{"id":13996,"date":"2024-08-27T09:59:40","date_gmt":"2024-08-27T09:59:40","guid":{"rendered":"https:\/\/dailyai.com\/?p=13996"},"modified":"2024-08-27T09:59:40","modified_gmt":"2024-08-27T09:59:40","slug":"the-future-of-ai-training-distros-game-changing-approach","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/08\/the-future-of-ai-training-distros-game-changing-approach\/","title":{"rendered":"Fremtiden for AI-tr\u00e6ning: DisTrO's banebrydende tilgang"},"content":{"rendered":"<p><strong>Den anvendte AI-forskningsgruppe Nous Research har udviklet en tr\u00e6ningsoptimering til AI-modeller, som kan \u00e6ndre den m\u00e5de, fremtidens AI-modeller bliver tr\u00e6net p\u00e5, dramatisk.<\/strong><\/p>\n<p>Traditionelt kr\u00e6ver tr\u00e6ning af en AI-model massive datacentre fyldt med GPU'er som NVIDIAs H100'ere og h\u00f8jhastighedsforbindelser til at synkronisere gradient- og parameteropdateringer mellem GPU'erne.<\/p>\n<p>Hvert tr\u00e6ningstrin kr\u00e6ver store m\u00e6ngder data, der skal deles mellem tusindvis af GPU'er. Den n\u00f8dvendige b\u00e5ndbredde betyder, at disse GPU'er skal v\u00e6re fastkoblede og fysisk t\u00e6t p\u00e5 hinanden. Med DisTrO har Nous Research m\u00e5ske fundet en m\u00e5de at \u00e6ndre det fuldst\u00e6ndigt p\u00e5.<\/p>\n<p>N\u00e5r en model tr\u00e6nes, justerer en optimeringsalgoritme modellens parametre for at minimere tabsfunktionen. Tabsfunktionen m\u00e5ler forskellen mellem modellens forudsigelser og de faktiske resultater, og m\u00e5let er at reducere dette tab s\u00e5 meget som muligt gennem iterativ tr\u00e6ning.<\/p>\n<p><a href=\"https:\/\/github.com\/NousResearch\/DisTrO\/blob\/main\/A_Preliminary_Report_on_DisTrO.pdf\" target=\"_blank\" rel=\"noopener\">DisTrO-AdamW<\/a> er en variant af den popul\u00e6re AdamW-optimeringsalgoritme. DisTrO st\u00e5r for \"Distributed Training Over-the-Internet\" og hentyder til, hvad der g\u00f8r den s\u00e5 speciel.<\/p>\n<p>DisTrO-AdamW reducerer drastisk m\u00e6ngden af inter-GPU-kommunikation, der kr\u00e6ves under tr\u00e6ningen af store neurale netv\u00e6rk. Og det sker uden at g\u00e5 p\u00e5 kompromis med konvergenshastigheden eller n\u00f8jagtigheden af tr\u00e6ningsprocessen.<\/p>\n<p>I empiriske tests opn\u00e5ede DisTrO-AdamW en 857x reduktion i inter-GPU-kommunikation. Det betyder, at DisTrO-tilgangen kan tr\u00e6ne modeller med sammenlignelig n\u00f8jagtighed og hastighed, men uden behov for dyr hardware med h\u00f8j b\u00e5ndbredde.<\/p>\n<p>For eksempel matchede DisTrO-AdamW under pr\u00e6tr\u00e6ningen af en 1,2 milliarder LLM de traditionelle metoders ydeevne, samtidig med at den kr\u00e6vede b\u00e5ndbredde blev reduceret fra 74,4 GB til kun 86,8 MB pr. tr\u00e6ningstrin.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Hvad nu, hvis man kunne bruge al verdens computerkraft til at tr\u00e6ne en f\u00e6lles open source AI-model?<\/p>\n<p>Forel\u00f8big rapport: <a href=\"https:\/\/t.co\/b1XgJylsnV\">https:\/\/t.co\/b1XgJylsnV<\/a><\/p>\n<p>Nous Research er stolte over at kunne offentligg\u00f8re en forel\u00f8big rapport om DisTrO (Distributed Training Over-the-Internet), en familie af... <a href=\"https:\/\/t.co\/h2gQJ4m7lB\">pic.twitter.com\/h2gQJ4m7lB<\/a><\/p>\n<p>- Nous Research (@NousResearch) <a href=\"https:\/\/twitter.com\/NousResearch\/status\/1828121648383566270?ref_src=twsrc%5Etfw\">26. august 2024<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2>Konsekvenser for AI-tr\u00e6ning<\/h2>\n<p>DisTrO's indvirkning p\u00e5 AI-landskabet kan v\u00e6re dybtg\u00e5ende. Ved at reducere kommunikationsomkostningerne giver DisTrO mulighed for decentral tr\u00e6ning af store modeller. I stedet for et datacenter med tusindvis af GPU'er og h\u00f8jhastighedsswitche kan man tr\u00e6ne en model p\u00e5 distribueret kommerciel hardware, der er forbundet via internettet.<\/p>\n<p>Man kunne have et f\u00e6llesskab af mennesker, der bidrog med adgang til deres computerhardware for at tr\u00e6ne en model. Forestil dig millioner af inaktive pc'er eller overfl\u00f8dige Bitcoin-minerigge, der arbejder sammen om at tr\u00e6ne en open source-model. DisTrO g\u00f8r det muligt, og det g\u00e5r n\u00e6sten ikke ud over tiden til at tr\u00e6ne modellen eller dens n\u00f8jagtighed.