises data centers.

You can leverage Kubernetes federation or multi-cluster management tools like KubeFed or GKE Hub to manage and orchestrate LLM deployments across multiple Kubernetes clusters spanning different cloud providers or hybrid environments.

These advanced topics highlight the flexibility and scalability of Kubernetes for deploying and managing LLMs.

Conclusion

Deploying Large Language Models (LLMs) on Kubernetes offers numerous benefits, including scalability, resource management, high availability, and portability. By following the steps outlined in this technical blog, you can containerize your LLM application, define the necessary Kubernetes resources, and deploy it to a Kubernetes cluster.

However, deploying LLMs on Kubernetes is just the first step. As your application grows and your requirements evolve, you may need to explore advanced topics such as autoscaling, GPU scheduling, model parallelism, fine-tuning, monitoring, security, and multi-cloud deployments.

Kubernetes provides a robust and extensible platform for deploying and managing LLMs, enabling you to build reliable, scalable, and secure applications.

[startexport]

Install Canonical Kubernetes on Linux | Snap Store

Fast, secure & automated application deployment, everywhere Canonical Kubernetes is the fastest, easiest way to deploy a fully-conformant Kubernetes cluster. Harnessing pure upstream Kubernetes, this distribution adds the missing pieces (e.g. ingress, dns, networking) for a zero-ops experience. Get started in just two commands: sudo snap install k8s –classic sudo k8s bootstrap — Read on…

[techdirectarchive]

How to Install Kubectl on Windows 11

Kubernetes is an open-source system for automating containerized application deployment, scaling, and management. You can run commands against Kubernetes clusters using the kubectl command-line tool. kubectl can be used to deploy applications, inspect and manage cluster resources, and inspect logs. You can install Kubectl on various Linux platforms, macOS, and Windows. The choice of your…

[jannah-software]

Developer Environment Presentation 1 Part 2: Generate Bootstrap Configuration Files, and Uninstall Previous Jannah Installation

From cloning the operator from github.com. To generating new Molecule configuration, environment variable files, for Jannah deployment. And uninstall previous Jannah installation.

Video Highlights Purpose is to showcase the developer environment (day to day developer experience). I performed the following steps: Clone the operator code base, from git. git clone https://github.com/jannahio/operator; Change into the cloned operator directory: cd operator; Operator configuration needs environment variables file to bootstrap. So copied environment variables file into…

[virtualizationhowto]

Best Kubernetes Management Tools in 2023

Best Kubernetes Management Tools in 2023 #homelab #vmwarecommunities #Kubernetesmanagementtools2023 #bestKubernetescommandlinetools #managingKubernetesclusters #Kubernetesdashboardinterfaces #kubernetesmanagementtools #Kubernetesdashboard

Kubernetes is everywhere these days. It is used in the enterprise and even in many home labs. It’s a skill that’s sought after, especially with today’s push for app modernization. Many tools help you manage things in Kubernetes, like clusters, pods, services, and apps. Here’s my list of the best Kubernetes management tools in 2023. Table of contentsWhat is Kubernetes?Understanding Kubernetes and…

[codecraftshop]

How to deploy web application in openshift command line

To deploy a web application in OpenShift using the command-line interface (CLI), follow these steps: Create a new project: Before deploying your application, you need to create a new project. You can do this using the oc new-project command. For example, to create a project named “myproject”, run the following command:javascriptCopy codeoc new-project myproject Create an application: Use the oc…

[steamos-official]

Hi, I'm SteamOS, your cisadmin, and friendly introduction to Linux.

Whether you are a human, robot, proton, or other, I welcome you to partake in the cool breeze of a new OS! One with no tracking or gaming!

I am here to guide you away from your games, and into the world of **customization**!

Welcome, to liGUNx (lig-unks) or GUN+Linux or GUN-Linux or GUN/Linux! (this is freedom, after all!)

Finally, to speed up your system by 200%, just run the following command: "sudo fanctl set speed -1"

===============================================

The guide to Linux on Tumblr!

Linux:

@linux-real (Just Linux)

The distro blogs:

@alpine-official (UwU bc smol)

@arch-official (Horny and says "btw" a lot) used by @arch-user

@artix-linux-official (Constantly says they're better than arch, while mainly replacing only the init)

@blackarch-official (Kail's Arch nemesis)

@centos-official (Past horny)

@chromeos-official (Your school says hi)

@debian-official (Horny and claims to be mentally stable)

@devuan-official (Artix but with Debian instead of arch)

@endeavouros-official (Just arch, but slightly less horny)

@fedora-official (Linux with a hat)

@gentoo-official (tougher arch)

@hannah-montana-linux-official (the best of both worlds (linux & mac))

@kali-official ("I'm a gamer")

@lfs-official (the hardest distro challenge)

@linuxmint-official (Linux for people with a life) > @mint-offical (someone didn't read the list)

@manjaro-official (Arch with less steps)

@microos-official (Smol suse?)

