Vite 5.0: What is New in it?
On December 9, 2022, Vite 4.0 was released with numerous advancements. Within less than a year, this front-end tool has come up with its new version, i.e., Vite 5.0. Released on November 16, 2023, Vite 5.0 is all set to take front-end development to the next level. If you are wondering what is new in Vite 5.0, this blog post is for you. Overview of Vite For those who do not know, Vite is a renowned front-end web development tool. It helps users build and optimize web applications. Evan You is the key person behind the launch of this lightning-fast web development tool. It was first released in 2020, and since then, it has witnessed several upgrades. Let us learn about the changes we can see in its new version, which is Vite 5.0. Upgrades in Vite 5.0 Node.js Support With this new version, Vite 5.0 has started supporting the latest versions of Node.js. If you were working with previous versions of Node.js, this might disappoint you. Vite 5.0 does not support Note.js 14, 16, 17, and 19 anymore. You will be required to have Node.js 18/ 20+. Rollup 4 Vite 5.0 adopts Rollup 4, which brings various improvements in terms of performance. For instance, users can experience improvement in tree-shaking capabilities that naturally minimize the bundle size. Additionally, you can notice the following changes as well. The assertions prop is now named as attributes prop. You cannot use Acorn plugins anymore. For Vite plugins, this.resolve skipSelf option is now true by default. Deprecate CJS Node API Another crucial change you notice in Vite 5.0 is the deprecated CJS Node API. If you call require(‘vite’), it projects a deprecation warning. Users will need to update their frameworks or files to import the ESM build of Vite. Performance In Vite 5.0, you also get a new feature server.warmup. It improves the startup time several times. It enables users to define a list of modules that should be pre-transformed as soon as the server starts. When a user uses —open or server.open, Vite 5.0 warms up the entry point of their applications. Improved Type-Script Support TypeScript has turned into a crucial part of the JavaScript ecosystem. Vite 5.0 provides full support for this statically typed language. Now users can experience improved TypeScript integration. In addition to this, they can do better type-checking and get enhanced tooling support now. Worker. Plugins Acts as a Function Now In the previous version of Vite, worker.plugins used to accept an array of plugins ((Plugin | Plugin[ ])[ ]). However, in Vite 5.0, it has to be configured as a function that returns an array of plugins (() => (Plugin | Plugin[ ])[] ). It is an imperative change; therefore, parallel worker builds run steadily. Browser Compatibility Cross-browser compatibility is vital in the contemporary web development landscape. Vite 5.0 recognizes this need. This new version supports enhanced browser compatibility features. Users can do cross-browser testing without any issues now. It helps web developers build and test applications across different browser environments. Support for Manifest Files in .vite Directory Earlier the manifest files used to be generated in the root of build.outDir by default. With Vite 5, the user can produce them in the .vite directory in the build.outDir by default. This change aids in avoiding conflicts among public files having the same manifest file names during their transfer to the build.outDir. Developer Tooling Vite 5.0 has given more emphasis on improving the overall web development experience. Users can notice improvement in the errors handling capabilities. Developers can identify and resolve issues more quickly than before. In addition to this, Vite 5.0 streamlines the development workflow by adopting a productive coding environment. Press an Additional Enter for CLI Shortcuts Users using CLI shortcuts will need to press an additional Enter to prompt the shortcut command. For instance, earlier users had to press only r to restart the dev server. Now they will press r + Enter to restart the dev server. Conclusion Vite 5.0 introduces an array of new features while also eliminating several components. Some of the primary changes you can notice in this current version of Vite are improved TypeScript integration, Rollup 4, Cleaned up API, and Deprecated CJS Node API. It would not be wrong to mention that Vite is a next-generation front-end web development tool. This new version brings a lot of revolutionary changes and simplifies the developing experience.
