#git project setup

[codeonedigest]

What is GitHub? Github tutorial and project setup, Reference guide to GitHub Commands

Hi, a new #video on #GitHub #tutorial and #Project #Setup is published on @codeonedigest #youtube channel. Learn, what is GitHub in 1 minute. #Gitprojecttutorial #gitproject #gitprojectupload #gitprojectsetup #gitprojectmanagement #gitprojectexample #git

GitHub is a code hosting platform for version control and collaboration. It lets people work together on projects from anywhere. GitHub is easy to use, it supports both public and private repositories, it is free of cost. In this video we will learn, how to start working with GITHUB repository? How to create GITHUB project? How to initialize GITHUB repository? How to add files in GITHUB? How to…

[fujowebdev]

After an intense 30-day campaign, The Fujoshi Guide to Web Development has officially raised $18,039. 🎉

We're immensely grateful for your trust and support, and look forward to the journey to come 💜

As we salute this feat, let's now cheer for those who made it happen.

✨Final Spotlight: The #FujoGuide Team ✨

You’ve already met many of our amazing contributors (if you need a refresher on our spotlights, find them at the end), but today we want to focus on those behind the scenes and highlight the incredible team that made our success possible.

While the hot men did a lot of heavy lifting in attracting (😘) people to our cause, a lot goes into successfully hitting ambitious goals like ours: planning, organization, research, marketing, and writing (omg so much writing).

It's impossible to accurately list everyone's contributions. This Kickstarter campaign, pulled off in only 2 months, is the collective work of an incredible team of people who came together with a mission: make programming accessible to fandom in the most hilarious way possible.

As project lead @essential-randomness found out, setting up a Kickstarter is a daunting task, even more than it seems to those who’ve never run one. Thankfully the @bobaboard community came together to her aid and managed to pull off the seemingly impossible.

Together with Slogbait, who did incredibly vital work, like setting up deadlines, budget, & rewards, a team of Kickstarter connoisseurs researched similar projects to understand what would go into making our campaign a success: the incredible @a-brilliant-loser & @elfwreck.

With data on our side, we started building. A fundamental part of this process was the research on expenses and manufacturers for the book and potential merch. Here, Candle provided us with his expertise in publishing and helped us understand the best options available.

At the same time, @tempural, @mizunotic helped us untangle the questions surrounding merchandise. What is popular with our audience? What are the best manufacturers around? What beautiful items can we afford to deliver to our amazing supporters?

Next, it was time for the practical setup: project lead @essential-randomness provided the first draft of our amazing campaign story, while @enigmalea and Heidi whipped it into the beautiful shape that will forever be immortalized on our Kickstarter page.

To decorate our riveting narrative, we needed graphics to match. As is April 1st tradition, project lead @essential-randomness got together with trusty pinch-hit designer CatBathingSun to create character cards and other campaign graphics, including the ones you saw on our socials.

But no Kickstarter campaign page is complete without captivating reward tiers. @a-brilliant-loser led this charge, coming up with our creative "GIT"-based naming and taking care of other copy needs. On the art side, @cmdonovann contributed the Boba-tans decorating our limited tiers.

Launching the campaign was a lot of work on its own. But that work, while important, could not have pushed us to these incredible heights without our incredible social media and marketing team, who worked tirelessly this whole month to reach and inspire old and new supporters.

While many people worked on this, we want to first shout out the amazing work of @owlpockets, who whipped our content calendar into shape, taught us social media strategy (and alt-texting), and helped us navigate the unknown waters of relentless promotion.

Then we have project lead @essential-randomness who spent an uncountable number of hours devising our marketing strategy and writing the threads that entertained you during this campaign.

As she herself writes, "May she now finally get to go back to coding, instead!"

But she could not have done it without collaborators like @elendraug, @enigmalea, and, again, @owlpockets, who painstakingly reviewed and improved her drafts and provided immense support throughout this extremely intense, exhilarating, month-long tour de force.

There are many other contributions-smaller, yes, but not less integral to our success. Like @thebiballerina, who first helped us wrap our heads around the inner workings of social media strategy, or @cmdonovann who kept our Discord updated on the week's happenings.

And then all the people who supported us with small (and big) bits here and there, like our very own Secret Final Boss, @thunder-the-ranger-wolf, Tovanish, @madgastronomer, Cante, Mantra, @PamuyaBlue, @playerprophet, @ignitiondork, @nianeyna, @codeargent and so many more.

Our artists, obviously, like our art director @brokemycrown; character designers @sgt-spank, @mappapapa, and @ymkse_art; illustrators @ikam177, Ererifan915, @kiwipon, @catter-bug, @admiralexclipse, and @tempural. Sensitivity readers, like @admiralexclipse and @angelfeast.

And then our project organizers, like our aforementioned anonymous contributor and @enigmalea who worked tirelessly on logistics and wrangling and showed up repeatedly to pick up all the dangling bits and pieces that a project this ambitious inevitably has.

Last but definitely not least, our project lead and director @essential-randomness who brought-and kept!-everyone together, oversaw and pitched in on every aspect of the project, designed our notepads (she's proud of them), and provided the initial investment that made this a reality.

And with this, our last spotlight comes to a close. We'd pitch the Kickstarter link here, but we're done with that for now. ;)

Thank you everyone for your incredible support. This campaign blew all of our expectations out of the water, and we'll forever treasure this experience.

We'll be back soon with more updates on the road from here. We're incredibly excited to get to work on delivering what we promised, and also pumped to get a small vacation break before we do :)

Find our previous spotlights here ⬇️

Spotlight on Localhost HQ

Spotlight on Browserland

Zine Demo

Backer Rewards

Thank you all, The FujoGuide Team

[tap-tap-tap-im-in]

A friend of mine asked me recently to detail my Linux setup, and after thinking about it for a bit, I realized that this is essentially a personality quiz for the Linux users I thought I would detail it here as well.

I no longer have a desktop computer at all. I have two older generation "gaming" laptops and three Raspberry Pis. I'm going to go through in the order I got them:

Laptop #1:

[Purchased New in 2016] Acer ROG 7th Gen i7, 16GB Ram, nVidia 1050Ti Mobile, Internal 1TB HDD, external 2TB HDD

This was originally a windows laptop when I got it back in 2016, but in 2021 I was tired of the long windows boot times on the the HDD and was much more familiar with Linux due to several years experience doing webserver admin work.

I use Ubuntu LTS as my base. It's easy, it's well supported, it's well documented, and the official repos have just about everything I could need. The only thing I've really had to add myself is the repo for i3, but we'll get to that in a bit. I also chose Ubuntu because I already had my first two Raspberry pis, and both were running Raspbian, so using a debian based kernal meant that it wouldn't be much of a change when ssh'ing into them.

That said, I've never really liked the default Ubuntu desktop. Gnome3 is slow and full of too many effects that don't look especially nice but are still heavy to execute. Instead I loaded up KDE plasma. You can download Kubuntu and have them to the setup for you, but I did it the hard way because I found out about Plasma after installing Ubuntu and didn't want to start from scratch.

My plasma desktop looks like this:

Of my two laptops, this one is in the best shape. It's the one that I usually take with me on trips. With the dedicated GPU it can do some light gaming (it did heavier gaming on windows, but due to emulation layers the performance is just a little worse these days, Linux gaming isn't perfect), the screen hinge has never been an issue, and it's on the lighter side of gaming laptops (which is not to say that it's light). For that reason, I often find myself actually using it on my lap, in airports, at people's houses, on my own couch typing this up.

For this reason, I started looking into ways to better keep my hands on the keyboard, rather than having to drift down to the track pad, which is my least favorite part of this laptop. During that research I discovered i3. If you're not familiar i3 is a Linux Desktop Environment that is entirely keyboard driven. https://i3wm.org/

To be fair, it's less of a desktop environment and more of a keyboard driven window manager, as it doesn't have a "desktop" per se. Instead when you log into it, you simply get a black status bar at the bottom of the screen. It doesn't even black out the login screen, so if you don't know what to look for, you might think the whole thing has hung. But, the big benefit of this is that the whole thing is lighting fast for a DE. It doesn't waste any resources on effects or really anything that you don't need. But it's really nice for window tiling and task switching without having to get the mouse involved. This is great for productivity (if you're into that), but it's also just convenient for working on a gaming laptop, which might be balanced such that if you take your hands off of it, it might topple off your lap.