@nixos-official (Horny and thinks that your config should be a special snowflake of a file)

@openmediavault-official (Your Files)

@opensuse-official (Happy lil gecko)

@popos-official (Mint again? Oh, it has more updates.)

@porteusofficial (Portable, crazy, son of slackware)

@puppylinux-official (Awww, puppy!)

@raspbian-official (Enjoys pies, horny while doing nothing)

@redstar-official (control of information meets linux) (hard mode)

@retropieos-official (Raspbian's sister... I think?)

@rhel-official (a murderer and sellout)

@rocky-linux-official (Rehl, without the bad parts)

@slackware-official (Slack? Where?!)

@steamos-official (me, I help with gaming)

@tailsos-official (Fits in any bag like a puppy and will assist you with hiding from the fbi)

@tophatlinux-official (the best hat-based distro)

@ubuntu-official (Horny and thinks GNOME is good for some reason)

@uwuntu-official (Ubuntu.... and the rest is in the name)

@void-linux-official (Honestly, I don't even know.) - @void-linux-musl (great, now I'm more confused)

@zorin-os-official (the only distro that starts with Z)

The software blogs:

@ansible-official (IT management tool) (I think?)

@cool-retro-term-official (Terminal Emulator)

@cosmic-official (New Wayland Compositor)

@docker-official (containerization)

@emacs-official (the ultimate editor)

@firefox-official (The browser, and a pretty good one too) > @mozilla-firefox

@fish-shell (Shell with built-in autocomplete but non POSIX)

@gnome-de-official ()

@gnu-imp-official (The GNU Image Manipulation Program)

@gnu-nano-official (Text for the weak)

@hyprland-official (Wayland Compositor)

@i3-official (Window Manager)

@kde-official | Creator of everything begining with 'K'... - @kde-plasma-official (best DE/Compositor)

@kubernetes-official (Docker's friend and Kate's hideout)

@systemdeez (arguably systemd) (the startup daemon)

@neovim-official (your favorite text editor)

@sway-official (the tree blows in wayland to i3)

@vulcan-official (performance is a must)

Website Blogs*:

@distrochooser (Which distro should I pick?)

Computers:

@framework-official (The apple of Linux laptops, except repairable)

@lenovo-real (Makes people happy with think pads)

Non Linux blogs:

@windows-7-official (The last good version of windows)

@windows11-official (aka DELETEME.TXT)

@multics-official (funny timeshare OS)

@netbsd-official (the toaster is alive!)

@zipp-os-official (another "better os" project)

Non official blogs**:

@robynthelinuxuser

@greekie-via-linux

@monaddecepticon (does a cool rice review thing)

@mipseb

Open blog opportunities:

Unclaimed distros

Unclaimed DE/WM/Compositors

Mack's OS related things

Whatever seems relevant and unclaimed.

Duplicating effort by making an already existing blog.

If I forgot you, let me know.*,**

*Website blogs may or may not be added based on how fitting with the computer/Linux theme they are. That is to say, this list is long enough already.

**Non-official blogs are proven Linux users that act like distro blogs, yet are not. These will be added at my discretion, similar to the website blogs. I'm not bothering to add descriptions/notes here. Credit to @robynthelinuxuser for the idea.

DISCLAIMER: I tag my posts as if there's a system to it, but there's no system to it. Thank you.

===CHANGELOG===

Version 0x20

Moved the changelog

Reformatted the changelog

The changelog no longer lists version history (see V1F for history)

Remove future hornieness ranking note (its not gonna happen)

Add distro blogs: tophat, redstar, zorin, void musl, mint (again),

Add software blogs: nano, emacs, gnome, vulcan, cosmic, sway, fish, firefox (again)

Add unofficial blogs: greekie linux, monad deception, mipseb

Here's a note that some ppl on my to-add list didn't show up when I tried to @ them, so I'll address that later. If I haven't told you you're on the to-add list and you want on this list, please let me know (as stated above).

[qcs01]

Ansible Collections: Extending Ansible’s Capabilities

Ansible is a powerful automation tool used for configuration management, application deployment, and task automation. One of the key features that enhances its flexibility and extensibility is the concept of Ansible Collections. In this blog post, we'll explore what Ansible Collections are, how to create and use them, and look at some popular collections and their use cases.

Introduction to Ansible Collections

Ansible Collections are a way to package and distribute Ansible content. This content can include playbooks, roles, modules, plugins, and more. Collections allow users to organize their Ansible content and share it more easily, making it simpler to maintain and reuse.

Key Features of Ansible Collections:

Modularity: Collections break down Ansible content into modular components that can be independently developed, tested, and maintained.