TeamCity vs. Jenkins An In-depth Comparison
While several CI/CD tools are available, the two most preferred options are TeamCity and Jenkins. Both are powerful tools that help web developers streamline their workflow. Nevertheless, they differ in various aspects. This blog post highlights the differences between TeamCity vs. Jenkins based on several factors. Understanding these dissimilarities will help you choose the best option for your next project. So without any delay, let us get started. TeamCity vs. Jenkins: Overview What is TeamCity? Developed by JetBrains, TeamCity is a renowned continuous integration and delivery server system. It was first released in 2006. However, it has come a long way since then. The software enables users to relish flexible workflows, collaborate with other developers, and do unlimited development practices. This Java-based platform supports various programming languages and can integrate with Visual Studio, Eclipse, and more. What is Jenkins? Jenkins is a server-based, open-source CI/CD tool that allows users to build, test, and deploy web codes. It operates within servlet containers like Apache Tomcat. Jenkins was initially released on the 2nd of February 2011 by Kohsuke Kawaguchi. Jenkins operates in Java language. As a result, users can install it on UNIX, LINUX, and Windows. TeamCity vs. Jenkins: Comparison of Different Factors User-friendliness What’s the benefit of making a powerful software or tool that users can’t understand? Ease of use is one of the crucial factors to assess when choosing a CI/CD platform. You can’t leverage the platform if you cannot navigate through its interface. TeamCity features a clean web-based interface that simplifies navigation. Users often praise its straightforward setup and configuration. You get an easy-to-use dashboard with detailed build logs. In comparison to TeamCity, Jenkins can be a little difficult to navigate for beginners. However, overall it is good. The platform offers various customization options. It also supports custom CSS and Javascript files. Jenkins requires more configuration initially. Nevertheless, it won’t be that tough for you to understand it. Installation It is convenient to install TeamCity. Download the TeamCity server and read the documentation. It mentions all the instructions that you have to follow step-by-step. To install Jenkins, you need to have Java in your system. However, the installation is as smooth as that of TeamCity. As it is an open-source, you will set up the server and accomplish dependencies manually. Community Support Problem-solving approach, user engagement, and knowledge sharing are primary parts of an excellent community support system. TeamCity has community support available on its website. Users can contact their team with details of their issues. The support team might ask for information related to system or logs to fix the problem. Users can also share troubleshooting information for different issues in the knowledge base. Jenkins has the upper hand in terms of community support. Since it is an open-source platform, users share their troubleshooting experiences. Beginners can learn and resolve their issues from the experiences of previous customers. Scalability Scalability is one of the imperative considerations when deciding between TeamCity vs. Jenkins. TeamCity is capable of handling large-scale developments and deployments. Users get built-in support for distributed forms. TeamCity can distribute workload through different build agents. As a result, the platform can manage large projects efficiently. Jenkins won’t disappoint you in terms of scalability as well. With additional setup and configuration, Jenkins can manage large deployments and projects. You will have to set up distributed build agents to improve scalability. Moreover, you can also use external tools such as Kubernetes. Plugin Support TeamCity offers you almost all the basic required integrations. It has a wide range of built-in features for different technologies. For example, you can integrate it with the Visual Studio Team service. Jenkins is slightly better when it comes to integrations. You can choose from hundreds of free plugins based on your requirements. Key Integration Both platforms allow integration with different version control systems, such as Git, Mercurial, SVN, and more. TeamCity prioritizes native integration for a seamless developer experience. You can quickly integrate with JetBrains’ suite of IDEs. On the other hand, Jenkins is an open-source platform. As a result, it supports a broad range of integration with third-party tools. Security Security is another vital aspect you cannot compromise on. TeamCity supports integration with the Snyk security plugin. The plugin is capable of doing vulnerability scanning in the build pipeline. The plugin can identify and resolve various threats and risks. Jenkins also comes with various security features. Users can enable features like user authentication and more. Furthermore, Jenkins also allows integration with external authentication systems, such as LDAP, Active Directory, etc. Pricing TeamCity has three versions: TeamCity Professional, TeamCity Enterprise (from $1,999 per year), and TeamCity Cloud ( from $45 per month). Out of three, TeamCity Professional provides free access. You can even use it for commercial use. However, it comes with certain limitations. Note: Since the price keeps changing, visit the official website to check the current price. Jenkins is free to use as it is an open-source platform. You can download it and start using it. Conclusion: What Should I Choose? This comparison of TeamCity vs. Jenkins makes you aware of the ins and outs of both platforms. Now you can determine which one fulfills your needs better. Choosing between TeamCity and Jenkins depends on your specific requirements. If you are looking for user-friendly yet a robust CI/CD, go for TeamCity. On the other hand, users looking for extensive customization can choose Jenkins. However, you will have to spend more time on configurations and maintenance of this platform. Evaluate your priorities and make decisions accordingly.