This laptop is my primary project computer. It has all my git repos and scripts for doing things like renewing my website's ssl certs. I also run game servers on it for Minecraft. I'm probably going to spin up a Valheim server on it in the near future too. Especially now that the process has improved somewhat.

Raspberry Pi #1:

[Gifted New in 2016] Raspberry Pi 3b, 4GB RAM, 32GB SD card

This one is my oldest RPi. It's had a lot of roles through the years, including an early version of the vogon media server during initial development in 2020. It's run headless Raspbian for a good three or four years now. Currently it's configured as a web server/php scripted web crawler and a pi-hole DNS server. My router currently refuses to use it as a DNS server without bringing the whole network down, but I will on occasion manually switch devices to it when I'm running especially ad-ridden applications.

There's not too much to say about this one. It's stable, I almost never have problems with it. I frequently use it for things that I want running in the background because they'll take too long and I don't want them blocking up one of my other computers.

Laptop #2

[Gifted Used in 2020] Asus Predator 7th Gen i7, 16GB Ram, nVidia 1080 Mobile, 2 internal 256GB SSDs, External 2TB HDD

This one runs windows 10 still. I use this primarily for gaming. The screen hinge is an absolute joke, and replacing it involves replacing the entire screen bezel assembly, which I can absolutely do, but is such a pain that I haven't gotten around to it in the 3 years I've owned this laptop.

There's nothing really special about this one, other than that when both laptops are at my desk, I use a KVM switch to swap my external monitor, keyboard, and trackball between the two computers.

Raspberry Pi #2:

[Gifted New in 2020/21] Raspberry Pi 4b, 4GB Ram, 16GB SD card, 2 120GB USB Sticks, External 2TB HDD

This is my media server. I got it for Christmas 2020 (or 2021, I don't actually remember which because 2020 was a hard hard year). It runs Rasbian, the full OS, with the desktop environment disabled from booting via the command line. It runs PHP 8.2, MariaDB, Apache2, and MiniDLNA to serve the content via my Vogon Media Server.

If you can't tell from the above storage, I'm running the USB ports well past the power delivery they are rated for. The webserver and OS are on the internal storage, so functionally this just means that sometimes the media disappears. I need to build a migration script to put the contents of the two USB sticks on the external storage, as there is more than enough room, and if I can put the HDD in an enclosure with dedicated power, that will solve the issue. But that's at least a hundred dollars of expense, and since the server only has 1, maybe two users at a time, we've been limping along like this for a few years now.

Raspberry Pi #3:

[Purchased New in 2023] Raspberry Pi 4b, 8GB Ram, 16GB SD card

This is the newest Pi. Work gave me a gift card as a bonus for a project recently, so after weighing the pros and cons of getting a VR headset, I settled on setting up a retro gaming tv box. Currently it's running Batocero Linux and loaded up with classic game roms up through the PSX. Though, I would really like to use it as a tv client for the media server. I've upgraded the devices in the living room recently, and there's no longer a dedicated web browser we can use without hooking up one of our laptops. I've got a spare 128GB SD card in the office, so I'm strongly considering getting a wireless mouse and keyboard and setting it up to dual boot between Batocero (which is convenient because it can be navigated with just a controller), and Raspbian. I think I'd set Batocero as the default in Grub, and then if I want to use Raspbian I'd need to have the keyboard handy anyway.

Maybe I'll get one of those half-sized keyboards with the trackpad built in.

Speaking of controllers. I use an 8BitDo Pro 2 controller, and I've been super happy with it since purchase: https://www.8bitdo.com/pro2/

So that's the setup. I have entirely too many computers for any one person, but I included the dates when I got them to show that a number of these have been around for a long time, and that part of the reason I have so many now is that I've put a lot of time into ongoing maintenance and repurposing.

If you've read this far, I'd love to hear about your setups. You don't have to reblog this, but please tag me if you detail yours.

[visioncodekdp]

Updating the Git user in current machine

When we are doing projects we might need to switch between users like our friend might use our machine and wanted to push their code to their project. Even at the initial setup of the git project we need to provide our user credentials. So, as we already know Git always have a easy solution  Open Terminal --> Goto the path of specific project --> type git 

If git is already there then we will get git help documentation . If not,  git init --->  git config --global user.email "keerthidevipriya@gmail.com" --> git config --global user.name "keerthidevipriya"

Those are my details. Please replace them with yours dear

[quackquackcey]

Ch. 3: Hedgehogs, Honey, & Hazelnut-Covered Strawberries

Written for @hdcandyheartsfest day 3 prompt: handmade. Many thanks to my beta @wqtson​! 💛  

Start from beginning on AO3 here, or click the #fic: HHHS tag.

Summary:

A chance meeting—or is it a setup?—leads to the start of a relationship filled with buttery baked goods, sweet smelling flowers, and hedgehogs.~ 🌹🦔

“I am not filling in for one of your hedgehogs again!” Draco pointed his whisk at Luna. “You set me up last time!”

Luna gave a little shrug like it didn’t matter. “I’m simply telling you that Harry’s coming to visit solely for you. If you don’t want to fill in again, shall I tell Harry that Ormr’s had an unfortunate accident?”

“You— What?!” Draco spluttered. “You can’t just tell him that I bloody croaked! Just say…just say Ormr’s feeling under the weather or something.”

“But if Ormr won’t be making an appearance again, then I should just say he’s gone,” Luna told him as she walked out of Draco’s patisserie-bakery kitchen. “It’s not good to lead people on.” She paused for a moment, blue eyes piercing through Draco. “And, you know, Harry’s glamour—he’s set it so only people he trusts to some extent can see through it.”

And then she left.

Draco just stood there for a moment, dumbfounded, until he concluded Luna was just screwing with him, which then made him want to scream in frustration, but instead he muttered so many curses under his breath that Greg, in charge of the bread products, peered over from where he was baking the bread and suggested he take a break.

So Draco did.

And ended up spending the rest of the day across the street nibbling on fruits and being petted by Potter. 

He was in too deep, despite it being only the second time Potter had visited him, and the proof of that lay in the little gift Potter had brought him today—a tiny, knitted hedgehog sweater.

Hand-knitted.

By Potter.

Because apparently Potter knew to knit.

Draco would’ve thought this sort of thing wasn’t allowed in a hedgehog café, but Luna, that git—“Normally I don’t allow gifts for the hedgehogs, but seeing as you’re Ormr’s only visitor and I know you don’t have ill intentions, I’ll let it slide,” she said.

And to make matters worse, when Harry asked her what ‘Ormr’ meant, the conversation topic somehow fell on him.

“Hm, so it means ‘dragon’ in Old Norse?” mused Harry. “Like Draco. I bet he and Ormr would get along.”

“His patisserie-bakery’s right across the street,” said Luna. “Have you been? His baked goods and pastries are simply delicious.”

Harry paused his petting, and Draco looked up at him to see, to his surprise, a conflicted, somewhat disappointed expression. 

“I’ve heard. I want to visit, but….” Harry sighed and stirred his coffee. “Well, we didn’t part on great terms after the trial.”

Luna pulled up a chair, as if they weren’t discussing him right in front of him. “What happened? That was years ago, Harry. Surely the grudge between you two wouldn’t last that long.”

“It’s not that— It’s just, I asked him if he needed any help, erm, financial-wise or job-wise, because, well, you know—”

“Oh, Harry,” said Luna in an amused, knowing tone. “Did you tell him why you were offering help?”

