{"id":28348,"date":"2025-08-28T00:00:25","date_gmt":"2025-08-27T21:00:25","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/gpu-container-checkpoint-restore-ile-sifir-dusus-sureli-ml-is-yuku-gocu\/"},"modified":"2025-08-28T00:00:25","modified_gmt":"2025-08-27T21:00:25","slug":"gpu-container-checkpoint-restore-ile-sifir-dusus-sureli-ml-is-yuku-gocu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/gpu-container-checkpoint-restore-ile-sifir-dusus-sureli-ml-is-yuku-gocu\/","title":{"rendered":"GPU Container Checkpoint\/Restore ile S\u0131f\u0131r D\u00fc\u015f\u00fc\u015f S\u00fcreli ML \u0130\u015f Y\u00fck\u00fc G\u00f6\u00e7\u00fc"},"content":{"rendered":"

GPU Container Checkpoint\/Restore ile S\u0131f\u0131r D\u00fc\u015f\u00fc\u015f S\u00fcreli ML \u0130\u015f Y\u00fck\u00fc G\u00f6\u00e7\u00fc<\/p>\n

Makine \u00f6\u011frenmesi (ML) i\u015f y\u00fcklerinin h\u0131zla artan karma\u015f\u0131kl\u0131\u011f\u0131 ve \u00f6l\u00e7eklenebilirlik gereksinimleri, s\u00fcreklili\u011fi sa\u011flayan ve kesintisiz i\u015flemleri garanti eden \u00e7\u00f6z\u00fcmler gerektirir. CRI-O GPU ile container checkpoint\/restore (kontrol noktas\u0131\/geri y\u00fckleme) teknolojisi, bu ihtiyac\u0131 kar\u015f\u0131layarak s\u0131f\u0131r d\u00fc\u015f\u00fc\u015f s\u00fcresine sahip canl\u0131 g\u00f6\u00e7\u00fc m\u00fcmk\u00fcn k\u0131lar. Bu makalede, CRIU-gpu’nun temel prensiplerini, uygulamas\u0131n\u0131 ve ileri d\u00fczey kullan\u0131m senaryolar\u0131n\u0131, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle birlikte ele alaca\u011f\u0131z.<\/p>\n

CRI-O GPU: Temel Kavramlar Nedir?<\/h2>\n

CRI-O (Container Runtime Interface-Orthogonal), Kubernetes gibi container orchestration platformlar\u0131 i\u00e7in hafif ve g\u00fcvenilir bir container runtime’\u0131d\u0131r. CRI-O, container’lar\u0131n \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131, y\u00f6netimi ve ya\u015fam d\u00f6ng\u00fcs\u00fc s\u00fcre\u00e7lerini ele al\u0131r. CRI-O’nun GPU deste\u011fi, GPU kaynaklar\u0131n\u0131 container’lara atayarak ML i\u015f y\u00fcklerinin GPU h\u0131zland\u0131rmas\u0131n\u0131 sa\u011flar. Checkpoint\/restore ise, \u00e7al\u0131\u015fan bir container’\u0131n durumunu (hem CPU hem de GPU belle\u011fi dahil) bir disk dosyas\u0131na kaydedip daha sonra bu dosyadan yeniden ba\u015flatma i\u015flemidir. CRI-O GPU bu iki kavram\u0131 birle\u015ftirerek, \u00e7al\u0131\u015fan ML uygulamalar\u0131n\u0131n kesintisiz bir \u015fekilde farkl\u0131 makinelere veya kaynaklara ta\u015f\u0131nmas\u0131n\u0131 (canl\u0131 g\u00f6\u00e7) olanakl\u0131 k\u0131lar. Bu i\u015flem, GPU bellek i\u00e7eri\u011finin de kaydedilmesi ve geri y\u00fcklenmesi sayesinde, uygulaman\u0131n kesintisiz \u00e7al\u0131\u015fmaya devam etmesini sa\u011flar.<\/p>\n

