{"id":478,"date":"2025-12-08T08:00:38","date_gmt":"2025-12-08T05:00:38","guid":{"rendered":"https:\/\/neku.ai\/batch-size-ayarlama-ipuclari\/"},"modified":"2025-12-08T08:00:59","modified_gmt":"2025-12-08T05:00:59","slug":"batch-size-ayarlama-ipuclari","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/batch-size-ayarlama-ipuclari\/","title":{"rendered":"Batch size ayar\u0131n\u0131n model performans\u0131na etkisi"},"content":{"rendered":"

Batch size nedir<\/strong><\/h3>\n
\n

Giri\u015f<\/strong><\/h3>\n

Batch size, yapay zeka modellerinin e\u011fitiminde kullan\u0131lan temel kavramlardan biridir. Bir modelin her \u00f6\u011frenme ad\u0131m\u0131nda ka\u00e7 veri \u00f6rne\u011fini ayn\u0131 anda i\u015fleyece\u011fini ifade eder. Do\u011fru tan\u0131mlanmad\u0131\u011f\u0131nda hem model performans\u0131n\u0131 hem de hesaplama kaynaklar\u0131n\u0131 do\u011frudan etkiler. Temel AI kavramlar\u0131n\u0131 anlamak isteyenler i\u00e7in batch size, \u00f6\u011frenme s\u00fcrecinin verimlili\u011fini belirleyen kilit bir parametredir.<\/p>\n

\n

Batch size nedir tan\u0131m\u0131<\/strong><\/h3>\n

Batch size, bir yapay zeka modelinin e\u011fitiminde tek bir ileri ve geri yay\u0131l\u0131m (forward-backward pass) s\u0131ras\u0131nda kullan\u0131lan \u00f6rnek veri miktar\u0131n\u0131 tan\u0131mlar. \u00d6rne\u011fin batch size = 32 ise, model her \u00f6\u011frenme ad\u0131m\u0131nda 32 \u00f6rne\u011fi ayn\u0131 anda i\u015fler. Bu say\u0131 modelin haf\u0131za, e\u011fitim h\u0131z\u0131 ve sonu\u00e7 kalitesi aras\u0131ndaki dengeyi do\u011frudan belirler.<\/p>\n

\n

batch size nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

Batch size, modelin e\u011fitimi s\u0131ras\u0131nda verilerin toplu halde i\u015flenmesini sa\u011flar. E\u011fitim verisi, mini-batch denilen par\u00e7alara ayr\u0131l\u0131r ve bu par\u00e7alar modelin parametrelerini g\u00fcncellemek i\u00e7in s\u0131rayla kullan\u0131l\u0131r. B\u00f6ylece model her yinelemede hem genel e\u011filimleri hem de belirli \u00f6rneklerden gelen hatalar\u0131 \u00f6\u011frenir.<\/p>\n

\n

Temel parametreler ve ayarlar<\/strong><\/h3>\n

Batch size genellikle modelin b\u00fcy\u00fckl\u00fc\u011f\u00fc, GPU\/CPU belle\u011fi ve veri setinin boyutu dikkate al\u0131narak se\u00e7ilir. <\/p>\n

    \n
  • K\u00fc\u00e7\u00fck batch size (\u00f6rne\u011fin 8\u201332) daha do\u011fru genelleme sa\u011flar ancak e\u011fitim yava\u015f olabilir. <\/li>\n
  • B\u00fcy\u00fck batch size (\u00f6rne\u011fin 256\u20131024) hesaplama verimlili\u011fini art\u0131r\u0131r fakat genelleme yetene\u011fini d\u00fc\u015f\u00fcrebilir.
    \nBu nedenle \u00e7o\u011fu LLM veya derin \u00f6\u011frenme projesinde, optimum batch size denge testleriyle belirlenir.<\/li>\n<\/ul>\n
    \n

    S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h3>\n

    En s\u0131k yap\u0131lan hata, donan\u0131m kapasitesine g\u00f6re fazla b\u00fcy\u00fck batch size se\u00e7mektir; bu durum bellek ta\u015fmalar\u0131na ve e\u011fitim hatalar\u0131na yol a\u00e7ar. Ayr\u0131ca a\u015f\u0131r\u0131 k\u00fc\u00e7\u00fck de\u011ferler modelin karars\u0131z \u00f6\u011frenmesine neden olabilir. En iyi yakla\u015f\u0131m, sistem kaynaklar\u0131n\u0131 ve model karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 g\u00f6z \u00f6n\u00fcne alarak art\u0131ml\u0131 testlerle uygun de\u011feri bulmakt\u0131r.<\/p>\n

    \n

    Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h3>\n

    Bir LLM modelini e\u011fitirken batch size = 64 se\u00e7mek, her ad\u0131mda 64 \u00f6rne\u011fi ayn\u0131 anda CUDA tabanl\u0131 GPU \u00fczerinde i\u015fler. SAP entegrasyonlar\u0131nda ise batch i\u015flemleri, sistemden gelen b\u00fcy\u00fck veri k\u00fcmelerini paralel olarak i\u015fleyerek operasyonel y\u00fck\u00fc azalt\u0131r. n8n ya da benzeri orkestrasyon ara\u00e7lar\u0131nda batch kavram\u0131, otomasyon s\u00fcre\u00e7lerinde veri gruplar\u0131n\u0131n toplu i\u015flenmesi anlam\u0131na gelir.<\/p>\n