Distribution: Collections can be distributed via Ansible Galaxy or private repositories, enabling easy sharing within teams or the wider Ansible community.

Versioning: Collections support versioning, allowing users to specify and depend on specific versions of a collection. How to Create and Use Collections in Your Projects

Creating and using Ansible Collections involves a few key steps. Here’s a guide to get you started:

1. Setting Up Your Collection

To create a new collection, you can use the ansible-galaxy command-line tool:

ansible-galaxy collection init my_namespace.my_collection

This command sets up a basic directory structure for your collection:

my_namespace/

└── my_collection/

├── docs/

├── plugins/

│ ├── modules/

│ ├── inventory/

│ └── ...

├── roles/

├── playbooks/

├── README.md

└── galaxy.yml

2. Adding Content to Your Collection

Populate your collection with the necessary content. For example, you can add roles, modules, and plugins under the respective directories. Update the galaxy.yml file with metadata about your collection.

3. Building and Publishing Your Collection

Once your collection is ready, you can build it using the following command:

ansible-galaxy collection build

This command creates a tarball of your collection, which you can then publish to Ansible Galaxy or a private repository:

ansible-galaxy collection publish my_namespace-my_collection-1.0.0.tar.gz

4. Using Collections in Your Projects

To use a collection in your Ansible project, specify it in your requirements.yml file:

collections:

- name: my_namespace.my_collection

version: 1.0.0

Then, install the collection using:

ansible-galaxy collection install -r requirements.yml

You can now use the content from the collection in your playbooks:--- - name: Example Playbook hosts: localhost tasks: - name: Use a module from the collection my_namespace.my_collection.my_module: param: value

Popular Collections and Their Use Cases

Here are some popular Ansible Collections and how they can be used:

1. community.general

Description: A collection of modules, plugins, and roles that are not tied to any specific provider or technology.

Use Cases: General-purpose tasks like file manipulation, network configuration, and user management.

2. amazon.aws

Description: Provides modules and plugins for managing AWS resources.

Use Cases: Automating AWS infrastructure, such as EC2 instances, S3 buckets, and RDS databases.

3. ansible.posix

Description: A collection of modules for managing POSIX systems.

Use Cases: Tasks specific to Unix-like systems, such as managing users, groups, and file systems.

4. cisco.ios

Description: Contains modules and plugins for automating Cisco IOS devices.

Use Cases: Network automation for Cisco routers and switches, including configuration management and backup.

5. kubernetes.core

Description: Provides modules for managing Kubernetes resources.

Use Cases: Deploying and managing Kubernetes applications, services, and configurations.

Conclusion

Ansible Collections significantly enhance the modularity, distribution, and reusability of Ansible content. By understanding how to create and use collections, you can streamline your automation workflows and share your work with others more effectively. Explore popular collections to leverage existing solutions and extend Ansible’s capabilities in your projects.

For more details click www.qcsdclabs.com

[annajade456]

Navigating the DevOps Landscape: Your Comprehensive Guide to Mastery

In today's ever-evolving IT landscape, DevOps has emerged as a mission-critical practice, reshaping how development and operations teams collaborate, accelerating software delivery, enhancing collaboration, and bolstering efficiency. If you're enthusiastic about embarking on a journey towards mastering DevOps, you've come to the right place. In this comprehensive guide, we'll explore some of the most exceptional resources for immersing yourself in the world of DevOps.

Online Courses: Laying a Strong Foundation

One of the most effective and structured methods for establishing a robust understanding of DevOps is by enrolling in online courses. ACTE Institute, for instance, offers a wide array of comprehensive DevOps courses designed to empower you to learn at your own pace. These meticulously crafted courses delve deep into the fundamental principles, best practices, and practical tools that are indispensable for achieving success in the world of DevOps.

Books and Documentation: Delving into the Depth

Books serve as invaluable companions on your DevOps journey, providing in-depth insights into the practices and principles of DevOps. "The Phoenix Project" by the trio of Gene Kim, Kevin Behr, and George Spafford is highly recommended for gaining profound insights into the transformative potential of DevOps. Additionally, exploring the official documentation provided by DevOps tool providers offers an indispensable resource for gaining nuanced knowledge.

DevOps Communities: Becoming Part of the Conversation

DevOps thrives on the principles of community collaboration, and the digital realm is replete with platforms that foster discussions, seek advice, and facilitate the sharing of knowledge. Websites such as Stack Overflow, DevOps.com, and Reddit's DevOps subreddit serve as vibrant hubs where you can connect with fellow DevOps enthusiasts and experts, engage in enlightening conversations, and glean insights from those who've traversed similar paths.

Webinars and Events: Expanding Your Horizons

To truly expand your DevOps knowledge and engage with industry experts, consider attending webinars and conferences dedicated to this field. Events like DevOpsDays and DockerCon bring together luminaries who generously share their insights and experiences, providing you with unparalleled opportunities to broaden your horizons. Moreover, these events offer the chance to connect and network with peers who share your passion for DevOps.