“I thought it was obvious,” muttered Harry. “But he said ‘thank you for defending me, but I’m not a charity project,’ and that I didn’t need to keep an eye on him in case he turns Death Eater again because ‘the Ministry’s already doing that, thank you very much,’ and he looked hurt, and that was the end of that.”

Luna looked at Potter in that oddly penetrating gaze that Draco knew well, like she was staring into their soul. “Why did you ask him that?” she asked. “You two were never on good terms in your school years, and I’ve never heard you offer help to anyone else after the war, not like that.”

Draco startled—from the way everyone talked about Potter and all the charities Potter donated to, he’d thought Potter would be helping people left and right.

“…It just seemed like the odds were stacked against him,” said Potter after a moment. “So I thought I could help even those odds. He…” Potter hesitated. “You know what he went through in that manor.”

“You saw, didn’t you?” asked Luna. It was more a statement than a question. “Through your link with Voldemort.”

It was the first Draco had heard of Potter being linked with Voldemort, and he couldn’t help but wonder how much the wizarding world was kept in the dark regarding what exactly Potter had gone through during the war.

For one, the fact that Potter had sacrificed himself to save everyone, and died. Actually died.

And he sounded like he knew what Draco had done in that manor…. But that was impossible. He’d never told anyone, and even Luna only knew because she’d been on the receiving end once—

“I saw Voldemort Crucio-ing him,” Potter said softly. “Forcing him to Crucio others.”

Draco’s breath choked in his throat.

“It’s not something like pity, or charity,” continued Potter. “I just…. He wasn’t a Death Eater, Luna. He was a prisoner. And I guess I felt like he understood what it was like to have Voldemort in your head, and, I don’t know, I thought maybe we could go for drinks and be friends or something….” He trailed off and huffed out a wry laugh. “It sounds dumb saying it out loud. But I feel like if I walked into his shop, he might misunderstand and think I’m there to check in on him, so I get Dean and Seamus to buy me stuff when they go sometimes.” He grinned, eyes sparkling. “I like his tiramisu the most. Oh, and those dome-shaped white chocolate and caramel tarts with piping on the edge. Do you know what I’m talking about?”

And then Luna replied with her favourites, and it just devolved into a conversation about all of Draco’s desserts that they liked, and Draco just sat there in a daze, overwhelmed by the surreal scene playing in front of him. 

[labexio]

Linux for Developers: Essential Tools and Environments for Coding

For developers, Linux is not just an operating system—it's a versatile platform that offers a powerful array of tools and environments tailored to coding and development tasks. With its open-source nature and robust performance, Linux is a preferred choice for many developers. If you're looking to get the most out of your Linux development environment, leveraging resources like Linux Commands Practice Online, Linux Practice Labs, and Linux Online Practice can significantly enhance your skills and productivity.

The Linux Advantage for Developers

Linux provides a rich environment for development, featuring a wide range of tools that cater to various programming needs. From command-line utilities to integrated development environments (IDEs), Linux supports an extensive ecosystem that can streamline coding tasks, improve efficiency, and foster a deeper understanding of system operations.

Essential Linux Tools for Developers

Text Editors and IDEs: A good text editor is crucial for any developer. Linux offers a variety of text editors, from lightweight options like Vim and Nano to more feature-rich IDEs like Visual Studio Code and Eclipse. These tools enhance productivity by providing syntax highlighting, code completion, and debugging features.

Version Control Systems: Git is an indispensable tool for version control, and its integration with Linux is seamless. Using Git on Linux allows for efficient version management, collaboration, and code tracking. Tools like GitHub and GitLab further streamline the development process by offering platforms for code sharing and project management.

Package Managers: Linux distributions come with powerful package managers such as apt (Debian/Ubuntu), yum (CentOS/RHEL), and dnf (Fedora). These tools facilitate the installation and management of software packages, enabling developers to quickly set up their development environment and access a wide range of libraries and dependencies.

Command-Line Tools: Mastery of Linux commands is vital for efficient development. Commands like grep, awk, and sed can manipulate text and data effectively, while find and locate assist in file management. Practicing these commands through Linux Commands Practice Online resources helps sharpen your command-line skills.

Containers and Virtualization: Docker and Kubernetes are pivotal in modern development workflows. They allow developers to create, deploy, and manage applications in isolated environments, which simplifies testing and scaling. Linux supports these technologies natively, making it an ideal platform for container-based development.

Enhancing Skills with Practice Resources

To get the most out of Linux, practical experience is essential. Here’s how you can use Linux Practice Labs and Linux Online Practice to enhance your skills:

Linux Practice Labs: These labs offer hands-on experience with real Linux environments, providing a safe space to experiment with commands, configurations, and development tools. Engaging in Linux Practice Labs helps reinforce learning by applying concepts in a controlled setting.

Linux Commands Practice Online: Interactive platforms for practicing Linux commands online are invaluable. They offer scenarios and exercises that simulate real-world tasks, allowing you to practice commands and workflows without the need for a local Linux setup. These exercises are beneficial for mastering command-line utilities and scripting.

Linux Online Practice Platforms: Labex provide structured learning paths and practice environments tailored for developers. These platforms offer a variety of exercises and projects that cover different aspects of Linux, from basic commands to advanced system administration tasks.

Conclusion

Linux offers a powerful and flexible environment for developers, equipped with a wealth of tools and resources that cater to various programming needs. By leveraging Linux Commands Practice Online, engaging in Linux Practice Labs, and utilizing Linux Online Practice platforms, you can enhance your development skills, streamline your workflow, and gain a deeper understanding of the Linux operating system. Embrace these resources to make the most of your Linux development environment and stay ahead in the ever-evolving tech landscape.

[korshubudemycoursesblog]

What Is Jenkins, and Why Should You Care?

Before diving into the course itself, let’s take a moment to understand Jenkins and why it’s so crucial in today’s software development ecosystem. Jenkins is an open-source automation tool that plays a pivotal role in DevOps practices, specifically in building CI/CD pipelines. But what exactly does that mean?

CI/CD, which stands for Continuous Integration and Continuous Delivery, is the process that allows developers to continuously merge code and automate the testing, building, and deployment processes. This is where Jenkins shines. It automates these workflows, ensuring faster deployment, improved code quality, and most importantly, more efficient teams.

For anyone serious about a career in DevOps, mastering Jenkins is non-negotiable. It’s the tool that brings together all the moving parts of software development and delivery, ensuring smooth transitions from one stage to the next.

Why Enroll in The Complete Jenkins DevOps CI/CD Pipeline Bootcamp?

So, what makes The Complete Jenkins DevOps CI/CD Pipeline Bootcamp stand out from other online courses? It’s simple – this course is built to take you from beginner to advanced, making it ideal for both newcomers and those who already have some experience but are looking to solidify their knowledge.

Here are some reasons why this course is worth your time:

Comprehensive Curriculum: You’ll learn everything from setting up Jenkins to building and managing complex CI/CD pipelines. This isn’t just a basic introduction; it’s a deep dive into everything Jenkins can do.

Hands-On Projects: The best way to learn is by doing, and this course is packed with hands-on projects. You’ll get real-world experience setting up Jenkins pipelines, integrating tools like Git, Docker, and Kubernetes, and automating tasks that would otherwise be time-consuming.

Career Growth: DevOps professionals are in high demand. Completing this course and mastering Jenkins will set you apart from other job seekers, increasing your chances of landing a high-paying role in a top tech company.

What Will You Learn in The Complete Jenkins DevOps CI/CD Pipeline Bootcamp?

Here’s a breakdown of what you’ll cover in this course:

1. Jenkins Installation and Setup

The first step is getting Jenkins up and running. You’ll learn how to install Jenkins on your local machine, configure it for different environments, and get a solid grasp of its user interface.

2. Jenkins Plugins

One of Jenkins’ greatest strengths is its flexibility, thanks to the vast library of plugins available. You’ll explore essential plugins for CI/CD pipelines, version control systems like Git, and integration with tools like Docker and Kubernetes.

3. Building Your First Jenkins Pipeline

Once you’ve set up Jenkins and configured the necessary plugins, it’s time to build your first pipeline. This is where Jenkins automates the process of integrating code from various developers, testing it, and deploying it to production.

