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<!DOCTYPE html> <html lang="en-US"> <head> <meta charset="UTF-8"> <meta name="apple-mobile-web-app-capable" content="yes"/> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="format-detection" content="telephone=no"/> <meta name='robots' content='index, follow, max-image-preview:large, max-snippet:-1, max-video-preview:-1' /> <style>img:is([sizes="auto" i], [sizes^="auto," i]) { contain-intrinsic-size: 3000px 1500px }</style> <link rel="alternate" hreflang="en" href="https://gcore.com/learning/run-huggingface-spaces-on-gcore-inference-at-the-edge/" /> <link rel="alternate" hreflang="x-default" href="https://gcore.com/learning/run-huggingface-spaces-on-gcore-inference-at-the-edge/" />  <title>How to Run Hugging Face Spaces on Gcore Inference at the Edge | Gcore</title> <meta name="description" content="Discover how to harness the power of Gcore Inference at the Edge to deploy a Hugging Face Spaces in a few clicks." /> <link rel="canonical" href="https://gcore.com/learning/run-huggingface-spaces-on-gcore-inference-at-the-edge/" /> <meta property="og:locale" content="en_US" /> <meta property="og:type" content="article" /> <meta property="og:title" content="How to Run Hugging Face Spaces on Gcore Inference at the Edge | Gcore" /> <meta property="og:description" content="Discover how to harness the power of Gcore Inference at the Edge to deploy a Hugging Face Spaces in a few clicks." /> <meta property="og:url" content="https://gcore.com/learning/run-huggingface-spaces-on-gcore-inference-at-the-edge/" /> <meta property="og:site_name" content="Gcore" /> <meta property="article:publisher" content="https://www.facebook.com/gcorelabscom" /> <meta property="article:modified_time" content="2024-11-26T11:48:05+00:00" /> <meta property="og:image" content="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-1.jpg" /> <meta property="og:image:width" content="1600" /> <meta property="og:image:height" content="1200" /> <meta property="og:image:type" content="image/jpeg" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@gcore_official" /> <meta name="twitter:label1" content="Est. reading time" /> <meta name="twitter:data1" content="4 minutes" />  <link rel='dns-prefetch' href='//maps.googleapis.com' /> <link rel="alternate" type="application/rss+xml" title="Gcore » Feed" href="https://gcore.com/learning/feed/" /> <script type="text/javascript"> /* <![CDATA[ */ window._wpemojiSettings = {"baseUrl":"https:\/\/s.w.org\/images\/core\/emoji\/15.0.3\/72x72\/","ext":".png","svgUrl":"https:\/\/s.w.org\/images\/core\/emoji\/15.0.3\/svg\/","svgExt":".svg","source":{"wpemoji":"https:\/\/gcore.com\/wp-includes\/js\/wp-emoji.js?ver=6.7.2","twemoji":"https:\/\/gcore.com\/wp-includes\/js\/twemoji.js?ver=6.7.2"}}; /** * @output wp-includes/js/wp-emoji-loader.js */ /** * Emoji Settings as exported in PHP via _print_emoji_detection_script(). * @typedef WPEmojiSettings * @type {object} * @property {?object} source * @property {?string} source.concatemoji * @property {?string} source.twemoji * @property {?string} source.wpemoji * @property {?boolean} DOMReady * @property {?Function} readyCallback */ /** * Support tests. * @typedef SupportTests * @type {object} * @property {?boolean} flag * @property {?boolean} emoji */ /** * IIFE to detect emoji support and load Twemoji if needed. * * @param {Window} window * @param {Document} document * @param {WPEmojiSettings} settings */ ( function wpEmojiLoader( window, document, settings ) { if ( typeof Promise === 'undefined' ) { return; } var sessionStorageKey = 'wpEmojiSettingsSupports'; var tests = [ 'flag', 'emoji' ]; /** * Checks whether the browser supports offloading to a Worker. * * @since 6.3.0 * * @private * * @returns {boolean} */ function supportsWorkerOffloading() { return ( typeof Worker !== 'undefined' && typeof OffscreenCanvas !== 'undefined' && typeof URL !== 'undefined' && URL.createObjectURL && typeof Blob !