MongoDB Sharding: Everything You Need to Know About it
Data sharding is the breakthrough technique of the modern data-driven era. It eliminates the limitation that a single database server can store and process only a certain amount of data. Data sharding enables users to store the data across different database servers. Therefore, it improves response time and supports scalability. This blog post gives you in-depth knowledge about MongoDB sharding. It also provides you guidance on setting up this sharding. MongoDB Overview Before we learn about MongoDB sharding, let’s get to know MongoDB. MongoDB is an open-source document-oriented NoSQL database. It has been used by several prominent companies, including Facebook, Google, and eBay. MongoDB lets users store data in flexible, JSON-like documents. Since this database features schema design, it can handle unstructured data. Consequently, it has become the first choice for most modern web applications. Unlike conventional relational database systems, MongoDB can manage a large amount of data. The best thing is that users can integrate MongoDB with all prominent programming languages, such as PHP, Python, Java, Node.js, C, C++, and more. What is MongoDB Sharding? Most companies use NoSQL databases to deal with storage and computing requirements of high-volume data. Sharding in MongoDB is a method that helps individuals distribute a database horizontally through different nodes. These nodes or servers are called “Shards”. Each shard can handle a portion of the data, creating a sharded cluster. It helps MongoDB manage the large volume of data and user concurrency efficiently. Each shard works as a unique database in a sharded cluster. They are accountable for storing and handling data. As soon as the data is added to the cluster, the balancer of MongoDB reallocates it across the shards. Thus it makes sure the workload is equally distributed. The architecture of sharding consists of the following elements: Shard Servers These servers are separate nodes within the sharded cluster. Each node collects a subset of the data and acts as an independent database. MongoDB circulates data across the shard servers to ensure even distribution. Config Servers These servers keep the metadata and configuration information for the sharded cluster. Query Routers Also referred to as “Mongos”, these routers work as the interface between the sharded cluster and applications. Query routers receive requests and queries to the suitable shards and combine results as required. As Mongos processes hide the underlying sharding complexity from the application, it looks like a single logical database. Shard key The shard key is a field that determines how data will be distributed in different shards. For even data distribution and efficient querying, it is necessary to choose the right shard key. Chunk A chunk constitutes a data range housed within a shard. It is decided by the shard key. Chunks are data migration units between shards. Balancer Balancer assures the uniform distribution of data amid the shards. Once data is added or eliminated, the balancer distributes data chunks between shards. Steps to Set up MongoDB Sharding You will require three servers to set up sharding in MongoDB. One for the config server and another for the Mongos instance. The last one is required for the shards. Here are step-by-step instructions to set up sharding in MongoDB. 1. Make a Directory Using Config Server First, you need to create a directory for the config server data. You can do this by running the below command on the 1st server. mkdir /data/configdb 2. Begin MongoDB in Config Mode Once you have created the directory, your next step will be starting MongoDB in config mode on the 1st server. Use this command in order to do it. mongod –configsvr –dbpath /data/configdb –port 27019 The command will begin the config server on port 27019 and store all the data in the /data/configdb directory. Note: We are employing the –configsvr flag to specify this server’s role as a config server. 3. Start Mongos Instance Next, start the process of Mongos instance. It will route queries to the accurate shards depending on the sharding key. Use the following command to begin this process. mongos –configdb <config server>:27019 Remember, you have to replace <config server> with the hostname/IP address of the machine where the config server is running. 4. Connect to Mongos Instance Use MongoDB shell to connect to Mongos instance. Below is the command to perform this. mongo –host <mongos-server> –port 27017 In the above command, replace <mongos-server> with the IP address or hostname of the server running the mongos instance. The command will open up the MongoDB shell and let you interact with Mongo’s instance, and add servers to the cluster. 5. Add Servers to Clusters Once you are connected to the mangos instance, add servers to the cluster. Use the following command: sh.addShard(“<shard-server>:27017”) In the above command, you need to replace <shard-server> with the hostname or IP address of the server running the shard. It will add the shard to the cluster so that it is available for utilization. Repeat this step for each shard you need to add to the cluster. 6. Permit Sharding for Database In the end, you need to enable sharding for a database. Here is the command for this process: sh.enableSharding(“<database>”) Replace <database> with the name of the database you want to shard. It will allow sharding for the particular database, enabling you to share its data across different shards. Congratulations! Now you have a fully functional MongoDB sharded cluster to handle high-traffic loads. Final Words Undoubtedly, MongoDB sharding is a robust technique supporting the horizontal scaling of databases. It allows a database to handle extensive data loads without compromising on performance. We hope this post gives you adequate information on MongoDB sharding. You can leverage the power of sharding and make the best use of your hardware resources.
GitHub Actions: An In-depth Guide for Beginners