4. Integrating Jenkins with Git

Jenkins and Git are a match made in DevOps heaven. Git serves as your version control system, and Jenkins automates the process of pulling code, running tests, and building applications. In this section, you’ll learn how to integrate Jenkins with Git for a seamless CI/CD pipeline.

5. Docker and Jenkins: A Perfect Combination

Containerization has become a key part of modern software development, and Docker is leading the way. In this course, you’ll learn how to integrate Docker with Jenkins to create containerized applications and deploy them efficiently.

6. Automating Tests with Jenkins

One of the main benefits of CI/CD pipelines is the ability to automate tests. Jenkins makes this process incredibly simple. You’ll learn how to configure automated testing within your pipeline, ensuring that only high-quality code makes it to production.

7. Deploying with Kubernetes

As cloud-native applications become more popular, Kubernetes has emerged as the go-to solution for managing containers. You’ll discover how to deploy Jenkins pipelines on Kubernetes, taking your DevOps skills to the next level.

8. Monitoring and Scaling Jenkins Pipelines

Once you’ve set up a pipeline, the work doesn’t stop there. Jenkins pipelines need to be monitored and scaled according to the needs of your project. You’ll explore best practices for monitoring pipelines and scaling Jenkins to handle larger workloads.

How Can Free AI Help You in DevOps?

As AI continues to evolve, it’s becoming an essential tool in virtually every industry, and DevOps is no exception. There are several free AI tools and platforms that can enhance your Jenkins experience by automating tasks, predicting failures in your pipeline, and even optimizing code. AI can help you identify potential bottlenecks in your pipeline before they become significant issues. Some tools even suggest improvements to your configurations, making your pipeline more efficient over time.

Using AI tools alongside Jenkins is a game-changer for developers and DevOps professionals. Not only does it streamline your workflow, but it also helps you deliver better-quality products faster.

Why CI/CD Pipelines Are the Future of Software Development

In today’s fast-paced software development environment, the traditional methods of development and deployment just don’t cut it anymore. Companies need faster, more efficient ways to deliver updates and new features, and that’s where CI/CD pipelines come in.

By automating the process of building, testing, and deploying code, CI/CD pipelines significantly reduce the time it takes to get from development to production. More importantly, they ensure that the code being deployed is of the highest quality, reducing the risk of bugs and errors making it into production.

Who Should Take The Complete Jenkins DevOps CI/CD Pipeline Bootcamp?

This course is perfect for:

Aspiring DevOps Engineers: If you’re looking to break into the field, this course will give you the foundational skills you need to succeed.

Developers Looking to Automate: If you’re a developer tired of repetitive tasks, learning Jenkins will free up your time to focus on more critical aspects of development.

Project Managers and Tech Leads: Understanding how CI/CD pipelines work is essential for managing modern software projects.

Why Now Is the Perfect Time to Master Jenkins

The demand for DevOps professionals has never been higher, and companies are looking for individuals who can not only manage CI/CD pipelines but also bring new ideas and tools to the table. By completing The Complete Jenkins DevOps CI/CD Pipeline Bootcamp, you’ll be equipped with the knowledge and experience to stand out in a crowded job market.

Moreover, the integration of free AI tools into DevOps workflows is becoming more prevalent. By learning both Jenkins and these cutting-edge AI tools, you’ll stay ahead of the curve and ensure you’re using the latest technologies to your advantage.

Conclusion

In summary, The Complete Jenkins DevOps CI/CD Pipeline Bootcamp is your one-stop shop for mastering the key skills needed in today’s DevOps world. From setting up Jenkins and building pipelines to integrating with Git, Docker, and Kubernetes, this course covers it all. Plus, you’ll gain valuable insights into how free AI tools can enhance your DevOps workflows.

By the end of the course, you’ll be ready to build, test, and deploy software more efficiently than ever before. Whether you’re just starting out or looking to advance your career, this course is the perfect way to take your DevOps skills to the next level.

Now’s the time to dive into the world of Jenkins and CI/CD pipelines. Ready to accelerate your career? The Complete Jenkins DevOps CI/CD Pipeline Bootcamp is here to help you achieve that goal!

[spark-solution055]

The 7 Stages of Software Development: A Comprehensive Overview

software development process into seven key stages, explaining each in detail, and providing actionable insights to help you understand how developers move from idea to final product. By the end of this article, you will have a clear understanding of the steps required to build a software project and how to apply them in practice.

1. Planning and Requirement Analysis

The first and arguably the most crucial stage of software development is planning and requirement analysis. At this point, stakeholders (including clients, project managers, and developers) collaborate to define the project’s objectives.

During this stage:

Requirement gathering: Developers must work closely with stakeholders to collect requirements. This ensures that the final product meets business objectives and solves the intended problem.

Feasibility study: A feasibility study is conducted to analyze whether the project is viable, both technically and financially.

Resource allocation: Teams also decide on the resources required, including manpower, technology, and tools.

The planning phase sets the foundation for the entire software development process. Failing to gather all necessary requirements or miscalculating the project's scope can lead to costly revisions later.

2. Design

Once the requirements are fully understood, the design stage begins. This is where the structure and architecture of the software are planned. There are two key aspects of design:

High-level design (HLD): The overall system architecture is laid out, including data flow diagrams, system architecture, and technology stacks.

Low-level design (LLD): Detailed designs for individual components, including algorithms, workflows, and interfaces, are created.

This stage ensures that the developers have a roadmap for how the software will function and be integrated. It also defines the technologies and tools that will be used to develop the product.

3. Implementation and Coding

This is where the actual coding of the software takes place. The design created in the previous phase is transformed into executable code. Developers write code in programming languages suited to the project’s requirements, which may include Python, Java, C++, or JavaScript, among others.

Version control: Most teams use version control systems like Git to manage code changes and collaborations.

Testing environments: As developers build individual components, they usually do so in a controlled environment where bugs can be identified early.

The coding phase can be time-consuming, and it requires a high degree of focus and collaboration among team members.

4. Testing

After the code is written, it must go through rigorous testing to ensure that it works as expected. The purpose of this stage is to identify bugs, security flaws, and usability issues.

There are several types of testing:

Unit testing: Verifies that individual components work correctly.

Integration testing: Ensures that different modules of the software work together as intended.

System testing: Examines the system as a whole to validate that the final product meets the project requirements.

User acceptance testing (UAT): End-users test the software to make sure it satisfies their needs.

Thorough testing ensures that the software is robust, user-friendly, and free of major defects before it is released to the public.

5. Deployment

Once testing is complete, the software is ready for deployment. Deployment means releasing the product to the end users or making it live in a production environment.

During the deployment phase:

Installation and setup: The software is installed on the end-users' systems, or hosted on servers for web applications.

Monitoring and feedback: Developers often monitor the deployment for any issues that arise, such as bugs that were not identified during testing.

There are multiple deployment methods, such as phased deployment (gradually releasing the software) or big bang deployment (releasing everything at once). The choice depends on the complexity of the system and the project timeline.

6. Maintenance and Support

Once the software is live, the work isn’t done. The next phase is maintenance and support, which involves addressing bugs, improving performance, and implementing new features.

Key activities include:

Bug fixes: Ongoing patches for bugs that emerge after release.

System updates: Updating the software to accommodate new hardware, operating systems, or third-party integrations.

Feature enhancements: Adding new features based on user feedback.

Maintenance ensures that the software continues to run smoothly and evolves to meet the changing needs of users.

7. Evaluation and Closure

The final stage in the software development lifecycle is evaluation and closure. At this point, stakeholders assess whether the software meets its original goals and whether it has delivered value to the business.

Project evaluation: Teams review the project to identify lessons learned and areas of improvement for future projects.

Documentation: Proper documentation is prepared, ensuring that future teams can easily maintain and upgrade the software.

Project closure: The project is officially closed, and resources are reallocated to new initiatives.