Basit\u00e7e s\u00f6ylemek gerekirse, CRIU-gpu, bir trenin tam h\u0131zla giderken ray de\u011fi\u015ftirmesi gibi \u00e7al\u0131\u015f\u0131r. Tren (ML uygulamas\u0131), h\u0131z kesmeden raydan (bir sunucudan) ba\u015fka bir raya (ba\u015fka bir sunucuya) ge\u00e7er. Bu ge\u00e7i\u015f, yolcular\u0131n (veri ve i\u015flem) rahats\u0131z olmadan ger\u00e7ekle\u015ftirilir.<\/p>\n

CRI-O GPU ile Container Canl\u0131 G\u00f6\u00e7\u00fcn\u00fc Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n

CRI-O GPU ile canl\u0131 g\u00f6\u00e7 i\u015flemi, temelde CRIU (Checkpoint\/Restore In Userspace) arac\u0131n\u0131n geni\u015fletilmi\u015f bir versiyonunu kullan\u0131r. CRI-O GPU, GPU belle\u011fi dahil olmak \u00fczere container’\u0131n t\u00fcm durumunu yakalayarak, bu durumu disk \u00fczerinde bir checkpoint dosyas\u0131na yazar. Daha sonra, bu checkpoint dosyas\u0131 farkl\u0131 bir d\u00fc\u011f\u00fcme ta\u015f\u0131n\u0131r ve orada CRIU-gpu taraf\u0131ndan yeniden y\u00fcklenerek container’\u0131n \u00e7al\u0131\u015fmaya devam etmesi sa\u011flan\u0131r. Bu s\u00fcre\u00e7, uygulamada minimum d\u00fczeyde kesintiye neden olur, genellikle mili saniyeler mertebesindedir.<\/p>\n

Ad\u0131m Ad\u0131m Uygulama:<\/h3>\n
    \n
  1. Checkpoint Olu\u015fturma:<\/b> criu dump --image-dir \/path\/to\/checkpoint-dir <\/code> komutu ile container’\u0131n durumu kaydedilir.<\/li>\n
  2. Checkpoint Ta\u015f\u0131ma:<\/b> Checkpoint dosyalar\u0131 (\/path\/to\/checkpoint-dir<\/code>) hedef d\u00fc\u011f\u00fcme ta\u015f\u0131n\u0131r.<\/li>\n
  3. Checkpoint Y\u00fckleme:<\/b> Hedef d\u00fc\u011f\u00fcmde, criu restore --image-dir \/path\/to\/checkpoint-dir <\/code> komutu ile container yeniden ba\u015flat\u0131l\u0131r.<\/li>\n<\/ol>\n

    \u00d6nemli Not:<\/b> Bu ad\u0131mlar, basit bir g\u00f6sterimdir. Ger\u00e7ek d\u00fcnya uygulamalar\u0131nda daha karma\u015f\u0131k konfig\u00fcrasyonlar ve scriptler kullan\u0131labilir. Ayr\u0131ca, a\u011f yap\u0131land\u0131rmas\u0131, depolama ve di\u011fer kaynaklar\u0131n do\u011fru \u015fekilde y\u00f6netilmesi gerekir.<\/p>\n