== 'undefined' ); } /** * @typedef SessionSupportTests * @type {object} * @property {number} timestamp * @property {SupportTests} supportTests */ /** * Get support tests from session. * * @since 6.3.0 * * @private * * @returns {?SupportTests} Support tests, or null if not set or older than 1 week. */ function getSessionSupportTests() { try { /** @type {SessionSupportTests} */ var item = JSON.parse( sessionStorage.getItem( sessionStorageKey ) ); if ( typeof item === 'object' && typeof item.timestamp === 'number' && new Date().valueOf() < item.timestamp + 604800 && // Note: Number is a week in seconds. typeof item.supportTests === 'object' ) { return item.supportTests; } } catch ( e ) {} return null; } /** * Persist the supports in session storage. * * @since 6.3.0 * * @private * * @param {SupportTests} supportTests Support tests. */ function setSessionSupportTests( supportTests ) { try { /** @type {SessionSupportTests} */ var item = { supportTests: supportTests, timestamp: new Date().valueOf() }; sessionStorage.setItem( sessionStorageKey, JSON.stringify( item ) ); } catch ( e ) {} } /** * Checks if two sets of Emoji characters render the same visually. * * This function may be serialized to run in a Worker. Therefore, it cannot refer to variables from the containing * scope. Everything must be passed by parameters. * * @since 4.9.0 * * @private * * @param {CanvasRenderingContext2D} context 2D Context. * @param {string} set1 Set of Emoji to test. * @param {string} set2 Set of Emoji to test. * * @return {boolean} True if the two sets render the same. */ function emojiSetsRenderIdentically( context, set1, set2 ) { // Cleanup from previous test. context.clearRect( 0, 0, context.canvas.width, context.canvas.height ); context.fillText( set1, 0, 0 ); var rendered1 = new Uint32Array( context.getImageData( 0, 0, context.canvas.width, context.canvas.height ).data ); // Cleanup from previous test. context.clearRect( 0, 0, context.canvas.width, context.canvas.height ); context.fillText( set2, 0, 0 ); var rendered2 = new Uint32Array( context.getImageData( 0, 0, context.canvas.width, context.canvas.height ).data ); return rendered1.every( function ( rendered2Data, index ) { return rendered2Data === rendered2[ index ]; } ); } /** * Determines if the browser properly renders Emoji that Twemoji can supplement. * * This function may be serialized to run in a Worker. Therefore, it cannot refer to variables from the containing * scope. Everything must be passed by parameters. * * @since 4.2.0 * * @private * * @param {CanvasRenderingContext2D} context 2D Context. * @param {string} type Whether to test for support of "flag" or "emoji". * @param {Function} emojiSetsRenderIdentically Reference to emojiSetsRenderIdentically function, needed due to minification. * * @return {boolean} True if the browser can render emoji, false if it cannot. */ function browserSupportsEmoji( context, type, emojiSetsRenderIdentically ) { var isIdentical; switch ( type ) { case 'flag': /* * Test for Transgender flag compatibility. Added in Unicode 13. * * To test for support, we try to render it, and compare the rendering to how it would look if * the browser doesn't render it correctly (white flag emoji + transgender symbol). */ isIdentical = emojiSetsRenderIdentically( context, '\uD83C\uDFF3\uFE0F\u200D\u26A7\uFE0F', // as a zero-width joiner sequence '\uD83C\uDFF3\uFE0F\u200B\u26A7\uFE0F' // separated by a zero-width space ); if ( isIdentical ) { return false; } /* * Test for UN flag compatibility. This is the least supported of the letter locale flags, * so gives us an easy test for full support. * * To test for support, we try to render it, and compare the rendering to how it would look if * the browser doesn't render it correctly ([U] + [N]). */ isIdentical = emojiSetsRenderIdentically( context, '\uD83C\uDDFA\uD83C\uDDF3', // as the sequence of two code points '\uD83C\uDDFA\u200B\uD83C\uDDF3' // as the two code points separated by a zero-width space ); if ( isIdentical ) { return false; } /* * Test for English flag compatibility. England is a country in the United Kingdom, it * does not have a two letter locale code but rather a five letter sub-division code. * * To test for support, we try to render it, and compare the rendering to how it would look if * the browser doesn't render it correctly (black flag emoji + [G] + [B] + [E] + [N] + [G]). */ isIdentical = emojiSetsRenderIdentically( context, // as the flag sequence '\uD83C\uDFF4\uDB40\uDC67\uDB40\uDC62\uDB40\uDC65\uDB40\uDC6E\uDB40\uDC67\uDB40\uDC7F', // with each code point separated by a zero-width space '\uD83C\uDFF4\u200B\uDB40\uDC67\u200B\uDB40\uDC62\u200B\uDB40\uDC65\u200B\uDB40\uDC6E\u200B\uDB40\uDC67\u200B\uDB40\uDC7F' ); return ! isIdentical; case 'emoji': /* * Four and twenty blackbirds baked in a pie. * * To test for Emoji 15.0 support, try to render a new emoji: Blackbird. * * The Blackbird is a ZWJ sequence combining 🐦 