#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!

[devopssentinel]

Hiring talented DevOps engineers is like assembling a team of superheroes. They're the ones who keep your systems running smoothly, automate the impossible, and empower your developers to deliver amazing software at warp speed. But when it comes to determining the right salary for these tech wizards, things can get a little murky. It's like navigating a labyrinth, with twists and turns, hidden traps, and the constant fear of running into a minotaur (or worse, an underpaid and disgruntled engineer). Fear not, intrepid leader! This guide will help you navigate the DevOps salary labyrinth, find your way to the center, and emerge victorious with a team of happy, well-compensated, and highly motivated engineers. The X-Factors of DevOps Compensation Determining a fair salary for a DevOps engineer isn't as simple as throwing darts at a board (though that might be less stressful). It's a complex equation with a multitude of variables. First and foremost, experience reigns supreme. A junior engineer, fresh out of the coding dojo, will naturally command a lower salary than a seasoned veteran with battle scars from countless deployments and a track record of slaying production bugs. Then there's the skillset. Think of it like a superhero's superpower. A DevOps engineer with expertise in cloud security is like a cybernetic shield, protecting your systems from malicious attacks. A Kubernetes guru is like a master orchestrator, conducting a symphony of containers with grace and precision. These specialized skills, especially those in high demand, can significantly impact salary expectations. Location, location, location! Just like real estate, DevOps salaries can vary wildly depending on where you're hiring. Tech hubs like San Francisco or New York, with their vibrant tech scenes and fierce competition for talent, typically command higher salaries than other regions. The size and industry of your company also play a role. Large enterprises and companies in high-growth industries often have deeper pockets and are willing to pay top dollar to attract the best and brightest. And finally, there's the ever-present force of supply and demand. The demand for skilled DevOps engineers often outstrips supply, creating upward pressure on salaries. It's like trying to buy concert tickets for a sold-out show – you might have to pay a premium to get in the door. The Art of Salary Sleuthing Before you even think about making an offer, put on your detective hat and do some serious salary sleuthing. Consult industry salary surveys and reports from reputable sources like Glassdoor, Indeed, and Robert Half. These are your treasure maps, providing valuable benchmarks and insights into current salary trends. Next, do some competitive analysis. Peek behind the curtain and see what your competitors are offering for similar roles. This helps you stay competitive, avoid lowballing candidates, and prevent your top talent from being poached by rival companies. Don't forget the power of online resources. Websites like LinkedIn Salary and Payscale can provide valuable data on DevOps salaries in your specific location and industry. It's like having a crystal ball that shows you the future of salary expectations. Beyond the Benjamins: The Perks and the Promise Remember, salary is just one piece of the compensation puzzle. Think of it as the foundation of a house – it's essential, but it's not the only thing that makes a home. Consider the total compensation package you're offering. Benefits like health insurance, retirement plans, and paid time off are like the walls and roof of your compensation house, providing security and comfort. For startups or high-growth companies, equity options like stock options or profit sharing can be a powerful incentive. It's like offering your engineers a piece of the pie, giving them a stake in the company's success. And don't underestimate the power of perks. Flexible work arrangements, professional development budgets, and company-sponsored events are like the furniture and decorations of your compensation house, adding comfort and personality. The Negotiation Tango: A Delicate Dance Salary negotiations are like a tango – a delicate dance that requires finesse, balance, and a willingness to listen. Be prepared to discuss salary expectations openly and honestly, building trust and rapport with your candidates. Transparency is key. Be upfront about your salary range and the factors that influence it. This shows respect for the candidate's time and avoids any surprises down the road. Be flexible within your budget. If you find a candidate with exceptional skills or experience, be willing to stretch your budget to secure their talent. Remember, a top-notch DevOps engineer can bring immense value to your organization. And don't just talk numbers; emphasize the value the candidate brings to your organization and how their contributions align with your goals. Help them see the bigger picture and understand how their role contributes to the company's success. The Human Touch: Beyond the Dollars and Cents While salary is important, it's not the only thing that matters. Remember the human element in this equation. Consider the candidate's overall fit with your team culture and their long-term career goals. Assess whether their values and work style align with your team's culture. A good cultural fit is like the mortar that holds your team together, ensuring a cohesive and productive environment. Highlight opportunities for professional development and career growth within your organization. Ambitious candidates want to know that they have a path for advancement and that their skills will be valued and nurtured. And sometimes, the most valuable incentives aren't monetary. Offer challenging projects, opportunities for leadership, or a voice in shaping the company's technology direction. These non-monetary incentives can be incredibly motivating and foster a sense of ownership and purpose. Finding the right salary for new DevOps hires is a multifaceted challenge. It requires research, flexibility, and a focus on the total compensation package. But most importantly, it requires a human touch. By understanding the factors that influence salaries, conducting thorough research, engaging in open and honest negotiations, and considering the candidate's individual needs and aspirations, you can attract top talent, build a strong and motivated DevOps team, and navigate the salary labyrinth with confidence. Read the full article