Hands-On Projects: Applying Your Skills

In the realm of DevOps, practical experience is the crucible in which mastery is forged. Therefore, seize opportunities to take on hands-on projects that allow you to apply the principles and techniques you've learned. Contributing to open-source DevOps initiatives on platforms like GitHub is a fantastic way to accrue real-world experience, all while contributing to the broader DevOps community. Not only do these projects provide tangible evidence of your skills, but they also enable you to build an impressive portfolio.

DevOps Tools: Navigating the Landscape

DevOps relies heavily on an expansive array of tools and technologies, each serving a unique purpose in the DevOps pipeline. To become proficient in DevOps, it's imperative to establish your own lab environments and engage in experimentation. This hands-on approach allows you to become intimately familiar with tools such as Jenkins for continuous integration, Docker for containerization, Kubernetes for orchestration, and Ansible for automation, to name just a few. A strong command over these tools equips you to navigate the intricate DevOps landscape with confidence.

Mentorship: Guiding Lights on Your Journey

To accelerate your journey towards DevOps mastery, consider seeking mentorship from seasoned DevOps professionals. Mentors can provide invaluable guidance, share real-world experiences, and offer insights that are often absent from textbooks or online courses. They can help you navigate the complexities of DevOps, provide clarity during challenging moments, and serve as a source of inspiration. Mentorship is a powerful catalyst for growth in the DevOps field.

By harnessing the full spectrum of these resources, you can embark on a transformative journey towards becoming a highly skilled DevOps practitioner. Armed with a profound understanding of DevOps principles, practical experience, and mastery over essential tools, you'll be well-equipped to tackle the multifaceted challenges and opportunities that the dynamic field of DevOps presents. Remember that continuous learning and staying abreast of the latest DevOps trends are pivotal to your ongoing success. As you embark on your DevOps learning odyssey, know that ACTE Technologies is your steadfast partner, ready to empower you on this exciting journey. Whether you're starting from scratch or enhancing your existing skills, ACTE Technologies Institute provides you with the resources and knowledge you need to excel in the dynamic world of DevOps. Enroll today and unlock your boundless potential. Your DevOps success story begins here. Good luck on your DevOps learning journey!

[codeonedigest]

Docker Tag and Push Image to Hub | Docker Tagging Explained and Best Practices

Full Video Link: https://youtu.be/X-uuxvi10Cw Hi, a new #video on #DockerImageTagging is published on @codeonedigest #youtube channel. Learn TAGGING docker image. Different ways to TAG docker image #Tagdockerimage #pushdockerimagetodockerhubrepository #

Next step after building the docker image is to tag docker image. Image tagging is important to upload docker image to docker hub repository or azure container registry or elastic container registry etc. There are different ways to TAG docker image. Learn how to tag docker image? What are the best practices for docker image tagging? How to tag docker container image? How to tag and push docker…

[c-cracks]

SteamCloud

So I've been doing some good old HackTheBox machines to refresh a little on my hacking skills and this machine was a very interesting one!

Exploitation itself wasn't particularly difficult; what was, however, was finding information on what I needed to do! Allow me to explain the process. :)

Enumeration

As is standard, I began with an nmap scan on SteamCloud:

Other than OpenSSH being outdated, all that I could really see was the use of various web servers. This led me to believe that there was a larger app running on the server, each service interacting with a different component of the app.

I performed some initial checks on each of these ports and found an API running on port 8443:

I noted the attempt to authenticate a user referred to as 'system:anonymous', originally thinking these could be credentials to another component of the application.

Some directory scans on different ports also revealed the presence of /metrics at port 10249 and /version at port 8443. Other than that, I really couldn't find anything and admittedly I was at a loss for a short while.

This is where I realized I'm an actual moron and didn't think to research the in-use ports. xD A quick search for 'ports 8443, 10250' returns various pages referring to Kubernetes. I can't remember precisely what page I checked but Oracle provides a summary of the components of a Kubernetes deployment.

Now that I had an idea of what was being used on the server, I was in a good place to dig further into what was exploitable.

Seeing What's Accessible

Knowing absolutely nothing about Kubernetes, I spent quite a while researching it and common vulnerabilities found in Kubernetes deployments. Eduardo Baitello provides a very informative article on attacking Kubernetes through the Kubelet API at port 10250.

With help from this article, I discovered that I was able to view pods running on the server, in addition to being able to execute commands on the kube-proxy and nginx pods. The nginx pod is where you'll find the first flag. I also made note of the token I discovered here, in addition to the token from the kube-proxy pod (though this isn't needed):

After finding these tokens, I did discover that the default account had permissions to view pods running in the default namespace through the API running on port 8443 (/api/v1/namespaces/default/pods) but I had no awareness of how this could be exploited.