This stage is important because it provides an opportunity to review the entire process and capture valuable insights for future development projects.

Conclusion

The seven stages of software development—planning, design, implementation, testing, deployment, maintenance, and evaluation—form a comprehensive roadmap for delivering high-quality software. Each phase plays a critical role in ensuring that the final product is functional, scalable, and tailored to meet user needs. By understanding and following these stages, developers and project managers can maximize efficiency, minimize errors, and deliver software that exceeds expectations.

Remember, successful software development requires clear communication, proper planning, and continuous iteration. Following the SDLC framework will set you on the path to delivering robust software solutions that stand the test of time.

[markdarby]

Best Practices for Infrastructure as Code (IaC)

Hey there! If you're diving into the world of Infrastructure as Code (IaC), you're in for an exciting journey. IaC is all about managing and provisioning your infrastructure through code, making your deployments consistent, repeatable, and scalable. But like anything in tech, there are best practices to follow to make sure you're getting the most out of it. So, let's explore some key practices for effective IaC implementation that can make your life a whole lot easier!

Version Control for IaC

Why Version Control Matters

Alright, let's start with the basics. Why is version control so important for IaC? Imagine you're working on a complex infrastructure setup, and suddenly something breaks. Without version control, you're left scratching your head, wondering what went wrong. By using a version control system like Git, you can keep track of every change made to your IaC scripts. It's like having a rewind button for your infrastructure!

Tracking Changes and Collaboration

Version control isn't just about tracking changes; it's also a fantastic collaboration tool. When you're working with a team, everyone can work on different parts of the infrastructure without stepping on each other's toes. You can easily review changes, roll back to previous versions if something goes awry, and even experiment with new features in separate branches. It's all about teamwork and making sure everyone is on the same page.

Modular Design and Reusability

Creating Reusable Modules

Next up, let's talk about modular design and reusability. One of the best ways to streamline your IaC process is by creating reusable modules. Think of these modules as building blocks that you can mix and match across different projects. It saves time and ensures consistency. For example, if you've got a standard setup for deploying a web server, you can reuse that module whenever you need a web server, tweaking it only as necessary.

Encapsulation and Abstraction

When designing these modules, it's essential to encapsulate your infrastructure logic. This means hiding the complexity behind a simple interface. By doing so, you make it easier for others (and future you) to use these modules without needing to understand every detail. It's like driving a car; you don't need to know how the engine works to get from point A to point B.

Automated Testing and Validation

Testing IaC

Now, let's get into something super crucial: testing. Just like with any code, you want to catch errors before they make it to production. Automated testing for IaC scripts is your safety net. It helps you identify issues early on, saving you from potentially disastrous deployments.

Tools and Techniques

There are some fantastic tools out there for testing IaC. Terratest, for instance, is great for testing Terraform configurations, while Molecule is your go-to for testing Ansible playbooks. These tools allow you to run tests in isolated environments, ensuring that your scripts do what they're supposed to do. It's like having a practice run before the big game.

Security and Compliance

Ensuring Secure IaC

So, we've all heard horror stories about security breaches, right? In the world of IaC, security is just as critical as anywhere else in tech. When you're defining your infrastructure through code, you're also setting up security policies, permissions, and configurations. It's essential to scan your IaC scripts for vulnerabilities regularly. Tools like Checkov or TFLint can help you catch security issues before they go live. Trust me, a little prevention goes a long way!

Compliance Audits

Now, onto compliance. Whether you're working in healthcare, finance, or any other regulated industry, adhering to compliance standards is non-negotiable. IaC can make compliance audits a breeze. By codifying your infrastructure, you can create repeatable and auditable processes. This means you can quickly show auditors that your systems are up to snuff with industry regulations. It's like having a well-organized filing cabinet, but for your infrastructure!

Ensuring Best Practices with Professional Support

So, you've got your IaC scripts, version control, modular designs, and automated tests all set up. But what if you need a bit more help? This is where a DevOps services provider company comes in. These experts offer comprehensive support to implement best practices for IaC. They can guide you through the maze of tools and techniques, ensuring that your infrastructure is secure, compliant, and efficient. It's like having a personal trainer for your tech stack!

Final Thoughts

The Path to Effective IaC Implementation

Alright, let's wrap this up. Implementing Infrastructure as Code can be a game-changer for your organization. By following these best practices—using version control, designing reusable modules, testing your scripts, and ensuring security and compliance—you set yourself up for success. And remember, having professional guidance can make the journey smoother and more efficient. So, go ahead and dive into IaC with confidence, knowing that you've got a solid foundation to build on.

And there you have it, folks! That's the scoop on best practices for IaC. If you've got any questions or want to share your experiences, feel free to drop a comment. Happy coding!

Frequently Asked Questions (FAQs)

1. What is Infrastructure as Code (IaC) and why is it important?

Infrastructure as Code (IaC) is a method to manage and provision computer data centers through machine-readable scripts, rather than physical hardware configuration or interactive configuration tools. It's important because it automates and standardizes the deployment process, reducing manual errors, and speeding up the setup and scaling of infrastructure.

2. What are some best practices for implementing IaC?

Implementing IaC effectively involves several best practices, including using version control systems like Git, modularizing your infrastructure code for better reusability, and automating testing to catch errors early. It's also crucial to keep your IaC code secure by scanning for vulnerabilities and ensuring compliance with industry regulations.

3. How does version control help with IaC?

Version control systems help manage changes to IaC scripts, providing a history of changes, facilitating collaboration, and enabling rollback if something goes wrong. Tools like Git track every modification, making it easier to audit changes and maintain consistency across different environments​.

4. What are the common tools used for Infrastructure as Code?

Common IaC tools include Terraform, Ansible, and Pulumi. Terraform is known for its broad compatibility with cloud providers and its declarative syntax. Ansible is popular for configuration management and orchestration, while Pulumi allows for infrastructure provisioning using standard programming languages like Python and TypeScript​.

5. Why is automated testing important in IaC?

Automated testing in IaC ensures that infrastructure changes do not introduce errors or vulnerabilities. By running tests on your IaC scripts before deployment, you can catch issues early and maintain high reliability. Tools like Terratest and Molecule can automate these tests, providing continuous integration and delivery capabilities​.

[educationtech]

What Is Full Stack Developer: Essential Skills Required - Arya College

A full-stack developer course typically covers a comprehensive set of skills that enable

developers to work on both the front-end and back-end components of a web application.

Here are the key skills learned in a full-stack developer course:

Front-End Development Skills

1. HTML, CSS, and JavaScript: Mastering the core technologies of the web, including

HTML for structuring web pages, CSS for styling, and JavaScript for adding interactivity and

dynamic functionality.

2. Responsive Web Design: Developing websites and web applications that adapt to

different screen sizes and devices, ensuring a seamless user experience across desktop,

tablet, and mobile.

3. Front-End Frameworks and Libraries: Learning popular front-end frameworks like

React, Angular, or Vue.js, which provide a structured approach to building complex user

interfaces and enhance developer productivity.

4. UI/UX Design Principles: Understanding user interface (UI) and user experience (UX)

design principles to create visually appealing and intuitive web applications.

5. Web Accessibility: Ensuring web applications are accessible to users with disabilities,

following best practices and guidelines like WCAG (Web Content Accessibility Guidelines).

Back-End Development Skills

1. Server-Side Programming Languages: Proficiency in one or more back-end

programming languages, such as Python, Java, Node.js (JavaScript), or PHP, which are

used to build server-side logic and APIs.

2. Database Management: Familiarity with relational databases (e.g., MySQL,

PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra) for storing and retrieving

data.

3. API Development: Designing and developing RESTful APIs that allow the front end to

communicate with the back-end, enabling data exchange and functionality integration.

4. Web Frameworks and Libraries: Leveraging back-end frameworks (e.g., Django,

Flask, Ruby on Rails, Express.js) to accelerate development and follow best practices.

5. Web Server Configuration: Understanding web server setup and configuration,

including technologies like Apache, Nginx, or Node.js (with Express.js) to deploy and

manage the back-end infrastructure.