<\/p>\n<p>Nous Research indr\u00f8mmer, at de ikke er helt sikre p\u00e5, hvorfor deres tilgang fungerer s\u00e5 godt, og at der er brug for mere forskning for at se, om den kan skaleres til st\u00f8rre modeller.<\/p>\n<p>Hvis det sker, er det ikke l\u00e6ngere sikkert, at Big Tech-virksomheder med penge til store datacentre har monopol p\u00e5 massive tr\u00e6ningsmodeller. Det kan ogs\u00e5 have en stor indvirkning ved at reducere <a href=\"https:\/\/dailyai.com\/da\/2024\/06\/googles-search-engine-experience-sge-threatens-to-scale-ais-environmental-impacts\/\">milj\u00f8p\u00e5virkning<\/a> af energi- og vandkr\u00e6vende datacentre.<\/p>\n<p>Konceptet med decentral tr\u00e6ning kan ogs\u00e5 g\u00f8re nogle aspekter af regler som <a href=\"https:\/\/dailyai.com\/da\/2024\/07\/proposed-california-bills-could-be-disastrous-for-ai-development\/\">Californiens lovforslag SB 1047<\/a> Det kan diskuteres. Lovforslaget kr\u00e6ver yderligere sikkerhedstjek for modeller, der koster mere end $100 millioner at uddanne.<\/p>\n<p>Med DisTrO kan et f\u00e6llesskab af anonyme mennesker med distribueret hardware skabe deres egen \"supercomputer\" til at tr\u00e6ne en model. Det kunne ogs\u00e5 oph\u00e6ve <a href=\"https:\/\/dailyai.com\/da\/2023\/10\/the-us-government-moves-to-further-restrict-tech-exports-to-china\/\"><span class=\"noTranslate\" data-no-translation=\"\"><span class=\"noTranslate\" data-no-translation=\"\">US<\/span><\/span> regeringens bestr\u00e6belser p\u00e5 at stoppe Kina<\/a> fra at importere NVIDIA's mest kraftfulde GPU'er.<\/p>\n<p>I en verden, hvor AI bliver stadig vigtigere, giver DisTrO et glimt af en fremtid, hvor udviklingen af disse kraftfulde v\u00e6rkt\u00f8jer er mere inkluderende, b\u00e6redygtig og udbredt.<\/p>","protected":false},"excerpt":{"rendered":"<p>Den anvendte AI-forskningsgruppe Nous Research har udviklet en optimering af tr\u00e6ningen af AI-modeller, som kan \u00e6ndre den m\u00e5de, fremtidens AI-modeller tr\u00e6nes p\u00e5, dramatisk. Traditionelt kr\u00e6ver tr\u00e6ning af en AI-model massive datacentre fyldt med GPU'er som NVIDIAs H100'er og h\u00f8jhastighedsforbindelser til at synkronisere gradient- og parameteropdateringer mellem GPU'er. Hvert tr\u00e6ningstrin kr\u00e6ver store m\u00e6ngder data, der skal deles mellem tusindvis af GPU'er. Den kr\u00e6vede b\u00e5ndbredde betyder, at disse GPU'er skal v\u00e6re hardwired og fysisk t\u00e6t p\u00e5 hinanden. Med DisTrO har Nous Research m\u00e5ske fundet en m\u00e5de at \u00e6ndre det fuldst\u00e6ndigt p\u00e5. N\u00e5r en model er<\/p>","protected":false},"author":6,"featured_media":13999,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,624],"class_list":["post-13996","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-nous-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The future of AI training: DisTrO\u2019s game-changing approach | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/da\/2024\/08\/the-future-of-ai-training-distros-game-changing-approach\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The future of AI training: DisTrO\u2019s game-changing approach | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Applied AI research group Nous Research developed an AI model training optimizer that could dramatically change the way AI models of the future will be trained. Traditionally, training an AI model requires massive data centers packed with GPUs like NVIDIA\u2019s H100s, and high-speed interconnects to synchronize gradient and parameter updates between GPUs. Each training step requires vast amounts of data to be shared between thousands of GPUs. The required bandwidth means these GPUs need to be hardwired and physically close to each other. With DisTrO, Nous Research may have found a way to change that completely. 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