Actions Platform? It would not be wrong to mention that GitHub Actions has significantly transformed the workflow of web developers. This Continuous Integration and Continuous Delivery (CI/CD) platform enables them to build, test, and deploy web codes straight from GitHub. Are you a beginner? Do you want to learn how this tool can boost your productivity? Read this guide until the end. It makes you aware of the components and features of GitHub Actions. Let’s get started! What is GitHub GitHub Actions is an automated tool powered by GitHub. It supports the automation of software building, testing, and deployment within the repositories of GitHub. Since the user does not need to leave GitHub, it naturally enhances the workflow and productivity. Developers can perform repetitive tasks while reducing manual intervention. GitHub Actions utilizes a YAML file to outline different steps of a workflow. These steps include running a script, testing, deploying codes, and sending notifications. Components of GitHub Actions GitHub Actions is a powerful tool that makes web development smooth and quick. Wondering what mechanisms make GitHub Actions work so well? Let’s learn about them. Workflow A workflow is a programmed process that runs one or more jobs. This configurable process is defined by a YAML file in the .github/workflows directory in a repository. This repository can have several workflows. And each workflow can perform a different set of jobs. For instance, you can use one workflow to create and test pull requests while another to deploy your application. Events An event is a particular activity in a repository. It is like a trigger for workflows. When events occur within a repository, GitHub Actions respond to them. These events can push requests, pull requests, or other actions. Jobs Jobs are a set of steps in a workflow. They are executed under the same runner. Each step is either a shell script or an action. Scripts execute while actions run. Action An action is an application for the GitHub Actions. It performs frequently repeated tasks. The application helps web developers to reduce the number of repetitive codes they write in their workflow files. Runner A runner is a server that runs workflows when they are triggered. One runner can perform a single task at a time. Essential Features of GitHub Actions Though GitHub Actions offers various advantages to web developers, a few prominent features are below. Variable in Workflows The default GitHub actions environment variables, incorporated in every workflow, run automatically. However, users can customize the environment variables by setting them in their YAML files. In the following example, you can see how one can create custom variables for POSTGRES_HOST and POSTGRES_PORT. These variables are available in the node client.js script. jobs: demo-job: steps: – name: Connect to your PostgreSQL run: node client.js env: POSTGRES_HOST: postres POSTGRES_PORT: 5432 Addition of Scripts to Workflow GitHub Actions allow the addition of scripts to workflow. You can employ actions for running scripts and shell commands. They get executed on the selected runner. Find out how an action can use the run keyword to execute npm install –g bats on the runner in the flowing example. jobs: demo-job: steps: – run: npm install -g bats Sharing Data Between Jobs One of the crucial features of GitHub Actions is that you can reuse the jobs you created earlier. You can save files for later use as artifacts on GitHub. These files get generated while building and testing web code. These files could be screenshots, binary, test results, and package files. You can also make your file and upload it on artifacts for later use. jobs: demo-job: name: Save output steps: – shell: bash run: | expr 1+1 > output.log – name: Upload output file users: actions/upload-artifact@v3 with: name: output-log-file path: output.log Step-by-Step Creation of GitHub Action File If you want to learn the workings of the GtiHub actions workflows, here is the step-by-step guide. You will need a GitHub repository to create the GitHub actions. Set up of the GitHub Action File ⦁ Make a .github/workflows directory in your repository on GitHub in case it does not already exist. ⦁ In the directory, you may create a file name: GitHub-actions-demo.yml. ⦁ Next, copy the following YAML content into the GitHub-actions-demo.yml file. name: GitHub Actions Example on: [push] jobs: Explore-GitHub-Actions: runs-on: ubuntu-latest steps: run: echo " The job was automatically triggered by a ${{Github.event_name }} event." run: echo " This job is now running on a ${{ runner.os }} server hosted by GitHub!" run: echo " The name of your branch is ${{ GitHub.ref }} and your repository is ${{ GitHub.repository }}." name: Check out repository code user: actions/checkout@v3 run: echo " The ${{ GitHub.repository }} repository has been cloned to the runner." run: echo " The workflow is now ready to test your code on the runner." name: List files in the repository run: | Is ${{ GitHub.workspace }} run: echo " This job's status is ${{ job.status }}." ⦁ Create a new branch for this commit and begin a pull request. ⦁ To create a pull request, click Propose new file. ⦁ When you commit your workflow file to a branch within your repository, it initiates the push event and then executes your workflow. Run the Files Your next step should be running the file. ⦁ Visit github.com and go to the main page of the repository. ⦁ Beneath your repository name, click Actions. ⦁ On the left sidebar, hit the workflow you want ⦁ Under Jobs, click on the Explore-GitHub-Actions job. The above log shows the breakdown of each step carried out. You can expand these steps to view its details. Conclusion GitHub Actions is a robust automation tool that streamlines development workflows. Web developers can leverage its flexibility, automation, and integration within GitHub. In addition to this, the platform supports event-driven workflows. In this blog, we learned about components of GitHub Actions. Also, we came to know about its essential features. All-in-all, GitHub Actions is a versatile tool for developers that simplifies the
Astro 3.0: Everything You Need to Know about it