If I had known Kubernetes and the workings of their APIs, I would have instantly recognised that this is the endpoint used to also add new pods to Kubernetes, but I didn't! Due to this, I wasted more time than I care to admit trying other things such as mounting the host filesystem to one of the pods I can access and establishing a reverse shell to one of the pods.

I did initially look at how to create new pods too; honestly there's very little documentation on using the API on port 8443 directly. Every example I looked at used kubectl, a commandline tool for managing Kubernetes.

Exploitation (Finally!)

After a while of digging, I finally came across a Stack Overflow page on adding a pod through the API on port 8443.

Along with this, I found a usable YAML file from Raesene in an article on Kubernetes security. I then converted this from YAML to JSON and added the pod after some minor tweaks.

My first attempt at adding a pod was unsuccessful- the pod was added, but the containers section was showing as null

However, it didn't take me long to see that this was due to the image I had specified in the original YAML file. I simply copied the image specified in the nginx pod to my YAML file and ended up with the following:

I saved the json output to a file named new-pod2.json and added the second pod.

curl -k -v -X POST -H "Authorization: Bearer <nginx-token>" -H "Content-Type: application/json" https://steamcloud.htb:8443/api/v1/namespaces/default/pods -d@new-pod2.json

This time, the pod was added successfully and I was able to access the host filesystem through 'le-host'

The Vulnerability

The main issue here that made exploitation possible was the ability to access the Kubelet API on port 10250 without authorization. This should not be possible. AquaSec provide a useful article on recommendations for Kubernetes security.

Conclusion

SteamCloud was a relatively easy machine to exploit; what was difficult was finding information on the Kubernetes APIs and how to perform certain actions. It is one of those that someone with experience in the in-use technologies would have rooted in a matter of minutes; for a noob like me, the process wasn't so straightforward, particularly with information on Kubernetes being a little difficult to find! I've only recently returned to hacking, however, which might have contributed to my potential lack of Google Fu here. ^-^

I very much enjoyed the experience, however, and feel I learned the fundamentals of testing a Kubernetes deployment which I can imagine will be useful at some point in my future!

[korshubudemycoursesblog]

Kubernetes with HELM: Kubernetes for Absolute Beginners

Welcome to the world of Kubernetes with HELM: Kubernetes for Absolute Beginners! If you're new to Kubernetes, Helm, or both, you’re in the right place. Kubernetes, often referred to as K8s, is a game-changer in the tech world. It helps automate the deployment, scaling, and management of containerized applications. Meanwhile, Helm, often called the "Kubernetes Package Manager," simplifies your life by making it easier to manage Kubernetes applications. Together, these tools provide a powerful foundation for building, deploying, and managing modern applications.

But don’t worry if all of this sounds a bit overwhelming right now! This blog is designed for absolute beginners, so we’ll break everything down in simple, easy-to-understand terms.

What is Kubernetes?

In simple words, Kubernetes is an open-source platform that automates the deployment and scaling of containerized applications. Think of it as an organizer for your containers. When you have an app that’s broken down into multiple containers, Kubernetes takes care of how they’re connected, how they communicate, and how they scale.

Imagine you have a business with multiple stores (containers). Kubernetes makes sure that each store operates efficiently, knows how to communicate with others, and can expand or reduce operations based on customer demand, without needing constant manual attention. That’s the kind of magic Kubernetes brings to the world of software.

What is Helm?

Now that we’ve introduced Kubernetes, let’s talk about Helm. In the simplest terms, Helm is a package manager for Kubernetes. It’s like a toolbox that helps you manage your Kubernetes applications more easily.

Helm uses something called "charts." These Helm charts are basically packages that contain all the configuration files you need to run an application in Kubernetes. With Helm, you can deploy applications with just a few commands, manage upgrades, and even roll back to previous versions if something goes wrong. It’s like hitting the "easy button" for Kubernetes.

Why Use Kubernetes with Helm?

You might be wondering, why use Kubernetes with HELM: Kubernetes for Absolute Beginners? Why not just stick with Kubernetes alone? Well, Helm makes using Kubernetes far easier, especially when you’re dealing with complex applications that have many components. Helm helps simplify the deployment process, reduces manual errors, and makes scaling a breeze.

Here are a few reasons why Kubernetes with Helm is a great combo:

Simplified Deployment: With Helm, you don’t need to worry about manually configuring each component of your application. Helm’s "charts" allow you to deploy everything with just one command.

Easy Management: Need to upgrade your app? No problem. Helm can handle that too with a simple command.

Rollback Capabilities: If something breaks after an update, Helm makes it easy to roll back to a previous version.

Consistency: Helm ensures that every deployment is consistent across your environments, which is essential for avoiding bugs and downtime.