    \n

    Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

    Batch size, modelin parametre g\u00fcncellemelerini ortalama hata geri bildirimine g\u00f6re \u015fekillendirir. K\u00fc\u00e7\u00fck batch size, her ad\u0131mda daha rastgele ama esnek bir \u00f6\u011frenme sa\u011flar. B\u00fcy\u00fck batch size ise hatay\u0131 daha kararl\u0131 ancak dura\u011fan hale getirir. Ba\u015flang\u0131\u00e7 seviyesinde, batch size\u2019\u0131 dosyalarla dolu k\u00fc\u00e7\u00fck kutular olarak d\u00fc\u015f\u00fcnmek m\u00fcmk\u00fcnd\u00fcr: Her kutu modelin bir defada “\u00f6\u011frendi\u011fi” veri grubunu temsil eder. Bu kutular\u0131n b\u00fcy\u00fckl\u00fc\u011f\u00fc \u00f6\u011frenme h\u0131z\u0131 ve do\u011frulu\u011fu aras\u0131ndaki dengeyi belirler.<\/p>\n

    \n

    \u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n

    Batch size kavram\u0131n\u0131n i\u015fletme ortamlar\u0131nda \u00f6nemi birka\u00e7 temel ba\u015fl\u0131kta \u00f6zetlenebilir: <\/p>\n

      \n
    • Performans:<\/strong> Daha b\u00fcy\u00fck batch\u2019ler h\u0131zl\u0131 e\u011fitim sa\u011flar. <\/li>\n
    • G\u00fcvenilirlik:<\/strong> Stabil \u00f6\u011frenme, model davran\u0131\u015f\u0131n\u0131n \u00f6ng\u00f6r\u00fclebilirli\u011fini art\u0131r\u0131r. <\/li>\n
    • Maliyet:<\/strong> Kaynak t\u00fcketimini optimize eder. <\/li>\n
    • \u00d6l\u00e7ekleme:<\/strong> B\u00fcy\u00fck veri k\u00fcmelerinin verimli i\u015flenmesini m\u00fcmk\u00fcn k\u0131lar. <\/li>\n
    • Otomasyon:<\/strong> S\u00fcrekli entegrasyon ve da\u011f\u0131t\u0131m (CI\/CD) s\u00fcre\u00e7lerinde model e\u011fitimini h\u0131zland\u0131r\u0131r. <\/li>\n
    • Karar alma:<\/strong> Modeller daha tutarl\u0131 sonu\u00e7lar \u00fcretir. <\/li>\n
    • Operasyonel verimlilik:<\/strong> E\u011fitim s\u00fcresinin k\u0131salmas\u0131yla zaman kazanc\u0131 sa\u011flar.<\/li>\n<\/ul>\n
      \n

      Bu kavram NeKu.AI i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

      NeKu.AI s\u00fcre\u00e7lerinde batch size kavram\u0131, hem yapay zeka modellerinin e\u011fitimi hem de otomasyon senaryolar\u0131n\u0131n optimize edilmesi i\u00e7in kullan\u0131l\u0131r. \u00d6rne\u011fin, bir LLM modeli SAP verileri \u00fczerinde e\u011fitilirken batch size parametresi, sistem belle\u011fi s\u0131n\u0131rlar\u0131 ve veri hacmine g\u00f6re dinamik olarak ayarlan\u0131r. Bu yakla\u015f\u0131m, hem \u00f6\u011frenme s\u00fcresini k\u0131salt\u0131r hem de kurumsal veri entegrasyonlar\u0131n\u0131n kararl\u0131l\u0131\u011f\u0131n\u0131 art\u0131r\u0131r. Temel kavram serisinin bir par\u00e7as\u0131 olarak bu t\u00fcr ayarlar\u0131n a\u00e7\u0131klanmas\u0131, geli\u015ftiricilerin sistem davran\u0131\u015f\u0131n\u0131 \u00f6ng\u00f6rmesini kolayla\u015ft\u0131r\u0131r.<\/p>\n

      \n

      AI geli\u015ftiricileri, \u00fcr\u00fcn y\u00f6neticileri, SAP dan\u0131\u015fmanlar\u0131 i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
        \n
      1. Sorun:<\/strong> Bir ekip, LLM tabanl\u0131 dok\u00fcman s\u0131n\u0131fland\u0131rma modelini SAP verisiyle e\u011fitirken CPU\/memory hatalar\u0131yla kar\u015f\u0131la\u015f\u0131yor. <\/li>\n
      2. Ba\u011flam:<\/strong> E\u011fitim s\u00fcreci uzun, kay\u0131p (loss) de\u011feri karars\u0131z ilerliyor. <\/li>\n
      3. Kavram\u0131n uygulanmas\u0131:<\/strong> Batch size 128\u2019den 32\u2019ye d\u00fc\u015f\u00fcr\u00fclerek her ad\u0131mda daha az veri i\u015flenir, GPU y\u00fck\u00fc dengelenir. <\/li>\n
      4. Sonu\u00e7:<\/strong> E\u011fitim stabil hale gelir, do\u011fruluk oran\u0131 y\u00fckselir. <\/li>\n
      5. \u0130\u015f etkisi:<\/strong> Model \u00fcretim sistemine daha h\u0131zl\u0131 entegre edilir, tahmin s\u00fcre\u00e7leri g\u00fcvenilirle\u015fir.<\/li>\n<\/ol>\n
        \n