Full-Stack Integration Skills

1. Version Control: Proficiency in using version control systems, such as Git, to

collaborate on code, track changes, and manage project workflows.

2. Deployment and Hosting: Deploying and hosting the full-stack application on cloud

platforms (e.g., AWS, Google Cloud, Microsoft Azure) or traditional hosting services.

3. Testing and Debugging: Implementing unit tests, integration tests, and end-to-end

tests to ensure the application's functionality and reliability, as well as debugging techniques

to identify and fix issues.

4. Continuous Integration and Deployment: Setting up automated build, test, and

deployment pipelines to streamline the development and release process.

5. DevOps Practices: Understanding and applying DevOps principles, such as

infrastructure as code, containerization (Docker), and orchestration (Kubernetes), to

enhance the scalability and reliability of the application.

6. Security and Performance Optimization: Implementing secure coding practices,

protecting against common web vulnerabilities, and optimizing the application's performance

for a better user experience.

There are many full stack developer course which are provide by Arya college of

Engineering and I.T. which is one of the best Engineering College in Jaipur, learners gain

a comprehensive understanding of web development, from the front-end user interface to

the back-end server-side logic, as well as the skills to integrate and deploy the entire

application. This versatility makes full-stack developers highly valuable in the industry, as

they can contribute to all aspects of the web development lifecycle.

[appcurators]

Mastering Git Configurations: The Three Essential Levels!

System Level: Configures preferences globally on your machine. Useful for applying settings to all users and all projects on your computer. Adjustments here affect Git's core system settings.

Global Level: Specific to your user profile. This is ideal for settings like your username and email, which are specific to your contributions across projects.

Local Level: Specific to a single project. Overrides system and user settings for individual project preferences. Perfect for project-specific configurations.

Dive deeper into each level to optimize your Git setup for streamlined and efficient workflow!

#GitConfig #DeveloperTools #TechTips #CodingLife #SoftwareDevelopment #VersionControl #GitTips #ProgrammingBasics #TechCommunity #CodeNewbie

[codeshive]

COMP 3522 Lab 1 solved

Welcome to your first COMP 3522 lab. Today’s lab is all about setting up your tools, and using them to generate and share a simple Hello World application. You will install CLion, git, g++, and create your first C++ project. You’ll also learn how to use Github classroom, commit and push your code. Let’s get started! 1 CLion setup Please complete the following: 1. Start by signing up for a free…

[yethiconsulting]

How to Integrate Automated Testing into Your CI/CD Pipeline

Integrating automated testing into your Continuous Integration/Continuous Deployment (CI/CD) pipeline is essential for ensuring the quality and reliability of software releases. Here’s a step-by-step guide to effectively incorporate automated testing into your CI/CD workflow:

1. Set Up Your CI/CD Pipeline: Start by configuring your CI/CD pipeline using tools like Jenkins, GitLab CI, Travis CI, or CircleCI. These platforms automate the process of building, testing, and deploying code, enabling continuous integration and delivery.

2. Select Appropriate Testing Frameworks: Choose testing frameworks that align with your project’s technology stack and testing needs. Popular frameworks include Selenium for web applications, Appium for mobile apps, and JUnit or TestNG for unit testing in Java.

3. Write and Organize Automated Tests: Develop automated test scripts for different types of tests, including unit, integration, and functional tests. Organize these tests in a structured manner within your repository, ensuring they are easy to locate and maintain.

4. Integrate Tests into the Pipeline: Configure your CI/CD tool to trigger automated tests at various stages of the pipeline. Common practice involves running unit tests during the build phase, followed by integration and functional tests in subsequent stages. This ensures issues are detected early.

5. Use Test Suites: Group your automated tests into suites to manage and execute them efficiently. Test suites can be tailored for different environments or types of testing, allowing for more focused and efficient test runs.

6. Implement Parallel Testing: Enable parallel test execution to reduce overall testing time. CI/CD tools often support running multiple tests concurrently, which can significantly speed up the feedback loop and improve productivity.

7. Monitor and Analyze Test Results: Set up robust reporting and monitoring systems to track test results. CI/CD tools typically provide dashboards and logs that display test outcomes, helping you quickly identify and address failures.

8. Automate Test Environment Setup: Use containerization tools like Docker to create consistent test environments. Automating the setup of test environments ensures that tests run in consistent conditions, reducing the chances of environment-related issues.

9. Integrate with Version Control: Ensure your CI/CD pipeline is tightly integrated with your version control system (e.g., Git). Automated tests should run on every code commit or pull request, providing immediate feedback on code changes.

10. Foster Collaboration and Continuous Improvement: Encourage collaboration between developers, testers, and operations teams. Regularly review and refine your automated testing and CI/CD processes to address any bottlenecks and continuously improve the workflow.

By following these steps, you can seamlessly integrate automated testing into your CI/CD pipeline, ensuring rapid and reliable software delivery while maintaining high quality standards.

[mulemasters]

tosca ci integration with jenkins

Tosca CI Integration with Jenkins: A Guide

If you're working in software development, you know that Continuous Integration (CI) is a game-changer. It ensures that your codebase remains stable and that issues are caught early. Integrating Tricentis Tosca with Jenkins can streamline your testing process, making it easier to maintain high-quality software. Here’s a simple guide to help you set up Tosca CI integration with Jenkins.

Step 1: Prerequisites

Before you start, make sure you have:

Jenkins Installed: Ensure Jenkins is installed and running. You can download it from the official Jenkins website.

Tosca Installed: You should have Tricentis Tosca installed and configured on your system.

Tosca CI Client: The Tosca CI Client should be installed on the machine where Jenkins is running.

Step 2: Configure Tosca for CI

Create Test Cases in Tosca: Develop and organize your test cases in Tosca.

Set Up Execution Lists: Create execution lists that group your test cases in a logical order. These lists will be triggered during the CI process.

Step 3: Install Jenkins Plugins

Tosca CI Plugin: You need to install the Tosca CI Plugin in Jenkins. Go to Manage Jenkins > Manage Plugins > Available and search for "Tosca". Install the plugin and restart Jenkins if required.

Required Plugins: Ensure you have other necessary plugins installed, like the "Pipeline" plugin for creating Jenkins pipelines.

Step 4: Configure Jenkins Job

Create a New Job: In Jenkins, create a new job by selecting New Item, then choose Freestyle project or Pipeline depending on your setup.

Configure Source Code Management: If your test cases or project are in a version control system (like Git), configure the repository URL and credentials under the Source Code Management section.

Build Steps: Add build steps to integrate Tosca tests.

For a Freestyle project, add a Build Step and select Execute Windows batch command or Execute shell script.

Use the Tosca CI Client command to trigger the execution list: sh ToscaCIClient.exe --executionList="" --project=""

Step 5: Configure Pipeline (Optional)

If you prefer using Jenkins Pipelines, you can add a Jenkinsfile to your repository with the following content:pipeline { agent any stages { stage('Checkout') { steps { git 'https://github.com/your-repo/your-project.git' } } stage('Execute Tosca Tests') { steps { bat 'ToscaCIClient.exe --executionList="<Your Execution List>" --project="<Path to Your Tosca Project>"' } } } }

Step 6: Trigger the Job

Manual Trigger: You can manually trigger the job by clicking Build Now in Jenkins.

Automated Trigger: Set up triggers like SCM polling or webhook triggers to automate the process.

Step 7: Review Results

Once the build completes, review the test results. The Tosca CI Client will generate reports that you can view in Jenkins. Check the console output for detailed logs and any potential issues.

Conclusion

Integrating Tosca with Jenkins enables you to automate your testing process, ensuring continuous feedback and early detection of issues. This setup not only saves time but also enhances the reliability of your software. By following these steps, you'll have a robust CI pipeline that leverages the strengths of Tosca and Jenkins. Happy testing!