Astro is an open-source web framework that enables users to design fast, efficient, and high-performance websites. It released its new avatar on August 30, 2023, known as Astro 3.0. According to the team, it is several times faster and feature-rich than previous versions. In this post, we will take an in-depth look at the revolutionary features of Astro 3.0. Before that, let us have a quick look at an Astro overview. Overview of Astro This open-source web framework is used to design heavy websites, such as landing pages, blogs, technical documentation, etc. Launched in June 2021, this open-source project provides support to more than 10,000 users. Similar to platforms like Next.js, Nuxt.js, and SolidStart, Astro features single-file components. The framework comes with a wide range of features, including partial hydration, zero-config development, incremental static regeneration, and static site generation. Let’s find out how Astro 3.0 is different from the previous version. What is new with Astro 3.0? Astro 3.0 is becoming popular as a prominent web framework to support the View Transitions API. Furthermore, it provides functionality for new browser APIs. Astro 3.0 features several exciting features, such as image optimization, SSR enhancements, performant HTML output, faster rendering, and more. Get to know more about these features in detail below. Astro 3.0: Exploring the New Features and Enhancements Full support for View Transitions API One of the outstanding features of Astro 3.0 is its support for View Transitions API. This application programming interface allows developers to create seamless transitions between different website components. You can fade, persist, morph, and slide stateful elements across page navigation. Furthermore, it enables native browser transition effects between pages. Earlier, only single-page applications could do transitions. With Astro 3.0, native page transition is possible. This feature makes it easier for developers to enhance user experience. Now you can: Transform persistent elements from one page to another Fade content on and off Slide content on and off Persist regular UI across pages without a refresh. Image Optimization (stable) Astro 3.0 features stable image optimization. Like Next.js, it also has a built-in <image> component that manages everything about rendering images on the web. This new version of Astro allows developers to compress or resize images. It automatically minimizes the page load times. As a result, images load faster in the browsers of users. This built-in feature has a significant role in enhancing website speed and performance. You can follow the below steps to use image optimization in Astro 3.0. Configure astro assets in your astro.config.mjs. Import images from the relative path from the existing .astro file. Use the image’s src and other properties in the <img> tag. Some other worth describing updates about image optimization in Astro 3.0 include: Support for Vercel’s built-in image service. You can optimize images from CMS tools and remote workflows. Astro utilizes Sharp as the default optimization library Faster Rendering Performance Astro is renowned for its rendering performance. However, with version 3.0, it has taken one step further. Compared to Astro 2.9, this new version renders components about 30% faster. This significant performance improvement is due to the less amount of JavaScript transported to the client by improving the rendering path. The developing team at Astro removed as much superfluous code as possible. SSR Enhancements for Serverless Astro 3.0 has brought numerous improvements to Server Side Rendering (SSR) for serverless applications. SSR technique generates web pages on the server before transporting them to the clients. Astro 3.0 lets you create responsive and dynamic web applications using serverless functions. Therefore, you get new ways to connect to the hosting platforms. HMR Enhancements for JSX Updating code was a big challenge with previous versions. Fortunately, with Astro 3.0, you can update codes instantly without refreshing the entire page. The framework brings HMR enhancements to JSX components. As a result, developers see changes in real-time. Not only does it minimize the development time, but also speeds up the debugging process. Astro 3.0 provides fast refresh support to React and Preact users. They can make changes without the fear of losing the component’s state. In addition, it also supports the component hierarchy. Consequently, the application maintains its structure after code changes. Optimized Build Output Astro 3.0 comes up with several changes in optimized build outputs. This latest version naturally minifies HTML outputs, reducing the payload and improving the overall response time. In addition, the team at Astro has replaced the messy astro-xxxx class names with a specific HTML attribute. Conclusion Astro 3.0 improvements make this framework ideal for web developers looking to create user-friendly yet powerful websites. It puts more stress on developer experience, performance, and user satisfaction. With features like support for the View Transitions API, improved rendering performance, and Image Optimization, we can say that Astro 3.0 leaps forward in the web development world.
Keras Core 3.0 — Pioneering the Next Frontier in Deep Learning APIs