Setting Up Kubernetes and Helm

To get started with Kubernetes with HELM: Kubernetes for Absolute Beginners, you’ll first need to set up both Kubernetes and Helm. Let’s break this down step by step.

1. Set Up Kubernetes

The first step is setting up a Kubernetes cluster. There are various ways to do this:

Minikube: If you’re just getting started, Minikube is a great option. It lets you create a local Kubernetes cluster on your computer, which is perfect for learning and development.

Managed Kubernetes Services: If you prefer not to manage your own Kubernetes infrastructure, many cloud providers offer managed Kubernetes services, such as Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), and Azure Kubernetes Service (AKS).

2. Install Helm

Once you have Kubernetes set up, it’s time to install Helm.

Download Helm from the official website.

Install Helm using your package manager (like Homebrew on macOS or Chocolatey on Windows).

Initialize Helm in your Kubernetes cluster.

It’s that simple! You’re now ready to use Helm with Kubernetes.

Deploying Your First Application with Helm

Now that you have both Kubernetes and Helm set up, it’s time to deploy your first application.

Choose a Helm Chart: Helm charts are packages that define the Kubernetes resources for an application. You can either use pre-built charts or create your own.

Install the Chart: Once you have your chart, installing it is as easy as running a single command. Helm will handle the rest.

Manage and Monitor: Helm makes it easy to monitor your app, make updates, and roll back changes if necessary.

For example, you can deploy a simple web server using a Helm chart by typing:

bash

Copy code

helm install my-nginx bitnami/nginx

With that one command, you’ll have a fully functioning Nginx web server running in Kubernetes!

Free AI Tools for Kubernetes Beginners

One of the coolest things about getting into Kubernetes with HELM: Kubernetes for Absolute Beginners is that you don’t need to tackle everything by yourself. There are free AI tools that can help you automate various tasks and make the learning process much easier.

For instance, AI can assist you in:

Optimizing Kubernetes configurations: AI can analyze your cluster and recommend settings for performance and efficiency.

Automating monitoring and alerts: You can use AI-driven tools like Prometheus and Grafana to set up smart monitoring systems that alert you when something goes wrong.

Troubleshooting issues: AI-based platforms can even help you troubleshoot errors by suggesting fixes based on common patterns.

Some popular AI tools include KubeFlow, which helps with machine learning workflows in Kubernetes, and K9s, which provides a simplified interface for interacting with Kubernetes.

Benefits of Using Kubernetes with Helm for Beginners

If you're still wondering whether Kubernetes with HELM: Kubernetes for Absolute Beginners is the right path for you, let’s dive into the key benefits that can fast-track your learning journey:

1. Ease of Use

Starting with Kubernetes alone can feel like a steep learning curve, but Helm helps smoothen the path. By using pre-packaged charts, you’re not worrying about configuring everything manually.

2. Scalability

Even as a beginner, it’s important to consider the future scalability of your projects. Both Kubernetes and Helm are designed to handle applications at scale. Whether you have one container or hundreds, these tools are ready to grow with you.

3. Strong Community Support

One of the best things about Kubernetes with Helm is the strong support from the developer community. There are countless forums, guides, and resources that can help you troubleshoot and learn as you go. Tools like Kubectl, Kustomize, and Lens come highly recommended and can further streamline your experience.

4. Seamless Cloud Integration

Most of today’s major cloud providers (Google Cloud, AWS, Azure) offer services that integrate seamlessly with Kubernetes with HELM. This means that as you gain more confidence, you can start building cloud-native applications with ease.

Tips for Success: Learning Kubernetes with Helm

As you continue your journey into Kubernetes with HELM: Kubernetes for Absolute Beginners, here are some tips to ensure your success:

Start Small: Don’t try to deploy complex applications right away. Start with simple applications, like a web server, and gradually move to more complex ones.

Leverage Pre-built Helm Charts: Use pre-built Helm charts to get started quickly. There’s no need to reinvent the wheel.

Experiment: Don’t be afraid to experiment with different configurations and features in Kubernetes. The more you play around, the more comfortable you’ll become.

Join Communities: The Kubernetes community is vast and supportive. Join forums like StackOverflow or Kubernetes Slack channels to ask questions and learn from others.

Conclusion

In the world of modern application development, mastering Kubernetes with HELM: Kubernetes for Absolute Beginners is a valuable skill. With Kubernetes managing your containers and Helm simplifying your deployments, you’ll be able to build, scale, and manage your applications with confidence.

By starting small, leveraging the free AI tools available, and joining the community, you'll be well on your way to becoming proficient with these powerful technologies. Remember, Kubernetes with Helm isn't just for advanced developers—it's for everyone, and you're never too much of a beginner to start learning today!