[dishachrista]

Your Path to Becoming a DevOps Engineer

Thinking about a career as a DevOps engineer? Great choice! DevOps engineers are pivotal in the tech world, automating processes and ensuring smooth collaboration between development and operations teams. Here’s a comprehensive guide to kick-starting your journey with the Best Devops Course.

Grasping the Concept of DevOps

Before you dive in, it’s essential to understand what DevOps entails. DevOps merges "Development" and "Operations" to boost collaboration and efficiency by automating infrastructure, workflows, and continuously monitoring application performance.

Step 1: Build a Strong Foundation

Start with the Essentials:

Programming and Scripting: Learn languages like Python, Ruby, or Java. Master scripting languages such as Bash and PowerShell for automation tasks.

Linux/Unix Basics: Many DevOps tools operate on Linux. Get comfortable with Linux command-line basics and system administration.

Grasp Key Concepts:

Version Control: Familiarize yourself with Git to track code changes and collaborate effectively.

Networking Basics: Understand networking principles, including TCP/IP, DNS, and HTTP/HTTPS.

If you want to learn more about ethical hacking, consider enrolling in an Devops Online course They often offer certifications, mentorship, and job placement opportunities to support your learning journey.

Step 2: Get Proficient with DevOps Tools

Automation Tools:

Jenkins: Learn to set up and manage continuous integration/continuous deployment (CI/CD) pipelines.

Docker: Grasp containerization and how Docker packages applications with their dependencies.

Configuration Management:

Ansible, Puppet, and Chef: Use these tools to automate the setup and management of servers and environments.

Infrastructure as Code (IaC):

Terraform: Master Terraform for managing and provisioning infrastructure via code.

Monitoring and Logging:

Prometheus and Grafana: Get acquainted with monitoring tools to track system performance.

ELK Stack (Elasticsearch, Logstash, Kibana): Learn to set up and visualize log data.

Consider enrolling in a DevOps Online course to delve deeper into ethical hacking. These courses often provide certifications, mentorship, and job placement opportunities to support your learning journey.

Step 3: Master Cloud Platforms

Cloud Services:

AWS, Azure, and Google Cloud: Gain expertise in one or more major cloud providers. Learn about their services, such as compute, storage, databases, and networking.

Cloud Management:

Kubernetes: Understand how to manage containerized applications with Kubernetes.

Step 4: Apply Your Skills Practically

Hands-On Projects:

Personal Projects: Develop your own projects to practice setting up CI/CD pipelines, automating tasks, and deploying applications.

Open Source Contributions: Engage with open-source projects to gain real-world experience and collaborate with other developers.

Certifications:

Earn Certifications: Consider certifications like AWS Certified DevOps Engineer, Google Cloud Professional DevOps Engineer, or Azure DevOps Engineer Expert to validate your skills and enhance your resume.

Step 5: Develop Soft Skills and Commit to Continuous Learning

Collaboration:

Communication: As a bridge between development and operations teams, effective communication is vital.

Teamwork: Work efficiently within a team, understanding and accommodating diverse viewpoints and expertise.

Adaptability:

Stay Current: Technology evolves rapidly. Keep learning and stay updated with the latest trends and tools in the DevOps field.

Problem-Solving: Cultivate strong analytical skills to troubleshoot and resolve issues efficiently.

Conclusion

Begin Your Journey Today: Becoming a DevOps engineer requires a blend of technical skills, hands-on experience, and continuous learning. By building a strong foundation, mastering essential tools, gaining cloud expertise, and applying your skills through projects and certifications, you can pave your way to a successful DevOps career. Persistence and a passion for technology will be your best allies on this journey.

[jcmarchi]

Setting Up a Training, Fine-Tuning, and Inferencing of LLMs with NVIDIA GPUs and CUDA

New Post has been published on https://thedigitalinsider.com/setting-up-a-training-fine-tuning-and-inferencing-of-llms-with-nvidia-gpus-and-cuda/

Setting Up a Training, Fine-Tuning, and Inferencing of LLMs with NVIDIA GPUs and CUDA

The field of artificial intelligence (AI) has witnessed remarkable advancements in recent years, and at the heart of it lies the powerful combination of graphics processing units (GPUs) and parallel computing platform.

Models such as GPT, BERT, and more recently Llama, Mistral are capable of understanding and generating human-like text with unprecedented fluency and coherence. However, training these models requires vast amounts of data and computational resources, making GPUs and CUDA indispensable tools in this endeavor.

This comprehensive guide will walk you through the process of setting up an NVIDIA GPU on Ubuntu, covering the installation of essential software components such as the NVIDIA driver, CUDA Toolkit, cuDNN, PyTorch, and more.

The Rise of CUDA-Accelerated AI Frameworks

GPU-accelerated deep learning has been fueled by the development of popular AI frameworks that leverage CUDA for efficient computation. Frameworks such as TensorFlow, PyTorch, and MXNet have built-in support for CUDA, enabling seamless integration of GPU acceleration into deep learning pipelines.

According to the NVIDIA Data Center Deep Learning Product Performance Study, CUDA-accelerated deep learning models can achieve up to 100s times faster performance compared to CPU-based implementations.

NVIDIA’s Multi-Instance GPU (MIG) technology, introduced with the Ampere architecture, allows a single GPU to be partitioned into multiple secure instances, each with its own dedicated resources. This feature enables efficient sharing of GPU resources among multiple users or workloads, maximizing utilization and reducing overall costs.

Accelerating LLM Inference with NVIDIA TensorRT

While GPUs have been instrumental in training LLMs, efficient inference is equally crucial for deploying these models in production environments. NVIDIA TensorRT, a high-performance deep learning inference optimizer and runtime, plays a vital role in accelerating LLM inference on CUDA-enabled GPUs.

According to NVIDIA’s benchmarks, TensorRT can provide up to 8x faster inference performance and 5x lower total cost of ownership compared to CPU-based inference for large language models like GPT-3.

NVIDIA’s commitment to open-source initiatives has been a driving force behind the widespread adoption of CUDA in the AI research community. Projects like cuDNN, cuBLAS, and NCCL are available as open-source libraries, enabling researchers and developers to leverage the full potential of CUDA for their deep learning.

Installation

When setting  AI development, using the latest drivers and libraries may not always be the best choice. For instance, while the latest NVIDIA driver (545.xx) supports CUDA 12.3, PyTorch and other libraries might not yet support this version. Therefore, we will use driver version 535.146.02 with CUDA 12.2 to ensure compatibility.

Installation Steps

1. Install NVIDIA Driver

First, identify your GPU model. For this guide, we use the NVIDIA GPU. Visit the NVIDIA Driver Download page, select the appropriate driver for your GPU, and note the driver version.

To check for prebuilt GPU packages on Ubuntu, run:

sudo ubuntu-drivers list --gpgpu

Reboot your computer and verify the installation:

nvidia-smi

2. Install CUDA Toolkit

The CUDA Toolkit provides the development environment for creating high-performance GPU-accelerated applications.