Bird and ⬛ large black square., * * 0x1F426 (\uD83D\uDC26) == Bird * 0x200D == Zero-Width Joiner (ZWJ) that links the code points for the new emoji or * 0x200B == Zero-Width Space (ZWS) that is rendered for clients not supporting the new emoji. * 0x2B1B == Large Black Square * * When updating this test for future Emoji releases, ensure that individual emoji that make up the * sequence come from older emoji standards. */ isIdentical = emojiSetsRenderIdentically( context, '\uD83D\uDC26\u200D\u2B1B', // as the zero-width joiner sequence '\uD83D\uDC26\u200B\u2B1B' // separated by a zero-width space ); return ! isIdentical; } return false; } /** * Checks emoji support tests. * * This function may be serialized to run in a Worker. Therefore, it cannot refer to variables from the containing * scope. Everything must be passed by parameters. * * @since 6.3.0 * * @private * * @param {string[]} tests Tests. * @param {Function} browserSupportsEmoji Reference to browserSupportsEmoji function, needed due to minification. * @param {Function} emojiSetsRenderIdentically Reference to emojiSetsRenderIdentically function, needed due to minification. * * @return {SupportTests} Support tests. */ function testEmojiSupports( tests, browserSupportsEmoji, emojiSetsRenderIdentically ) { var canvas; if ( typeof WorkerGlobalScope !== 'undefined' && self instanceof WorkerGlobalScope ) { canvas = new OffscreenCanvas( 300, 150 ); // Dimensions are default for HTMLCanvasElement. } else { canvas = document.createElement( 'canvas' ); } var context = canvas.getContext( '2d', { willReadFrequently: true } ); /* * Chrome on OS X added native emoji rendering in M41. Unfortunately, * it doesn't work when the font is bolder than 500 weight. So, we * check for bold rendering support to avoid invisible emoji in Chrome. */ context.textBaseline = 'top'; context.font = '600 32px Arial'; var supports = {}; tests.forEach( function ( test ) { supports[ test ] = browserSupportsEmoji( context, test, emojiSetsRenderIdentically ); } ); return supports; } /** * Adds a script to the head of the document. * * @ignore * * @since 4.2.0 * * @param {string} src The url where the script is located. * * @return {void} */ function addScript( src ) { var script = document.createElement( 'script' ); script.src = src; script.defer = true; document.head.appendChild( script ); } settings.supports = { everything: true, everythingExceptFlag: true }; // Create a promise for DOMContentLoaded since the worker logic may finish after the event has fired. var domReadyPromise = new Promise( function ( resolve ) { document.addEventListener( 'DOMContentLoaded', resolve, { once: true } ); } ); // Obtain the emoji support from the browser, asynchronously when possible. new Promise( function ( resolve ) { var supportTests = getSessionSupportTests(); if ( supportTests ) { resolve( supportTests ); return; } if ( supportsWorkerOffloading() ) { try { // Note that the functions are being passed as arguments due to minification. var workerScript = 'postMessage(' + testEmojiSupports.toString() + '(' + [ JSON.stringify( tests ), browserSupportsEmoji.toString(), emojiSetsRenderIdentically.toString() ].join( ',' ) + '));'; var blob = new Blob( [ workerScript ], { type: 'text/javascript' } ); var worker = new Worker( URL.createObjectURL( blob ), { name: 'wpTestEmojiSupports' } ); worker.onmessage = function ( event ) { supportTests = event.data; setSessionSupportTests( supportTests ); worker.terminate(); resolve( supportTests ); }; return; } catch ( e ) {} } supportTests = testEmojiSupports( tests, browserSupportsEmoji, emojiSetsRenderIdentically ); setSessionSupportTests( supportTests ); resolve( supportTests ); } ) // Once the browser emoji support has been obtained from the session, finalize the settings. .then( function ( supportTests ) { /* * Tests the browser support for flag emojis and other emojis, and adjusts the * support settings accordingly. */ for ( var test in supportTests ) { settings.supports[ test ] = supportTests[ test ]; settings.supports.everything = settings.supports.everything && settings.supports[ test ]; if ( 'flag' !== test ) { settings.supports.everythingExceptFlag = settings.supports.everythingExceptFlag && settings.supports[ test ]; } } settings.supports.everythingExceptFlag = settings.supports.everythingExceptFlag && ! 