    Ger\u00e7ek D\u00fcnya Senaryolar\u0131 ve Vaka Analizleri<\/h2>\n

    CRI-O GPU, \u00e7e\u015fitli ger\u00e7ek d\u00fcnya senaryolar\u0131nda kullan\u0131labilmektedir. \u00d6rne\u011fin, b\u00fcy\u00fck \u00f6l\u00e7ekli ML model e\u011fitimi s\u0131ras\u0131nda, bir d\u00fc\u011f\u00fcm\u00fcn ar\u0131zalanmas\u0131 veya bak\u0131m gereksinimi durumunda, CRI-O GPU ile \u00e7al\u0131\u015fan model, ba\u015fka bir d\u00fc\u011f\u00fcme an\u0131nda ta\u015f\u0131nabilir ve e\u011fitime kesintisiz devam edilebilir. Bu, e\u011fitim s\u00fcrecini h\u0131zland\u0131r\u0131r ve maliyetleri d\u00fc\u015f\u00fcr\u00fcr. Bir ba\u015fka \u00f6rnek ise, y\u00fcksek i\u015flem g\u00fcc\u00fc gerektiren ger\u00e7ek zamanl\u0131 tahmin uygulamalar\u0131d\u0131r. CRIU-gpu, bu uygulamalar\u0131n kesintisiz \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayarak, hizmet kalitesini korur.<\/p>\n

    Vaka Analizi 1: Otonom S\u00fcr\u00fc\u015f Sistemi<\/h3>\n

    Bir otonom s\u00fcr\u00fc\u015f sisteminde, g\u00f6r\u00fcnt\u00fc i\u015fleme ve karar verme i\u015flemleri ger\u00e7ek zamanl\u0131 olarak ger\u00e7ekle\u015ftirilir. E\u011fer sistemin \u00e7al\u0131\u015ft\u0131\u011f\u0131 sunucu ar\u0131zalan\u0131rsa, CRI-O GPU ile an\u0131nda yedek bir sunucuya ge\u00e7ilebilir, kazan\u0131lan zaman sayesinde olas\u0131 bir kazan\u0131n \u00f6n\u00fcne ge\u00e7ilebilir. Bu, sistemin y\u00fcksek g\u00fcvenilirlik ve g\u00fcvenlik gereksinimlerini kar\u015f\u0131lamas\u0131n\u0131 sa\u011flar.<\/p>\n

    Vaka Analizi 2: B\u00fcy\u00fck \u00d6l\u00e7ekli Doland\u0131r\u0131c\u0131l\u0131k Tespit Sistemi<\/h3>\n

    Finans sekt\u00f6r\u00fcnde kullan\u0131lan b\u00fcy\u00fck \u00f6l\u00e7ekli doland\u0131r\u0131c\u0131l\u0131k tespit sistemlerinde, ger\u00e7ek zamanl\u0131 veri ak\u0131\u015f\u0131 analiz edilir. Sistemin bir par\u00e7as\u0131n\u0131n ar\u0131zalanmas\u0131 durumunda, CRI-O GPU, sistemin di\u011fer k\u0131s\u0131mlar\u0131n\u0131n kesintisiz \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar, b\u00f6ylece doland\u0131r\u0131c\u0131l\u0131k olaylar\u0131n\u0131n tespiti ve \u00f6nlenmesi devam eder. Bu, i\u015fletmenin finansal kay\u0131plar\u0131n\u0131 en aza indirir.<\/p>\n

    CRI-O GPU ile Performans Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\n

    CRI-O GPU ile yap\u0131lan canl\u0131 g\u00f6\u00e7, geleneksel y\u00f6ntemlere g\u00f6re \u00e7ok daha h\u0131zl\u0131 ve verimlidir. Geleneksel y\u00f6ntemlerde, uygulaman\u0131n yeniden ba\u015flat\u0131lmas\u0131 uzun s\u00fcrebilir ve bu s\u00fcre\u00e7te veri kayb\u0131 ya\u015fanabilir. CRI-O GPU ile ise, uygulama neredeyse an\u0131nda yeniden ba\u015flat\u0131l\u0131r ve veri kayb\u0131 ya\u015fanmaz. A\u015fa\u011f\u0131daki tablo, farkl\u0131 y\u00f6ntemlerin performans kar\u015f\u0131la\u015ft\u0131rmas\u0131n\u0131 g\u00f6stermektedir:<\/p>\n\n\n\n\n
    Y\u00f6ntem<\/th>\nG\u00f6\u00e7 S\u00fcresi<\/th>\nVeri Kayb\u0131<\/th>\n<\/tr>\n
    Geleneksel Yeniden Ba\u015flatma<\/td>\nDakikalar<\/td>\nOlas\u0131<\/td>\n<\/tr>\n
    CRI-O GPU<\/td>\nMilisaniyeler<\/td>\nYok<\/td>\n<\/tr>\n<\/table>\n