For a non-LLM/deep learning setup, you can use:

sudo apt install nvidia-cuda-toolkit However, to ensure compatibility with BitsAndBytes, we will follow these steps: [code language="BASH"] git clone https://github.com/TimDettmers/bitsandbytes.git cd bitsandbytes/ bash install_cuda.sh 122 ~/local 1

Verify the installation:

~/local/cuda-12.2/bin/nvcc --version

Set the environment variables:

export CUDA_HOME=/home/roguser/local/cuda-12.2/ export LD_LIBRARY_PATH=/home/roguser/local/cuda-12.2/lib64 export BNB_CUDA_VERSION=122 export CUDA_VERSION=122

3. Install cuDNN

Download the cuDNN package from the NVIDIA Developer website. Install it with:

sudo apt install ./cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb

Follow the instructions to add the keyring:

sudo cp /var/cudnn-local-repo-ubuntu2204-8.9.7.29/cudnn-local-08A7D361-keyring.gpg /usr/share/keyrings/

Install the cuDNN libraries:

sudo apt update sudo apt install libcudnn8 libcudnn8-dev libcudnn8-samples

4. Setup Python Virtual Environment

Ubuntu 22.04 comes with Python 3.10. Install venv:

sudo apt-get install python3-pip sudo apt install python3.10-venv

Create and activate the virtual environment:

cd mkdir test-gpu cd test-gpu python3 -m venv venv source venv/bin/activate

5. Install BitsAndBytes from Source

Navigate to the BitsAndBytes directory and build from source:

cd ~/bitsandbytes CUDA_HOME=/home/roguser/local/cuda-12.2/ LD_LIBRARY_PATH=/home/roguser/local/cuda-12.2/lib64 BNB_CUDA_VERSION=122 CUDA_VERSION=122 make cuda12x CUDA_HOME=/home/roguser/local/cuda-12.2/ LD_LIBRARY_PATH=/home/roguser/local/cuda-12.2/lib64 BNB_CUDA_VERSION=122 CUDA_VERSION=122 python setup.py install

6. Install PyTorch

Install PyTorch with the following command:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

7. Install Hugging Face and Transformers

Install the transformers and accelerate libraries:

pip install transformers pip install accelerate

The Power of Parallel Processing

At their core, GPUs are highly parallel processors designed to handle thousands of concurrent threads efficiently. This architecture makes them well-suited for the computationally intensive tasks involved in training deep learning models, including LLMs. The CUDA platform, developed by NVIDIA, provides a software environment that allows developers to harness the full potential of these GPUs, enabling them to write code that can leverage the parallel processing capabilities of the hardware. Accelerating LLM Training with GPUs and CUDA.

Training large language models is a computationally demanding task that requires processing vast amounts of text data and performing numerous matrix operations. GPUs, with their thousands of cores and high memory bandwidth, are ideally suited for these tasks. By leveraging CUDA, developers can optimize their code to take advantage of the parallel processing capabilities of GPUs, significantly reducing the time required to train LLMs.

For example, the training of GPT-3, one of the largest language models to date, was made possible through the use of thousands of NVIDIA GPUs running CUDA-optimized code. This allowed the model to be trained on an unprecedented amount of data, leading to its impressive performance in natural language tasks.

import torch import torch.nn as nn import torch.optim as optim from transformers import GPT2LMHeadModel, GPT2Tokenizer # Load pre-trained GPT-2 model and tokenizer model = GPT2LMHeadModel.from_pretrained('gpt2') tokenizer = GPT2Tokenizer.from_pretrained('gpt2') # Move model to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) # Define training data and hyperparameters train_data = [...] # Your training data batch_size = 32 num_epochs = 10 learning_rate = 5e-5 # Define loss function and optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # Training loop for epoch in range(num_epochs): for i in range(0, len(train_data), batch_size): # Prepare input and target sequences inputs, targets = train_data[i:i+batch_size] inputs = tokenizer(inputs, return_tensors="pt", padding=True) inputs = inputs.to(device) targets = targets.to(device) # Forward pass outputs = model(**inputs, labels=targets) loss = outputs.loss # Backward pass and optimization optimizer.zero_grad() loss.backward() optimizer.step() print(f'Epoch epoch+1/num_epochs, Loss: loss.item()')

In this example code snippet, we demonstrate the training of a GPT-2 language model using PyTorch and the CUDA-enabled GPUs. The model is loaded onto the GPU (if available), and the training loop leverages the parallelism of GPUs to perform efficient forward and backward passes, accelerating the training process.

CUDA-Accelerated Libraries for Deep Learning

In addition to the CUDA platform itself, NVIDIA and the open-source community have developed a range of CUDA-accelerated libraries that enable efficient implementation of deep learning models, including LLMs. These libraries provide optimized implementations of common operations, such as matrix multiplications, convolutions, and activation functions, allowing developers to focus on the model architecture and training process rather than low-level optimization.

One such library is cuDNN (CUDA Deep Neural Network library), which provides highly tuned implementations of standard routines used in deep neural networks. By leveraging cuDNN, developers can significantly accelerate the training and inference of their models, achieving performance gains of up to several orders of magnitude compared to CPU-based implementations.

import torch import torch.nn as nn import torch.nn.functional as F from torch.cuda.amp import autocast class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1): super().__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_channels)) def forward(self, x): with autocast(): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out

In this code snippet, we define a residual block for a convolutional neural network (CNN) using PyTorch. The autocast context manager from PyTorch’s Automatic Mixed Precision (AMP) is used to enable mixed-precision training, which can provide significant performance gains on CUDA-enabled GPUs while maintaining high accuracy. The F.relu function is optimized by cuDNN, ensuring efficient execution on GPUs.

Multi-GPU and Distributed Training for Scalability

As LLMs and deep learning models continue to grow in size and complexity, the computational requirements for training these models also increase. To address this challenge, researchers and developers have turned to multi-GPU and distributed training techniques, which allow them to leverage the combined processing power of multiple GPUs across multiple machines.

CUDA and associated libraries, such as NCCL (NVIDIA Collective Communications Library), provide efficient communication primitives that enable seamless data transfer and synchronization across multiple GPUs, enabling distributed training at an unprecedented scale.

</pre> import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP # Initialize distributed training dist.init_process_group(backend='nccl', init_method='...') local_rank = dist.get_rank() torch.cuda.set_device(local_rank) # Create model and move to GPU model = MyModel().cuda() # Wrap model with DDP model = DDP(model, device_ids=[local_rank]) # Training loop (distributed) for epoch in range(num_epochs): for data in train_loader: inputs, targets = data inputs = inputs.cuda(non_blocking=True) targets = targets.cuda(non_blocking=True) outputs = model(inputs) loss = criterion(outputs, targets) optimizer.zero_grad() loss.backward() optimizer.step()

In this example, we demonstrate distributed training using PyTorch’s DistributedDataParallel (DDP) module. The model is wrapped in DDP, which automatically handles data parallelism, gradient synchronization, and communication across multiple GPUs using NCCL. This approach enables efficient scaling of the training process across multiple machines, allowing researchers and developers to train larger and more complex models in a reasonable amount of time.

Deploying Deep Learning Models with CUDA

While GPUs and CUDA have primarily been used for training deep learning models, they are also crucial for efficient deployment and inference. As deep learning models become increasingly complex and resource-intensive, GPU acceleration is essential for achieving real-time performance in production environments.

NVIDIA’s TensorRT is a high-performance deep learning inference optimizer and runtime that provides low-latency and high-throughput inference on CUDA-enabled GPUs. TensorRT can optimize and accelerate models trained in frameworks like TensorFlow, PyTorch, and MXNet, enabling efficient deployment on various platforms, from embedded systems to data centers.

import tensorrt as trt # Load pre-trained model model = load_model(...) # Create TensorRT engine logger = trt.Logger(trt.Logger.INFO) builder = trt.Builder(logger) network = builder.create_network() parser = trt.OnnxParser(network, logger) # Parse and optimize model success = parser.parse_from_file(model_path) engine = builder.build_cuda_engine(network) # Run inference on GPU context = engine.create_execution_context() inputs, outputs, bindings, stream = allocate_buffers(engine) # Set input data and run inference set_input_data(inputs, input_data) context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr) # Process output # ...

In this example, we demonstrate the use of TensorRT for deploying a pre-trained deep learning model on a CUDA-enabled GPU. The model is first parsed and optimized by TensorRT, which generates a highly optimized inference engine tailored for the specific model and hardware. This engine can then be used to perform efficient inference on the GPU, leveraging CUDA for accelerated computation.

Conclusion

The combination of GPUs and CUDA has been instrumental in driving the advancements in large language models, computer vision, speech recognition, and various other domains of deep learning. By harnessing the parallel processing capabilities of GPUs and the optimized libraries provided by CUDA, researchers and developers can train and deploy increasingly complex models with high efficiency.

As the field of AI continues to evolve, the importance of GPUs and CUDA will only grow. With even more powerful hardware and software optimizations, we can expect to see further breakthroughs in the development and deployment of  AI systems, pushing the boundaries of what is possible.