[devopssentinel2000]

Hiring talented DevOps engineers is like assembling a team of superheroes. They're the ones who keep your systems running smoothly, automate the impossible, and empower your developers to deliver amazing software at warp speed. But when it comes to determining the right salary for these tech wizards, things can get a little murky. It's like navigating a labyrinth, with twists and turns, hidden traps, and the constant fear of running into a minotaur (or worse, an underpaid and disgruntled engineer). Fear not, intrepid leader! This guide will help you navigate the DevOps salary labyrinth, find your way to the center, and emerge victorious with a team of happy, well-compensated, and highly motivated engineers. The X-Factors of DevOps Compensation Determining a fair salary for a DevOps engineer isn't as simple as throwing darts at a board (though that might be less stressful). It's a complex equation with a multitude of variables. First and foremost, experience reigns supreme. A junior engineer, fresh out of the coding dojo, will naturally command a lower salary than a seasoned veteran with battle scars from countless deployments and a track record of slaying production bugs. Then there's the skillset. Think of it like a superhero's superpower. A DevOps engineer with expertise in cloud security is like a cybernetic shield, protecting your systems from malicious attacks. A Kubernetes guru is like a master orchestrator, conducting a symphony of containers with grace and precision. These specialized skills, especially those in high demand, can significantly impact salary expectations. Location, location, location! Just like real estate, DevOps salaries can vary wildly depending on where you're hiring. Tech hubs like San Francisco or New York, with their vibrant tech scenes and fierce competition for talent, typically command higher salaries than other regions. The size and industry of your company also play a role. Large enterprises and companies in high-growth industries often have deeper pockets and are willing to pay top dollar to attract the best and brightest. And finally, there's the ever-present force of supply and demand. The demand for skilled DevOps engineers often outstrips supply, creating upward pressure on salaries. It's like trying to buy concert tickets for a sold-out show – you might have to pay a premium to get in the door. The Art of Salary Sleuthing Before you even think about making an offer, put on your detective hat and do some serious salary sleuthing. Consult industry salary surveys and reports from reputable sources like Glassdoor, Indeed, and Robert Half. These are your treasure maps, providing valuable benchmarks and insights into current salary trends. Next, do some competitive analysis. Peek behind the curtain and see what your competitors are offering for similar roles. This helps you stay competitive, avoid lowballing candidates, and prevent your top talent from being poached by rival companies. Don't forget the power of online resources. Websites like LinkedIn Salary and Payscale can provide valuable data on DevOps salaries in your specific location and industry. It's like having a crystal ball that shows you the future of salary expectations. Beyond the Benjamins: The Perks and the Promise Remember, salary is just one piece of the compensation puzzle. Think of it as the foundation of a house – it's essential, but it's not the only thing that makes a home. Consider the total compensation package you're offering. Benefits like health insurance, retirement plans, and paid time off are like the walls and roof of your compensation house, providing security and comfort. For startups or high-growth companies, equity options like stock options or profit sharing can be a powerful incentive. It's like offering your engineers a piece of the pie, giving them a stake in the company's success. And don't underestimate the power of perks. Flexible work arrangements, professional development budgets, and company-sponsored events are like the furniture and decorations of your compensation house, adding comfort and personality. The Negotiation Tango: A Delicate Dance Salary negotiations are like a tango – a delicate dance that requires finesse, balance, and a willingness to listen. Be prepared to discuss salary expectations openly and honestly, building trust and rapport with your candidates. Transparency is key. Be upfront about your salary range and the factors that influence it. This shows respect for the candidate's time and avoids any surprises down the road. Be flexible within your budget. If you find a candidate with exceptional skills or experience, be willing to stretch your budget to secure their talent. Remember, a top-notch DevOps engineer can bring immense value to your organization. And don't just talk numbers; emphasize the value the candidate brings to your organization and how their contributions align with your goals. Help them see the bigger picture and understand how their role contributes to the company's success. The Human Touch: Beyond the Dollars and Cents While salary is important, it's not the only thing that matters. Remember the human element in this equation. Consider the candidate's overall fit with your team culture and their long-term career goals. Assess whether their values and work style align with your team's culture. A good cultural fit is like the mortar that holds your team together, ensuring a cohesive and productive environment. Highlight opportunities for professional development and career growth within your organization. Ambitious candidates want to know that they have a path for advancement and that their skills will be valued and nurtured. And sometimes, the most valuable incentives aren't monetary. Offer challenging projects, opportunities for leadership, or a voice in shaping the company's technology direction. These non-monetary incentives can be incredibly motivating and foster a sense of ownership and purpose. Finding the right salary for new DevOps hires is a multifaceted challenge. It requires research, flexibility, and a focus on the total compensation package. But most importantly, it requires a human touch. By understanding the factors that influence salaries, conducting thorough research, engaging in open and honest negotiations, and considering the candidate's individual needs and aspirations, you can attract top talent, build a strong and motivated DevOps team, and navigate the salary labyrinth with confidence. Read the full article

