{"id":29970,"date":"2025-09-22T09:40:26","date_gmt":"2025-09-22T06:40:26","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/"},"modified":"2025-09-22T09:40:26","modified_gmt":"2025-09-22T06:40:26","slug":"pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/","title":{"rendered":"PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131"},"content":{"rendered":"

# PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131<\/p>\n

Derin \u00f6\u011frenme projelerinizde, \u00f6zellikle b\u00fcy\u00fck veri setleri ve karma\u015f\u0131k modellerle \u00e7al\u0131\u015f\u0131rken, bellek y\u00f6netimi ve hesaplama g\u00fcc\u00fc en b\u00fcy\u00fck sorunlar\u0131n\u0131zdan biri haline gelebilir. PyTorch’un sundu\u011fu g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131 kullanarak bu zorluklar\u0131n \u00fcstesinden nas\u0131l gelebilece\u011finizi bu makalede ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. PyTorch’ta bellek y\u00f6netimi ve \u00e7oklu GPU kullan\u0131m\u0131yla ilgili ipu\u00e7lar\u0131, pratik \u00f6rnekler ve performans optimizasyonu stratejileri bu rehberde ele al\u0131nmaktad\u0131r. Bu makale, yeni ba\u015flayanlardan ileri seviye kullan\u0131c\u0131lara kadar herkes i\u00e7in faydal\u0131 bilgiler sunmaktad\u0131r.<\/p>\n

PyTorch Bellek Y\u00f6netimi: Temel Kavramlar Nelerdir?<\/strong><\/p>\n

PyTorch, Python tabanl\u0131 bir derin \u00f6\u011frenme k\u00fct\u00fcphanesi olup, dinamik hesaplama grafi\u011fi \u00f6zelli\u011fi sayesinde bellek y\u00f6netimi di\u011fer k\u00fct\u00fcphanelere g\u00f6re daha esnektir. Ancak, bu esneklik kontrols\u00fcz kullan\u0131ld\u0131\u011f\u0131nda bellek t\u00fcketiminde b\u00fcy\u00fck art\u0131\u015flara ve performans d\u00fc\u015f\u00fc\u015flerine yol a\u00e7abilir. PyTorch’ta bellek y\u00f6netimini anlamak, torch.tensor<\/code> objelerinin ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc ve PyTorch’un otomatik bellek y\u00f6netim mekanizmas\u0131n\u0131 anlamakla ba\u015flar.<\/p>\n

Bir torch.tensor<\/code> olu\u015fturuldu\u011funda, bu tensor i\u00e7in gerekli bellek otomatik olarak ayr\u0131l\u0131r. Tensor art\u0131k kullan\u0131lmad\u0131\u011f\u0131nda, PyTorch’un \u00e7\u00f6p toplay\u0131c\u0131s\u0131 (garbage collector) taraf\u0131ndan otomatik olarak serbest b\u0131rak\u0131l\u0131r. Ancak, b\u00fcy\u00fck tensor’lar olu\u015fturup bunlar\u0131 gereksiz yere bellekte tutmak, \u00f6zellikle s\u0131n\u0131rl\u0131 bellek kaynaklar\u0131na sahip sistemlerde, ciddi sorunlara yol a\u00e7abilir. Bu nedenle, tensor’lar\u0131n ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc dikkatlice y\u00f6netmek ve gereksiz tensor’lar\u0131 manuel olarak silmek \u00f6nemlidir. del<\/code> anahtar kelimesi ile bir tensor’\u0131 silerek belle\u011fi serbest b\u0131rakabilirsiniz. \u00d6rne\u011fin:<\/p>\n