        S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n

        Hatalar:<\/strong> <\/p>\n

          \n
        • Batch size parametresini donan\u0131m kapasitesine g\u00f6re ayarlamamak <\/li>\n
        • E\u011fitim ve do\u011frulama s\u00fcre\u00e7lerinde farkl\u0131 batch de\u011ferleri kullanmak <\/li>\n
        • Dinamik veri ak\u0131\u015flar\u0131nda sabit batch boyutuna ba\u011fl\u0131 kalmak <\/li>\n<\/ul>\n

          En iyi uygulamalar:<\/strong> <\/p>\n

            \n
          • Deneysel olarak optimum batch size\u2019\u0131 belirlemek <\/li>\n
          • GPU bellek izleme ara\u00e7lar\u0131n\u0131 kullanmak <\/li>\n
          • Otomasyon orkestrasyonlar\u0131nda (\u00f6rne\u011fin n8n) paralel batch i\u015fleme yap\u0131lar\u0131n\u0131 tasarlamak <\/li>\n
          • SAP veya di\u011fer kurumsal veri kaynaklar\u0131nda toplu i\u015f y\u00fcklerini \u00f6l\u00e7eklenebilir hale getirmek <\/li>\n<\/ul>\n
            \n

            Sonu\u00e7<\/strong><\/h3>\n

            Batch size, yapay zeka ve LLM e\u011fitimlerinde modelin \u00f6\u011frenme s\u00fcresini, do\u011frulu\u011funu ve kaynak kullan\u0131m\u0131n\u0131 belirleyen temel bir kavramd\u0131r. Do\u011fru ayarland\u0131\u011f\u0131nda hem teknik hem de operasyonel anlamda verimlilik sa\u011flar. NeKu.AI gibi ak\u0131ll\u0131 orkestrasyon \u00e7\u00f6z\u00fcmleri geli\u015ftiren ekipler i\u00e7in batch size, sistem performans\u0131n\u0131 optimize etmenin en do\u011frudan yollar\u0131ndan biridir.<\/p>","protected":false},"excerpt":{"rendered":"

            Batch size nedir Giri\u015f Batch size, yapay zeka modellerinin e\u011fitiminde kullan\u0131lan temel kavramlardan biridir. Bir modelin her \u00f6\u011frenme ad\u0131m\u0131nda ka\u00e7 veri \u00f6rne\u011fini ayn\u0131 anda i\u015fleyece\u011fini ifade [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":479,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-478","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nBatch size ayar\u0131n\u0131n model performans\u0131na etkisi - NeKu.AI<\/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:\/\/neku.ai\/en\/batch-size-ayarlama-ipuclari\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Batch size ayar\u0131n\u0131n model performans\u0131na etkisi - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"Batch size nedir Giri\u015f Batch size, yapay zeka modellerinin e\u011fitiminde kullan\u0131lan temel kavramlardan biridir. Bir modelin her \u00f6\u011frenme ad\u0131m\u0131nda ka\u00e7 veri \u00f6rne\u011fini ayn\u0131 anda i\u015fleyece\u011fini ifade [\u2026]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/neku.ai\/en\/batch-size-ayarlama-ipuclari\/\" \/>\n<meta property=\"og:site_name\" content=\"NeKu.AI\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-08T05:00:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-08T05:00:59+00:00\" \/>\n<meta name=\"author\" content=\"Serkan \u00d6zcan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Serkan \u00d6zcan\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/neku.ai\\\/batch-size-ayarlama-ipuclari\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/batch-size-ayarlama-ipuclari\\\/\"},\"author\":{\"name\":\"Serkan \u00d6zcan\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/person\\\/cf640cfda3e16635fb740662d943e96b\"},\"headline\":\"Batch size ayar\u0131n\u0131n model performans\u0131na etkisi\",\"datePublished\":\"2025-12-08T05:00:38+00:00\",\"dateModified\":\"2025-12-08T05:00:59+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/batch-size-ayarlama-ipuclari\\\/\"},\"wordCount\":1018,\"publisher\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/batch-size-ayarlama-ipuclari\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-478.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/neku.ai\\\/batch-size-ayarlama-ipuclari\\\/\",\"url\":\"https:\\\/\\\/neku.ai\\\/batch-size-ayarlama-ipuclari\\\/\",\"name\":\"Batch size ayar\u0131n\u0131n model performans\u0131na etkisi - 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