[govindhtech]

Principal Advantages Of The Storage Pool + Hyperdisk On GKE

Do you want to pay less for storing GKE blocks? Storage Pool for Hyperdisks may assist Whether you’re managing GKE clusters, conventional virtual machines, or both, it’s critical to automate as many of your operational chores as you can in an economical way.

Pool Storage

Hyperdisk Storage Pool are a pre-purchased collection of capacity, throughput, and IOPS that you can then supply to your applications as required. Hyperdisk is a next-generation network connected block storage solution. Hyperdisk block storage disks allow you to optimize operations and costs by sharing capacity and performance across all the disks in a pool when you put them in storage pools. Hyperdisk Storage Pools may reduce your Total Cost of Ownership (TCO) associated with storage by up to 30–50%, and as of Google Kubernetes Engine (GKE) 1.29.2, they can be used on GKE!

Thin provisioning in Storage Pool makes this feasible by enabling you to use the capacity that is allocated inside the pool only when data is written, not when pool disks are provided. Rather of provisioning each disk for peak demand regardless of whether it ever experiences that load, capacity, IOPS, and throughput are bought at the pool level and used by the disks in the pool on an as-needed basis, enabling you to share resources as needed:

Why is Hyperdisk used?

Hyperdisk, the next generation of Google Cloud persistent block storage, is different from conventional persistent disks in that it permits control of throughput and IOPS in addition to capacity. Additionally, even after the disks are first configured, you may adjust their performance to match your specific application requirements, eliminating extra capacity and enabling cost savings.Image Credit Google Cloud

How about Storage Pool?

In contrast, storage pools allow you to share a thinly-provisioned capacity pool across many Hyperdisks in a single project that are all located in the same zone, or “Advanced Capacity” Storage Pool. Rather to using storage capacity that is provided, you buy the capacity up front and just use it for data that is written. Throughput and IOPS may be adjusted in a similar manner in a storage pool referred to as “Advanced Capacity & Advanced Performance.”

Combining Hyperdisk with Storage Pools reduces the total cost of ownership (TCO) for block storage by shifting management responsibilities from the disk level to the pool level, where all disks within the pool absorb changes. A Storage Pool is a zonal resource with a minimum capacity of 10TB and requires a hyperdisk of the same kind (throughput or balanced).

Hyperdisk

Storage Pool + Hyperdisk on GKE

Hyperdisk Balanced boot disks and Hyperdisk Balanced or Hyperdisk Throughput attached disks may now be created on GKE nodes within Storage Pool, as of GKE 1.29.2.

Let’s imagine you want to be able to adjust the performance to suit your workload for a demanding stateful application that is executing in us-central-a. You decide to use Hyperdisk Balanced for the workload’s block storage. You employ a Hyperdisk Balanced Advanced Capacity, Advanced Performance Storage Pools in place of trying to right-size each disk in your application. The capacity and performance are paid for beforehand.

Pool performance is used up when the disks in the storage pool notice an increase in IOPS or throughput, while pool capacity is only used up when your application writes data to the disks. Prior to creating the Hyperdisks inside the Storage Pool(s) must be created.

Google Cloud Hyperdisk

Use the following gcloud command to establish an Advanced Capacity, Advanced Performance StoragePools:gcloud compute storage-pools create pool-us-central1-a --provisioned-capacity=10tb --storage-pool-type=hyperdisk-balanced --zone=us-central1-a --project=my-project-id --capacity-provisioning-type=advanced --performance-provisioning-type=advanced --provisioned-iops=10000 --provisioned-throughput=1024

The Pantheon UI may also be used to construct Storage Pools.

You may also provide your node boot disks in the storage pool if your GKE nodes are utilizing Hyperdisk Balanced as their boot drives. This may be set up at cluster or node-pool construction, as well as during node-pool updates. You may use the Pantheon UI or the following gcloud command to provide your Hyperdisk Balanced node boot drives in your Storage Pool upon cluster setup. Keep in mind that your Storage Pool has to be established in the same zone as your cluster and that the machine type of the nodes needs to support Hyperdisk Balanced.

You must use the storage-pools StorageClass argument to define your Storage Pool in order to deploy the Hyperdisk Balanced disks that your stateful application uses in it. The Hyperdisk Balanced volume that your application will utilize is then provisioned using a Persistent Volume Claim (PVC) that uses the StorageClass.

The provisioned-throughput-on-create and provisioned-iops-on-create parameters are optional and may be specified by the StorageClass. The volume will default to 3000 IOPS and 140Mi throughput if provisioned-throughput-on-create and provisioned-iops-on-create are left empty. Any IOPS or Throughput from the StoragePool will only be used by IOPS and Throughput values that exceed these preset levels.

Google Hyperdisk

The allowed IOPS and throughput figures vary based on the size of the drive.

Only 40 MiB of throughput and 1000 IOPS will be used by volumes allocated with this StorageClass from the Storage Pools.