import torch\n\nx = torch.randn(1000, 1000)  # B\u00fcy\u00fck bir tensor olu\u015fturuldu\n# ... x tensoru ile i\u015flemler ...\ndel x  # x tensoru silindi ve belle\u011fi serbest b\u0131rak\u0131ld\u0131\ntorch.cuda.empty_cache() # GPU belle\u011fini temizler<\/code><\/pre>\n
torch.cuda.empty_cache()<\/code> fonksiyonu, GPU belle\u011finde kullan\u0131lmayan alanlar\u0131 serbest b\u0131rakmak i\u00e7in kullan\u0131labilir. Ancak, bu fonksiyonun her zaman gerekli olmad\u0131\u011f\u0131n\u0131 ve a\u015f\u0131r\u0131 kullan\u0131m\u0131n\u0131n performans\u0131 olumsuz etkileyebilece\u011fini unutmamak \u00f6nemlidir.  Bellek s\u0131z\u0131nt\u0131lar\u0131n\u0131 \u00f6nlemek i\u00e7in, \u00f6zellikle d\u00f6ng\u00fcler i\u00e7inde b\u00fcy\u00fck tensor’lar olu\u015ftururken dikkatli olmak ve gereksiz tensor’lar\u0131 zaman\u0131nda silmek \u00f6nemlidir.  Bu, \u00f6zellikle uzun s\u00fcre \u00e7al\u0131\u015fan uygulamalar i\u00e7in kritik \u00f6neme sahiptir.  Daha geli\u015fmi\u015f teknikler i\u00e7in, [Fatih Soysal’\u0131n derin \u00f6\u011frenme bloguna](https:\/\/fatihsoysal.com) g\u00f6z atabilirsiniz.<\/p>\n
PyTorch’ta \u00c7oklu GPU Kullan\u0131m\u0131: Nas\u0131l Yap\u0131l\u0131r?<\/strong><\/p>\n
\u00c7oklu GPU kullan\u0131m\u0131, derin \u00f6\u011frenme modellerinin e\u011fitim s\u00fcresini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r. PyTorch, torch.nn.DataParallel<\/code> ve torch.nn.parallel.DistributedDataParallel<\/code> gibi ara\u00e7lar sunarak \u00e7oklu GPU kullan\u0131m\u0131n\u0131 kolayla\u015ft\u0131r\u0131r.<\/p>\n
torch.nn.DataParallel<\/code>, modeli farkl\u0131 GPU’lar aras\u0131nda e\u015fit olarak payla\u015ft\u0131r\u0131r ve veri paralel e\u011fitim sa\u011flar.  Bu y\u00f6ntem, basit ve kullan\u0131m\u0131 kolayd\u0131r, ancak t\u00fcm GPU’lar\u0131n ayn\u0131 kapasitede olmas\u0131 ve ileti\u015fim gecikmelerinin performans\u0131 etkileyebilece\u011fi durumlar i\u00e7in uygun olmayabilir.<\/p>\n
import torch\nimport torch.nn as nn\nfrom torch.nn.parallel import DataParallel\n\nmodel = nn.Linear(10, 10) # basit bir model\nif torch.cuda.device_count() > 1:\n  print("Let's use", torch.cuda.device_count(), "GPUs!")\n  model = nn.DataParallel(model)\n\nmodel.to('cuda') # modeli GPU'ya ta\u015f\u0131ma<\/code><\/pre>\n
torch.nn.parallel.DistributedDataParallel<\/code> ise daha geli\u015fmi\u015f bir yakla\u015f\u0131m sunar.  Bu y\u00f6ntem, farkl\u0131 GPU’lar aras\u0131nda daha ince ayarl\u0131 bir veri payla\u015f\u0131m\u0131 sa\u011flar ve daha b\u00fcy\u00fck \u00f6l\u00e7ekli modeller i\u00e7in daha uygundur. Ancak, torch.distributed<\/code> paketinin kullan\u0131lmas\u0131n\u0131 gerektirir ve daha karma\u015f\u0131k bir kurulum gerektirir.  Bu y\u00f6ntem, daha iyi \u00f6l\u00e7eklenebilirlik ve performans sa\u011flar, ancak daha karma\u015f\u0131k bir kurulum gerektirir.<\/p>\n
PyTorch’ta Bellek Y\u00f6netimi: Ger\u00e7ek D\u00fcnya Senaryolar\u0131 ve Optimizasyon \u0130pu\u00e7lar\u0131<\/strong><\/p>\n
Bir g\u00f6r\u00fcnt\u00fc i\u015fleme projesinde, milyonlarca g\u00f6r\u00fcnt\u00fcn\u00fcn \u00f6zelliklerini \u00e7\u0131karmak i\u00e7in bir CNN modeli kullan\u0131ld\u0131\u011f\u0131n\u0131 d\u00fc\u015f\u00fcnelim.  Bu i\u015flem, b\u00fcy\u00fck miktarda bellek t\u00fcketimine yol a\u00e7abilir.  Bellek t\u00fcketimini azaltmak i\u00e7in a\u015fa\u011f\u0131daki stratejileri kullanabiliriz:<\/p>\n
* K\u00fc\u00e7\u00fck batch boyutlar\u0131 kullan\u0131n:<\/strong> Daha k\u00fc\u00e7\u00fck batch boyutlar\u0131, bellekte ayn\u0131 anda tutulmas\u0131 gereken veri miktar\u0131n\u0131 azalt\u0131r.
\n* Gradyan birikimi kullan\u0131n:<\/strong>  Her ad\u0131mda gradyanlar\u0131 biriktirip daha sonra g\u00fcncelleme yapmak, bellek t\u00fcketimini azalt\u0131r.