    \u0130leri D\u00fczey Konular ve P\u00fcf Noktalar\u0131<\/h2>\n

    CRI-O GPU’nun etkin bir \u015fekilde kullan\u0131lmas\u0131 i\u00e7in, baz\u0131 ileri d\u00fczey konulara hakim olmak \u00f6nemlidir. \u00d6rne\u011fin, checkpoint\/restore i\u015fleminin performans\u0131, diskin h\u0131z\u0131, a\u011f bant geni\u015fli\u011fi ve GPU belle\u011finin b\u00fcy\u00fckl\u00fc\u011f\u00fc gibi fakt\u00f6rlerden etkilenir. Bu fakt\u00f6rleri optimize ederek, canl\u0131 g\u00f6\u00e7 i\u015fleminin h\u0131z\u0131n\u0131 art\u0131rmak m\u00fcmk\u00fcnd\u00fcr. Ayr\u0131ca, farkl\u0131 GPU mimarilerinin uyumlulu\u011fu ve CRI-O GPU’nun di\u011fer ara\u00e7larla entegrasyonu gibi konular da dikkate al\u0131nmal\u0131d\u0131r.<\/p>\n

    Uzman \u0130pucu:<\/b> B\u00fcy\u00fck miktarda GPU belle\u011fi kullanan uygulamalar i\u00e7in, checkpoint\/restore i\u015flemini b\u00f6l\u00fcmlere ay\u0131rmak performans\u0131 art\u0131rabilir.<\/p>\n

    Dikkat:<\/b> CRI-O GPU kullan\u0131rken, g\u00fcvenlik a\u00e7\u0131klar\u0131na kar\u015f\u0131 dikkatli olmak \u00f6nemlidir. Checkpoint dosyalar\u0131n\u0131n g\u00fcvenli bir \u015fekilde saklanmas\u0131 ve yetkisiz eri\u015fime kar\u015f\u0131 korunmas\u0131 gerekir.<\/p>\n

    Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular<\/h2>\n

    CRI-O GPU, ML i\u015f y\u00fckleri i\u00e7in s\u0131f\u0131r d\u00fc\u015f\u00fc\u015f s\u00fcresine sahip canl\u0131 g\u00f6\u00e7\u00fc m\u00fcmk\u00fcn k\u0131lan g\u00fc\u00e7l\u00fc bir teknolojidir. Bu teknoloji, b\u00fcy\u00fck \u00f6l\u00e7ekli ML uygulamalar\u0131n\u0131n g\u00fcvenilirli\u011fi, \u00f6l\u00e7eklenebilirli\u011fi ve performans\u0131n\u0131 art\u0131r\u0131r. Daha fazla bilgi i\u00e7in fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz.<\/p>\n