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Dedicated Servers </li> </ul> </div> </div> </div> </div> </div> </div> <section class="section section_pt-s"> <div class="container"> <div class="section__breadcrumbs"> <div class="breadcrumbs"> <ol itemscope itemtype="https://schema.org/BreadcrumbList"><li itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem"><a itemprop="item" href="https://gcore.com"><span itemprop="name">Home</span></a><meta itemprop="position" content="1" /></li><li itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem"><a itemprop="item" href="https://gcore.com/learning/"><span itemprop="name">Learning</span></a><meta itemprop="position" content="2" /></li><li itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem"><span itemprop="name">How to Run Hugging Face Spaces on Gcore Inference at the Edge</span><meta itemprop="position" content="3" /></li></ol> </div> </div><h1 class="section__title section__title_mid_big section__title_pt-b">How to Run Hugging Face Spaces on Gcore Inference at the Edge</h1><div class="section__published"> <div class="published"> October 17, 2024 <span> 2 min read </span> </div> <div class="share"> <a href="https://www.linkedin.com/cws/share?url=https://gcore.com/learning/run-huggingface-spaces-on-gcore-inference-at-the-edge/" rel="nofollow" target="_blank"> <svg xmlns="http://www.w3.org/2000/svg" width="48" height="48" fill="none" viewBox="0 0 48 48" aria-hidden="true"><path fill="#150C18" d="M9.609 19.068h6.329v19.045h-6.33V19.068Zm3.207-9.181c-2.166 0-3.582 1.422-3.582 3.288 0 1.828 1.374 3.292 3.499 3.292h.04c2.208 0 3.582-1.464 3.582-3.292-.04-1.866-1.374-3.288-3.54-3.288Zm18.661 8.733c-3.36 0-4.864 1.848-5.704 3.144v-2.696h-6.33c.083 1.786 0 19.045 0 19.045h6.33V27.477c0-.57.041-1.137.208-1.546.458-1.137 1.5-2.314 3.247-2.314 2.292 0 3.208 1.747 3.208 4.306v10.19h6.33V27.192c0-5.85-3.122-8.572-7.289-8.572Z"></path></svg> </a> <a 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13M20 20L13 27" stroke="#251B29" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/> </svg> </button> <div class="popup__image"></div> </div> </div><div class="section__content"> <div class="content"> <img src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-1.jpg" alt="How to Run Hugging Face Spaces on Gcore Inference at the Edge" class="content__thumbnail"> <p>Running machine learning models, especially large-scale models like GPT 3 or BERT, requires a lot of computing power and comes with a lot of latency. This makes real-time applications resource-intensive and challenging to deliver. Running ML models at the edge is a lightweight approach offering significant advantages for latency, privacy, and resource optimization.  </p> <p><a href="https://gcore.com/inference-at-the-edge" target="_blank" rel="noreferrer noopener">Gcore Inference at the Edge</a> makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. </p> <h2 id="step-1-log-in-to-the-gcore-customer-portal">Step 1: Log In to the Gcore Customer Portal </h2> <p>Go to <a href="http://gcore.com/" target="_blank" rel="noreferrer noopener">gcore.com</a> and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. </p> <h2 id="step-2-go-to-inference-at-the-edge">Step 2: Go to Inference at the Edge </h2> <p>In the Gcore Customer Portal, click <strong>Inference at the Edge</strong> from the left navigation menu. Then click <strong>Deploy custom model</strong>. </p> <figure class="wp-block-image size-full"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-1.png"></figure> <h2 id="step-3-choose-a-hugging-face-model">Step 3: Choose a Hugging Face Model </h2> <p>Open <a href="http://huggingface.com/" target="_blank" rel="noreferrer noopener">huggingface.com</a> and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. </p> <figure class="wp-block-image size-full"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-6.png"></figure> <p>Click on <strong>Files</strong> in the Space and locate the Docker option. </p> <figure class="wp-block-image size-full is-resized"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-3.png"></figure> <p>Copy the Docker image link and startup command from Hugging Face Space. </p> <figure class="wp-block-image size-full"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-2.png"></figure> <h2 id="step-4-deploy-the-model-on-gcore">Step 4: Deploy the Model on Gcore </h2> <p>Return to the Gcore Customer Portal deployment page and enter the following details: </p> <ul class="wp-block-list"> <li>Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest </li> <li>Startup command: python app.py </li> <li>Container port: 7860 </li> </ul> <p>Configure the pod as follows: </p> <ul class="wp-block-list"> <li>GPU-optimized: 1x L40S </li> <li>vCPUs: 16 </li> <li>RAM: 232GiB </li> </ul> <p>For optimal performance, choose any available region for routing placement. Name your deployment and click <strong>Deploy</strong>.