In the dynamic landscape of artificial intelligence, where breakthroughs occur in rapid succession and the boundaries of what’s possible are constantly pushed, the Keras framework has emerged as a steadfast companion for machine learning practitioners and researchers. With the advent of Keras Core 3.0, the framework embarks on a transformative journey, poised to redefine the very essence of capabilities, performance, and adaptability, and solidify its position as a trailblazer in the realm of deep learning. This article delves into the evolution of Keras, highlights the remarkable features of version 3.0, and explores its compatibility with various backends. Understanding Keras — A Journey from Inception to Innovation Keras, born from the visionary mind of François Chollet in 2015, swiftly rose to prominence as a high-level neural networks API known for its intuitive design and unparalleled experimentation agility. Its initial incarnation and subsequent integration with TensorFlow marked a pivotal moment, propelling Keras into the limelight of machine learning tools. As the AI landscape evolved, Keras adapted in tandem, shaping itself to meet the diverse demands of an ever-expanding user community. Now, with the unveiling of Keras Core 3.0, this evolutionary saga culminates in a symphony of enhancements that not only elevate the framework’s capabilities but also redefine its role as an indispensable asset in the arsenal of AI practitioners. Redefining Possibilities — Unveiling Keras 3.0’s Game-Changing Features Embracing the Multi-Backend Landscape Keras 3.0 emerges as a trailblazer with its unprecedented support for multiple backends. While its roots are anchored in TensorFlow, this version casts a wider net, inviting frameworks like jAX and PyTorch into its fold. The result? A harmonious coexistence that empowers researchers and practitioners to wield their preferred framework without renouncing the prowess of Keras. Precision Perfected — Advanced Performance Optimization Keras Core 3.0 doubles down on performance optimization, seamlessly weaving techniques like mixed-precision training and distributed training into its fabric. The result is a turbocharged training process and maximized hardware resource utilization. These optimization strategies work behind the scenes, enabling users to focus on the art of model development and experimentation, confident that the framework is orchestrating the complex ballet beneath. Expanding the Horizons — A Flourishing Ecosystem The Keras ecosystem flourishes with renewed vigour in Keras 3.0. The framework’s enhanced support for KerasCV and KerasNLP, specialized libraries tailored for computer vision and natural language processing, empowers it to excel in these domains. This synergy doesn’t just streamline the development process; it equips users with an extensive toolkit to conquer the intricate challenges inherent in these fields. Uniting the Diverse — Cross-Framework Compatibility Keras Core 3.0 ushers in an era of harmony across deep learning frameworks. Models crafted in Keras effortlessly traverse the boundaries between TensorFlow, jAX, and PyTorch backends, reflecting a unification in an ecosystem historically divided. This seamless compatibility erases barriers, fostering an environment of collaboration and experimentation, where diverse tools coalesce to drive innovation. Evolution by Design — The Philosophy of Progressive Disclosure Keras 3.0 embodies the ethos of progressive disclosure, catering to both novices and seasoned practitioners. The API unfolds in a manner that facilitates the gentle onboarding of newcomers while gradually unveiling the advanced features craved by experts. This balanced approach ensures Keras remains accessible and indispensable, irrespective of users’ proficiency levels. A Stateless Symphony of Design — The Stateless API Paradigm The introduction of the stateless API marks a paradigm shift in Keras 3.0. Aligned with the trend of integrating functional programming concepts in deep learning, this design choice fosters modular architecture, encourages code reusability, and champions clean code organization. This leap not only elevates the development experience but also fortifies code maintenance and collaborative prowess. Navigating the Possibilities — Keras for TensorFlow, jAX, and PyTorch Embarking on the Voyage: Installation Embarking on the journey with Keras Core 3.0 is an effortless endeavour. Installation guides for each supported backend are readily available in the official documentation, providing users the freedom to opt for the backend that resonates with their ethos and project requisites. This adaptability cements Keras as an indispensable entity amid the ever-shifting currents of AI technology. For installation, $ pip install keras-core import keras_core as keras Aligning with the Core: Backend Configuration Configuring the backend is a seamless ritual, often requiring a mere few lines of code. This configuration determines the engine propelling Keras—be it TensorFlow, jAX, or PyTorch. This flexibility empowers users to fluidly transition between backends, paving the way for efficient exploration and experimentation. Run the following command for backend configuration: $ export KERAS_BACKEND="jax" $ python train.py Or $ KERAS_BACKEND=jax python train.py Mastery in Action: Integrating KerasCV and KerasNLP The integration of KerasCV and KerasNLP into Keras Core 3.0 paints a transformative landscape. KerasCV brings forth a symphony for computer vision tasks, providing dedicated APIs and pre-fabricated models for image classification, object detection, and segmentation. Meanwhile, KerasNLP empowers users to navigate the challenges of natural language processing with access to cutting-edge language models, tokenization tools, and sequence manipulation layers. And here is some KerasCV usage example: import keras_cv import keras_core as keras filepath = keras.utils.get_file(origin="https://i.imgur.com/gCNcJJI.jpg") image = np.array(keras.utils.load_img(filepath)) image_resized = ops.image.resize(image, (640, 640))[None, …] model = keras_cv.models.YOLOV8Detector.from_preset( "yolo_v8_m_pascalvoc", bounding_box_format="xywh", ) predictions = model.predict(image_resized) A Confluence of Innovation: In the ever-accelerating tapestry of deep learning, Keras Core 3.0 emerges as a beacon of innovation and adaptability. With its embrace of multiple backends, advanced performance optimization, amplified ecosystem, cross-framework harmony, philosophy of progressive disclosure, and the advent of the stateless API, Keras 3.0 redefines itself as the quintessential deep learning API. It resonates across the spectrum of users—novices venturing forth and experts charting the boundaries of possibility. As the grand symphony of deep learning unfolds, Keras Core 3.0 remains a steadfast companion, empowering developers to manifest their visions with unmatched finesse and precision.