    S\u0131k\u00e7a Sorulan Sorular:<\/h3>\n
      \n
    1. CRI-O GPU hangi GPU mimarilerini destekler?<\/b> CRI-O GPU, Nvidia ve AMD GPU’lar\u0131 da dahil olmak \u00fczere \u00e7e\u015fitli GPU mimarilerini destekler. Ancak, uyumluluk, kullan\u0131lan s\u00fcr\u00fcc\u00fclere ve di\u011fer yaz\u0131l\u0131mlara ba\u011fl\u0131 olabilir.<\/li>\n
    2. CRI-O GPU’nun maliyeti nedir?<\/b> CRI-O a\u00e7\u0131k kaynakl\u0131 bir projedir ve \u00fccretsiz olarak kullan\u0131labilir.<\/li>\n
    3. CRI-O GPU’nun kurulumu karma\u015f\u0131kt\u0131r m\u0131?<\/b> Kurulum, di\u011fer container teknolojilerine k\u0131yasla nispeten basittir, ancak baz\u0131 teknik bilgiler gerekebilir.<\/li>\n
    4. CRI-O GPU, t\u00fcm ML \u00e7er\u00e7eveleriyle uyumlu mudur?<\/b> \u00c7o\u011fu pop\u00fcler ML \u00e7er\u00e7evesi (TensorFlow, PyTorch, vb.) ile uyumludur, ancak baz\u0131 konfig\u00fcrasyonlar gerekebilir.<\/li>\n
    5. CRI-O GPU’nun g\u00fcvenli\u011fi nas\u0131l sa\u011flan\u0131r?<\/b> Checkpoint dosyalar\u0131n\u0131n \u015fifrelenmesi ve yetkisiz eri\u015fimin engellenmesi i\u00e7in g\u00fcvenlik \u00f6nlemleri al\u0131nmal\u0131d\u0131r.<\/li>\n<\/ol>\n

      Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"GPU Container Checkpoint\/Restore ile S\u0131f\u0131r D\u00fc\u015f\u00fc\u015f S\u00fcreli ML \u0130\u015f Y\u00fck\u00fc G\u00f6\u00e7\u00fc Makine \u00f6\u011frenmesi (ML) i\u015f y\u00fcklerinin h\u0131zla artan…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-28348","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nGPU Container Checkpoint\/Restore ile S\u0131f\u0131r D\u00fc\u015f\u00fc\u015f S\u00fcreli ML \u0130\u015f Y\u00fck\u00fc G\u00f6\u00e7\u00fc<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesi (ML) i\u015f y\u00fcklerinin h\u0131zla artan karma\u015f\u0131kl\u0131\u011f\u0131 ve \u00f6l\u00e7eklenebilirlik gereksinimleri, s\u00fcreklili\u011fi sa\u011flayan ve kesintisiz i\u015flemleri garanti eden \u00e7\u00f6z\u00fcmler gerektirir. CRI-O GPU ile container checkpoint\/restore (kontrol noktas\u0131\/geri y\u00fckleme) teknolojisi, bu ihtiyac\u0131 kar\u015f\u0131layarak s\u0131f\u0131r d\u00fc\u015f\u00fc\u015f s\u00fcresine sahip canl\u0131 g\u00f6\u00e7\u00fc m\u00fcmk\u00fcn k\u0131lar. Bu makalede, CRIU-gpu'nun temel prensiplerini, uygulamas\u0131n\u0131 ve ileri d\u00fczey kullan\u0131m senaryolar\u0131n\u0131, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle birlikte ele alaca\u011f\u0131z.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/gpu-container-checkpoint-restore-ile-sifir-dusus-sureli-ml-is-yuku-gocu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GPU Container Checkpoint\/Restore ile S\u0131f\u0131r D\u00fc\u015f\u00fc\u015f S\u00fcreli ML \u0130\u015f Y\u00fck\u00fc G\u00f6\u00e7\u00fc\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenmesi (ML) i\u015f y\u00fcklerinin h\u0131zla artan karma\u015f\u0131kl\u0131\u011f\u0131 ve \u00f6l\u00e7eklenebilirlik gereksinimleri, s\u00fcreklili\u011fi sa\u011flayan ve kesintisiz i\u015flemleri garanti eden \u00e7\u00f6z\u00fcmler gerektirir. CRI-O GPU ile container checkpoint\/restore (kontrol noktas\u0131\/geri y\u00fckleme) teknolojisi, bu ihtiyac\u0131 kar\u015f\u0131layarak s\u0131f\u0131r d\u00fc\u015f\u00fc\u015f s\u00fcresine sahip canl\u0131 g\u00f6\u00e7\u00fc m\u00fcmk\u00fcn k\u0131lar. 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