[trendingitcourses]

GCP DevOps Training in Hyderabad | Best GCP DevOps Training

GCP DevOps Training: Your Roadmap to a High-Paying Career

GCP DevOps- As the demand for cloud infrastructure and automation grows, acquiring expertise in Google Cloud Platform (GCP) DevOps can set you on a rewarding and high-paying career path. GCP DevOps Training offers a comprehensive skill set combining cloud proficiency with a strong foundation in automation, application deployment, and maintenance. This article explores how GCP DevOps Training can pave the way to career success, the certifications and skills essential for GCP, and how specialized training in cities like Hyderabad can elevate your competitive edge.

Why Choose GCP DevOps Training?

GCP DevOps Training in Hyderabad provides hands-on experience in cloud operations, continuous integration, continuous delivery (CI/CD), and infrastructure management. As a DevOps engineer skilled in GCP, you are equipped to bridge the gap between development and operations teams, ensuring smooth application deployment and maintenance. By mastering GCP DevOps, you develop expertise in managing containerized applications, monitoring systems, and utilizing tools like Kubernetes and Jenkins for seamless workflow automation.

Key Components of GCP DevOps Training

1. Understanding Cloud Infrastructure

One of the core components of GCP DevOps Training is learning about the GCP cloud infrastructure. Understanding its fundamentals is essential to developing scalable and secure cloud applications. Training focuses on using GCP’s Compute Engine, Kubernetes Engine, and App Engine for deploying, managing, and scaling applications. A solid foundation in cloud infrastructure also includes learning about storage solutions such as Cloud Storage and Bigtable, which are integral for data management in applications.