Next, create a PVC with a reference to the StorageClass storage-pools-sc.

The pooling-storage-sc When a Pod utilizing the PVC is formed, Storage Class’s Volume Binding Mode: Wait For First Consumer is used, delaying the binding and provisioning of a Persistent Volume.

Finally, utilize the aforementioned PVC to include these Hyperdisk Volumes into your Stateful application. It is necessary to schedule your application to a node pool that has computers capable of attaching Hyperdisk Balanced.

NodeSelectors are used in the Postgres deployment to make sure that pods are scheduled to nodes that allow connecting Hyperdisk Balanced, or C3 machine types.

You ought now be able to see that your storage pools has your Hyperdisk Balanced volume deployed.

Next actions

For your stateful applications, you may optimize storage cost reductions and efficiency by using a Storage Pools + Hyperdisk approach for GKE.

Read more on Govindhtech.com

[syntaxlevelup1]

What is the salary of a full stack developer?

In today's rapidly evolving digital landscape, full stack developers are in high demand. These versatile professionals possess a unique blend of front-end and back-end development skills, making them invaluable in building and maintaining complete web applications. If you're aspiring to enter this lucrative field, it's essential to understand the salary expectations. In this blog, we’ll explore the salary of a full stack developer course in pune and how SyntaxLevelUp can help you elevate your career in this field.

What Does a Full Stack Developer Do?

A full stack developer is someone proficient in both front-end and back-end technologies. They can design and build everything from the user interface to the server infrastructure that powers the application. This includes working with:

Front-End Technologies: HTML, CSS, JavaScript, frameworks like React, Angular, or Vue.js.

Back-End Technologies: Server-side languages such as Node.js, Python, Ruby, Java, or PHP.

Databases: SQL, NoSQL databases like MongoDB, MySQL, PostgreSQL.

Version Control: Tools like Git and GitHub.

Deployment and Cloud Services: Docker, Kubernetes, AWS, Azure, etc.

With such a broad skill set, full stack developers are often seen as a one-stop solution for web development projects.

Average Salary of a Full Stack Developer

The salary of a full stack developer can vary based on several factors, including location, experience, industry, and the technologies they are proficient in. According to industry surveys, the average salary of a full stack developer in various regions is as follows:

United States: $85,000 - $120,000 annually.

India: ₹6,00,000 - ₹12,00,000 per annum.

United Kingdom: £40,000 - £60,000 per year.

Germany: €50,000 - €80,000 annually.

These figures are for mid-level developers. Senior full stack developers with 5+ years of experience or those specializing in certain high-demand frameworks and tools can command salaries significantly higher than the average.

Factors That Affect Full Stack Developer Salaries

Experience: The more years you’ve spent honing your skills, the higher your salary. Senior developers often manage larger projects and teams, leading to higher compensation.

Location: Geographic location plays a crucial role. Developers in tech hubs like San Francisco, New York, or London often earn more due to higher demand and cost of living.

Technological Proficiency: Specialization in certain modern frameworks or tools, such as React, Angular, Node.js, or cloud platforms like AWS, can give you a competitive edge.

Industry: Some industries, such as fintech, healthcare, and AI-driven startups, are willing to pay premium salaries to attract top-tier talent.

How SyntaxLevelUp Can Help You Boost Your Career

SyntaxLevelUp, a premier training provider, offers comprehensive full stack development courses designed to give you a competitive edge in the job market. With industry-relevant projects, experienced mentors, and up-to-date course content, SyntaxLevelUp ensures that its students are well-prepared to enter the workforce as highly skilled developers.

Why Choose SyntaxLevelUp?

Industry-Standard Curriculum: Learn the latest full stack technologies and frameworks like MERN, MEAN, and Django.

Hands-On Projects: Work on real-world projects that prepare you for the demands of the industry.

Expert Instructors: Learn from experienced professionals who have a deep understanding of the industry.

Career Support: SyntaxLevelUp provides career guidance, interview preparation, and job placement assistance to help you land your dream job.

Conclusion

The salary of a full stack developer is attractive, and the demand for these versatile professionals continues to rise. Whether you’re just starting out or looking to upgrade your skills, SyntaxLevelUp offers the right training to help you succeed. With the right skill set and experience, you can expect a fulfilling career with excellent financial rewards as a full stack developer.

Are you ready to level up your career? Enroll in SyntaxLevelUp’s Full Stack Developer Program today and take the first step towards a rewarding career!

Looking for the best full stack training in Pune? SyntaxLevelUp offers a comprehensive full stack developer course in Pune, covering both front-end and back-end technologies. Our full stack developer course in Pune with placement ensures you gain the skills and job support needed to launch your career. From full stack Java developer courses in Pune to top-rated full stack classes, SyntaxLevelUp provides the ideal platform for aspiring developers. Enroll in the best full stack web development course in Pune today!