GitOps Explained — Principles, Deployment, and Best Practices
In the ever-evolving landscape of software development, efficient deployment practices have become crucial to stay ahead of the competition and deliver high-quality products. GitOps has emerged as a cutting-edge methodology, fostering collaboration, scalability, and reliability. As it leverages version control systems like Git, GitOps enables teams to maintain a declarative representation of the desired system state, streamlining the deployment process and ensuring consistency across different environments. From understanding the core components of GitOps to implementing the most effective deployment strategies, this article aims to equip readers with the knowledge and tools to excel in the dynamic world of modern software deployment. So, let’s get started! What is GitOps? — Core Components to Excel GitOps is an advanced software deployment paradigm that revolves around the principle of “desired state” and Git version control system. The core concept involves describing the desired configuration and state of a system declaratively and storing it in a Git repository. All changes to the system, whether they pertain to application code or infrastructure settings, are represented as Git commits in the repository. The Git repository serves as the single source of truth, reflecting the actual state of the system. CI/CD pipelines, tightly integrated with the Git repository, automatically detect changes and reconcile the system state with the desired state defined in Git. The entire deployment process is thus version-controlled and auditable. CI/CD (Continuous Integration and Continuous Deployment) Continuous Integration and Continuous Deployment (CI/CD) is a fundamental aspect of GitOps. CI/CD pipelines automate the process of building, testing, and deploying code changes to production or staging environments. By integrating version control systems with CI/CD pipelines, GitOps ensures that every change made to the codebase goes through automated testing and verification before being deployed, enhancing the overall quality and reliability of the software. IaC (Infrastructure as Code) Infrastructure as Code (IaC) is a key principle in GitOps. It involves managing infrastructure configurations in a version-controlled manner, just like application code. As it describes the desired infrastructure state in code, GitOps allows teams to automate the provisioning of infrastructure resources. This approach eliminates manual setup and reduces the risk of configuration drift, leading to more predictable and reproducible deployments. MRs and PRs (Merge Requests and Pull Requests) Merge Requests (MRs) in GitLab or Pull Requests (PRs) in GitHub play a crucial role in the GitOps workflow. They serve as collaboration platforms for code reviews and discussions among team members. Before changes are applied, they undergo thorough review, testing, and validation through MRs or PRs, ensuring that only well-tested and approved changes are merged into the main codebase. Principles of GitOps Declarative System The declarative system is a fundamental concept in GitOps. Instead of defining the sequence of actions to achieve a particular state, GitOps focuses on describing the desired end state of the system. The Git repository serves as the single source of truth, containing all the necessary configurations to achieve that state. As a result, GitOps ensures that the system converges to the desired state automatically, making it easier to manage and audit changes. System State Captured in a Git Repository GitOps relies on maintaining a Git repository as the central repository for the desired system state. All changes, whether related to infrastructure or application code, are committed and version-controlled in the repository. This approach provides a historical record of changes, enabling teams to track the evolution of the system and easily roll back to a previous known state if needed. Automatic Deployment With GitOps, deployments are automated based on changes to the Git repository. Whenever a new commit is pushed to the repository, the system automatically applies the changes to the target environment. This automation reduces manual intervention, minimizes the risk of human errors during deployments, and ensures a consistent and reliable deployment process. GitOps Deployment Strategies Rolling Strategy The Rolling Strategy is a deployment approach where new changes are incrementally rolled out to the target environment while the existing version remains operational. This gradual deployment minimizes downtime and allows for easy rollbacks if issues arise during the deployment process. Canary Deployment Canary Deployment is a technique where a small subset of users or servers receives the new changes while the majority continues to use the existing version. This approach helps validate the changes in a real-world environment with reduced risk. If the canary group shows positive results, the changes are gradually rolled out to the entire system, ensuring a smooth transition. Blue-Green Deployment Blue-Green Deployment involves maintaining two identical environments—blue and green. The current version of the application runs in one environment (e.g., blue), while the new version is deployed to the other (e.g., green). Once the green environment is thoroughly tested and verified, traffic is switched from the blue to the green environment, making it the new production version. This approach enables seamless rollbacks if issues are discovered during the deployment. A/B Deployment A/B Deployment, also known as Feature Toggling, allows for deploying multiple versions of a feature simultaneously. This approach enables teams to test different implementations or user experiences and analyze their performance and impact on users. By gradually exposing different features to different user groups, A/B Deployment allows for data-driven decision-making and fine-tuning of features before full rollout. GitOps Best Practices Avoid Mixed Environments Maintain clear separation between development, staging, and production environments. Avoid mixing different configurations or states, as this can lead to unpredictable outcomes and make it challenging to identify the root cause of issues. Separating environments ensures that changes are tested thoroughly in isolated environments before reaching production. Leverage the Request Discussion Encourage collaboration and knowledge sharing among team members by using MRs or PRs as platforms for discussions. This ensures that all changes are thoroughly reviewed, tested, and approved by relevant stakeholders before being deployed. Discussions within MRs or PRs provide valuable feedback and foster a culture of continuous improvement. Policy as Code Implementing policies as code helps ensure compliance with organizational standards and best practices. Policies defined in code are version-controlled and can be automatically enforced during
NetDevOps — A Comprehensive Guide with Components and Obstacles