2. Mastering Automation and CI/CD

Automation is at the heart of GCP DevOps Training, allowing organizations to achieve continuous integration and delivery. Through GCP DevOps Certification Training, you gain hands-on experience in setting up and managing CI/CD pipelines with GCP’s Cloud Build, enabling you to automate code testing, integration, and deployment. This helps in minimizing errors, speeding up the release cycle, and ensuring a seamless experience for end users. Additionally, automated monitoring and logging systems, such as Cloud Monitoring and Cloud Logging, enable efficient troubleshooting and proactive maintenance of applications.

3. Proficiency in Containerization with Kubernetes

Containerization, specifically with Kubernetes, is a fundamental skill in GCP DevOps. As applications grow complex, deploying them in containers ensures consistent behavior across environments. Kubernetes streamlines the deployment, scaling, and administration of applications that are containerized. GCP DevOps Certification Training emphasizes the use of GKE (Google Kubernetes Engine) to run and manage applications effectively. With these skills, you can efficiently manage microservices, making you a valuable asset for any tech company.

Benefits of GCP DevOps Certification Training

Completing GCP DevOps Certification Training comes with multiple advantages that extend beyond technical proficiency. Here’s why pursuing GCP DevOps Training is a smart move:

Enhanced Employability: GCP DevOps Certification Training is recognized by leading tech companies, positioning you as a valuable candidate. With cloud proficiency on the rise, companies seek skilled DevOps professionals who can operate within the GCP ecosystem.

Career Flexibility: GCP DevOps skills are transferable across industries, allowing you to work in sectors like finance, healthcare, e-commerce, and technology. This flexibility is especially beneficial if you plan to switch industries while maintaining a stable career.

High Salary Potential: Certified DevOps engineers, especially those with expertise in GCP, command high salaries. According to industry reports, DevOps professionals earn competitive pay, with compensation often growing after achieving certifications.

Career Growth and Advancement: As a certified GCP DevOps professional, you are equipped for advanced roles such as DevOps Architect, Cloud Solutions Architect, or Lead DevOps Engineer. GCP DevOps Training in Hyderabad provides you with the skills to grow, placing you in a favorable position to pursue leadership roles.

Competitive Edge: DevOps professionals with certifications stand out to employers. Pursuing GCP DevOps Certification Training gives you a competitive edge, enabling you to showcase expertise in both GCP services and best practices in automation, containerization, and CI/CD.

Choosing the Right GCP DevOps Training in Hyderabad

Selecting quality GCP DevOps Training in Hyderabad is crucial to mastering DevOps on the Google Cloud Platform. Hyderabad, as a major IT hub, offers diverse training programs with experienced instructors who provide hands-on guidance. By choosing a reputable training provider, you can participate in immersive labs, real-world projects, and simulations that build practical skills in GCP DevOps. Look for training programs that offer updated curriculum, certification preparation, and support from industry mentors.

Preparing for GCP DevOps Certification Exams

GCP DevOps Certification Training often includes preparation for certification exams, such as the Google Cloud Certified – Professional DevOps Engineer exam. By passing these exams, you validate your proficiency in using GCP services, managing CI/CD pipelines, and securing application infrastructure. Most GCP DevOps Training programs offer mock exams, which help familiarize you with exam formats, enabling you to succeed in official certification exams with confidence.

Conclusion

The journey to a high-paying career in GCP DevOps starts with the right training and certification. With GCP DevOps Training, you acquire the essential skills to manage cloud operations, automate workflows, and ensure robust application performance. GCP DevOps Certification Training validates your expertise, making you a standout candidate in a competitive job market. Whether you’re a beginner or an experienced IT professional, investing in GCP DevOps Training in Hyderabad can be a transformative step toward a rewarding career.

GCP DevOps expertise is in high demand, making it a valuable skill set for any IT professional. By choosing quality training, building your skills in automation and cloud infrastructure, and acquiring GCP certifications, you can position yourself for sustained career success. Embrace the opportunity to specialize in GCP DevOps, and you’ll be prepared to take on challenging roles in a dynamic field.

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