</p> <figure class="wp-block-image size-full is-resized"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-4.png"></figure> <h2 id="step-5-interact-with-your-model">Step 5: Interact with Your Model </h2> <p>Once the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.</p> <figure class="wp-block-image size-full"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-1.png"></figure> <figure class="wp-block-image size-full"><img decoding="async" src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-7.png"></figure> <h2 id="powerful-simple-ai-deployment-with-gcore">Powerful, Simple AI Deployment with Gcore </h2> <p><a href="https://gcore.com/inference-at-the-edge" target="_blank" rel="noreferrer noopener">Gcore Inference at the Edge</a> is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. </p> <p>Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, <a href="https://gcore.com/inference-at-the-edge" target="_blank" rel="noreferrer noopener">Gcore Inference at the Edge</a> offers the tools you need to innovate at the speed of tomorrow. </p> <p><a class="gb-button" style="color:#ffffff;background-color:#ff5500" href="https://gcore.com/inference-at-the-edge">Explore Gcore Inference at the Edge</a></p> </div> </div><div class="section__sidebar section__sidebar_sticky"> <div class="sidebar sidebar_sticky"> <div class="sidebar__toc"> <h4>Table of contents</h4> <button><span>Step 1: Log In to the Gcore Customer Portal </span></button><ul id="toc"><li><a href="#step-1-log-in-to-the-gcore-customer-portal">Step 1: Log In to the Gcore Customer Portal </a></li><li><a href="#step-2-go-to-inference-at-the-edge">Step 2: Go to Inference at the Edge </a></li><li><a href="#step-3-choose-a-hugging-face-model">Step 3: Choose a Hugging Face Model </a></li><li><a href="#step-4-deploy-the-model-on-gcore">Step 4: Deploy the Model on Gcore </a></li><li><a href="#step-5-interact-with-your-model">Step 5: Interact with Your Model </a></li><li><a href="#powerful-simple-ai-deployment-with-gcore">Powerful, Simple AI Deployment with Gcore </a></li></ul></div><div class="sidebar__product"> <h4>Try Gcore AI</h4> <div> <a href="https://auth.gcore.com/login/signup">Deploy cluster</a> <a href="https://gcore.com/cloud/ai-platform">About</a> </div> </div> </div> </div> <div class="section__thumbnail section__thumbnail_mob"> <img src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2024/10/run-huggingface-spaces-on-gcore-inference-at-the-edge-1.jpg" alt="How to Run Hugging Face Spaces on Gcore Inference at the Edge"> </div> </div></div> <div class="section__related"> <div class="related"> <div class="container"> <div class="related__top"> <h3 class="related__title">Related articles</h3> <div class="related__controller"> <button class="related__arrow related__prev"></button> <button class="related__arrow related__next"></button> </div> </div> <div class="related__items swiper-container"> <div class="swiper-wrapper"> <div class="related__item swiper-slide"> <div class="section__item"> <a href="https://gcore.com/learning/why-do-bad-actors-carry-out-minecraft-ddos-attacks/" class="post-item"> <div class="post-item__image"> <img width="360" height="270" alt="Why do bad actors carry out Minecraft DDoS attacks?" class="lazy" src="data:image/gif;base64,R0lGODdhAQABAPAAAMPDwwAAACwAAAAAAQABAAACAkQBADs=" data-src="https://assets.gcore.pro/blog_containerizing_prod/uploads/2025/01/why-do-bad-actors-carry-out-minecraft-ddos-attacks-fi.png" data-srcset="https://assets.gcore.pro/blog_containerizing_prod/uploads/2025/01/why-do-bad-actors-carry-out-minecraft-ddos-attacks-fi.png 2x, https://assets.gcore.pro/blog_containerizing_prod/uploads/2025/01/why-do-bad-actors-carry-out-minecraft-ddos-attacks-fi.png 1x" /> </div> <div class="post-item__desc"> <h5 class="post-item__title">Why do bad actors carry out Minecraft DDoS attacks?</h5><div class="post-item__text">One of the most played video games in the world, Minecraft, relies on servers that are frequently a target of distributed denial-of-service (DDoS) attacks. 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