Considering the automation through Agile development processes, the software development industry has experienced a massive shift towards NetDevOps. The credit goes to its underlying network infrastructure offering network automation to fast-paced modern businesses. Since the non-DevOps approach hovers around tools, developers may experience a lack of traceability, testing, and collaboration. Here NetDevOps can help you cop with these limitations and eliminate security vulnerabilities while ensuring expected performance. Similarly, there’s a glut of things you need to know about NetDevOps if you’re looking to incorporate it into your development process. This guide will lead you to the various NetDevOps components and obstacles for a better understanding. What is NetDevOps and Why is it Worth Using? As the term describes itself, NetDevOps is a technical blend of Networking and DevOps. It streamlines the DevOps principles for the deployment and management of network services. If we dig deeper, NetDevOps apply CI/CD DevOps concepts to networking activities for faster delivery. In addition to this, its automated workflows bolster the abstraction, codification, and Infrastructure as Code (IaC) implementation. NetDevOps also eliminate the configuration drift to embed quality and resiliency within the network. In a nutshell, it improves agility by driving clear workflows aiding auditing, governance, and troubleshooting. Challenges You May Face During NetDevOps Development Risk Aversion One of the challenges that organizations may face during NetDevOps development is risk aversion. Many companies are hesitant to adopt new technologies and practices due to the fear of potential failures or disruptions to their existing network infrastructure. This risk aversion can hinder the adoption of NetDevOps methodologies, which emphasize automation, continuous integration, and continuous delivery. To address this challenge, organizations need to focus on building trust by demonstrating the benefits and success stories of NetDevOps implementation. Technical Debt Technical debt refers to the accumulated shortcuts, workarounds, and suboptimal code or configurations that result from rushed or incomplete implementation of network automation processes. This can lead to various issues, including increased complexity, reduced maintainability, and decreased scalability. To mitigate technical debt, organizations should prioritize code quality, conduct regular code reviews, and follow established best practices and coding standards. Implementing automated testing frameworks and leveraging continuous integration and delivery pipelines can help identify and address technical debt early in the development process. Skills Shortage NetDevOps development requires a unique set of skills that combine network engineering, software development, and automation expertise. However, finding individuals with a strong skill set in these areas can be challenging due to the shortage of qualified professionals. To address this issue, organizations can invest in training and upskilling their existing network and IT teams. This can include providing access to relevant courses, certifications, and hands-on training programs. Collaboration with external training providers or universities can also help bridge the skills gap. Documentation Effective documentation plays a crucial role in NetDevOps development, as it ensures that network configurations, automation workflows, and troubleshooting processes are well-documented and accessible to the team. However, maintaining up-to-date and comprehensive documentation can be challenging, especially when changes occur rapidly in dynamic network environments. Organizations can address this challenge by adopting documentation frameworks and tools that facilitate automated documentation generation. Version control systems, wiki platforms, and collaborative document editing tools can also help streamline the documentation process. Unstandardized Data NetDevOps development relies on gathering and analyzing network data to drive automation and decision-making processes. However, network data can be highly diverse and unstandardized, making it challenging to extract meaningful insights and build reliable automation workflows. Organizations should invest in data normalization and standardization techniques to ensure consistency and compatibility across different data sources. This can include using standardized data models, implementing data transformation pipelines, and leveraging data analytics tools for data cleansing and preprocessing. Tool Limitations NetDevOps development often requires the use of various tools and technologies, including network configuration management systems, automation frameworks, and orchestration platforms. However, tool limitations can arise, such as a lack of integration capabilities, limited scalability, or inadequate support for specific network devices or protocols. To overcome these challenges, organizations should thoroughly evaluate and choose tools that align with their specific requirements and network environment. They should also consider open-source solutions that offer flexibility and community support. Top NetDevOps Components Modularity Modularity is a key component of NetDevOps, enabling the creation of flexible and scalable network architectures. By breaking down network systems into modular components, organizations can easily adapt and scale their networks as per evolving requirements. Modularity facilitates the deployment of microservices, allowing for the independent development and deployment of specific network functionalities. This approach not only enhances agility but also simplifies troubleshooting and maintenance, as issues can be isolated to specific modules. For instance, using containerization technologies like Docker, network functions can be encapsulated within lightweight, portable containers, ensuring consistent behavior across different environments. Example 1 – Multiple applications in a single VPC network architecture Example 2 – Single application per VPC network architecture Cultural Changes Cultural changes play a crucial role in successfully implementing NetDevOps. Traditionally, network and operations teams operated in silos, with limited collaboration between them. However, NetDevOps encourages a cultural shift towards increased collaboration, communication, and shared responsibility. By fostering a DevOps culture, organizations can break down barriers between different teams, promoting a collaborative approach to network management. This cultural shift involves embracing shared goals, establishing cross-functional teams, and encouraging continuous learning and skill development. Automation and Infrastructure as Code Automation and Infrastructure as Code (IaC) are pivotal components of NetDevOps, enabling organizations to achieve faster and more efficient network deployments. Automation eliminates manual, error-prone tasks and accelerates the provisioning and configuration of network devices. Tools like Ansible, Puppet, or Chef enable the automation of network device configurations, ensuring consistency and reducing human errors. Infrastructure as Code allows network infrastructure to be defined and managed through machine-readable configuration files, promoting version control and reproducibility. Continuous Integration/Continuous Deployment Continuous Integration/Continuous Deployment (CI/CD) practices are integral to NetDevOps, enabling organizations to rapidly and reliably deploy network changes. CI/CD pipelines automate the process of integrating code changes, testing them, and deploying them to