\n* Tensor’lar\u0131 gereksiz yere tutmay\u0131n:<\/strong>  \u0130\u015flem tamamland\u0131ktan sonra tensor’lar\u0131 del<\/code> komutu ile silin.
\n* torch.no_grad()<\/code> kullan\u0131n:<\/strong>  E\u011fitim a\u015famas\u0131 d\u0131\u015f\u0131nda, hesaplama grafi\u011fi olu\u015fturulmas\u0131n\u0131 \u00f6nlemek i\u00e7in torch.no_grad()<\/code> ba\u011flam y\u00f6neticisini kullan\u0131n.<\/p>\n
\u00d6rnek bir kod blo\u011fu:<\/p>\n
import torch\n\nwith torch.no_grad():\n    # ... hesaplama grafi\u011fi olu\u015fturulmadan i\u015flemler ...<\/code><\/pre>\n
Bu optimizasyon teknikleri, b\u00fcy\u00fck veri setleri ile \u00e7al\u0131\u015f\u0131rken bellek t\u00fcketimini \u00f6nemli \u00f6l\u00e7\u00fcde azaltabilir.<\/p>\n
PyTorch’ta \u00c7oklu GPU Kullan\u0131m\u0131: Performans Analizi ve Edge Case’ler<\/strong><\/p>\n
\u00c7oklu GPU kullan\u0131m\u0131, her zaman performans art\u0131\u015f\u0131 sa\u011flamaz.  GPU’lar aras\u0131 ileti\u015fim gecikmeleri, veri payla\u015f\u0131m\u0131 maliyetleri ve model mimarisi, performans\u0131 etkileyebilir.  Performans analizini yapmak i\u00e7in, e\u011fitim s\u00fcresini, bellek kullan\u0131m\u0131n\u0131 ve GPU kullan\u0131m oran\u0131n\u0131 izlemek \u00f6nemlidir.  Profiling ara\u00e7lar\u0131, bu parametreleri izlemek ve performans darbo\u011fazlar\u0131n\u0131 tespit etmek i\u00e7in kullan\u0131labilir.<\/p>\n
Baz\u0131 edge case’ler:<\/p>\n
* GPU belle\u011fi yetersizli\u011fi:<\/strong> \u00c7ok b\u00fcy\u00fck modeller veya veri setleri, GPU belle\u011fi yetersizli\u011fine yol a\u00e7abilir.  Bu durumda, daha k\u00fc\u00e7\u00fck batch boyutlar\u0131 kullanmak veya modeli daha k\u00fc\u00e7\u00fck par\u00e7alara b\u00f6lmek gerekebilir.
\n* GPU uyumlulu\u011fu:<\/strong>  Farkl\u0131 GPU modelleri, farkl\u0131 performans \u00f6zelliklerine sahip olabilir.  \u00c7oklu GPU kullan\u0131rken, GPU’lar\u0131n uyumlu oldu\u011fundan emin olmak \u00f6nemlidir.
\n* \u0130leti\u015fim gecikmeleri:<\/strong>  GPU’lar aras\u0131 veri transferi, ileti\u015fim gecikmelerine yol a\u00e7abilir.  Bu gecikmeleri azaltmak i\u00e7in, verimli veri payla\u015f\u0131m stratejileri kullanmak \u00f6nemlidir.<\/p>\n
PyTorch’ta Bellek Y\u00f6netimi: \u0130leri D\u00fczey Teknikler<\/strong><\/p>\n
Daha ileri d\u00fczey bellek y\u00f6netimi i\u00e7in, torch.utils.checkpoint<\/code> mod\u00fcl\u00fcn\u00fc kullanarak hesaplama grafi\u011fini par\u00e7alara b\u00f6lmek ve belle\u011fi daha verimli kullanmak m\u00fcmk\u00fcnd\u00fcr. Bu teknik, \u00f6zellikle b\u00fcy\u00fck ve derin modellerde bellek t\u00fcketimini azaltmak i\u00e7in olduk\u00e7a etkilidir.  Ayr\u0131ca,  pin_memory=True<\/code> arg\u00fcman\u0131n\u0131 kullanarak CPU ve GPU aras\u0131nda veri transferini optimize edebilirsiniz.<\/p>\n
import torch\nfrom torch.utils.data import DataLoader\n\n# ... veri y\u00fckleyici olu\u015fturma ...\ntrain_loader = DataLoader(dataset, batch_size=batch_size, pin_memory=True)<\/code><\/pre>\n
Bu, veri transferini h\u0131zland\u0131rarak toplam e\u011fitim s\u00fcresini k\u0131salt\u0131r.<\/p>\n
PyTorch’ta \u00c7oklu GPU Kullan\u0131m\u0131: Da\u011f\u0131t\u0131k E\u011fitim<\/strong><\/p>\n
Ger\u00e7ek d\u00fcnyada, b\u00fcy\u00fck veri setleri ve karma\u015f\u0131k modellerle \u00e7al\u0131\u015f\u0131rken, tek bir makinede bulunan \u00e7oklu GPU’lar\u0131n kapasitesi yetersiz kalabilir.  Bu durumlarda, da\u011f\u0131t\u0131k e\u011fitim teknikleri kullanmak gerekir.  PyTorch’un torch.distributed<\/code> paketi, birden fazla makinede bulunan \u00e7oklu GPU’lar aras\u0131nda model e\u011fitimini da\u011f\u0131tmak i\u00e7in ara\u00e7lar sa\u011flar.  Bu, \u00e7ok b\u00fcy\u00fck \u00f6l\u00e7ekli modellerin e\u011fitilmesini m\u00fcmk\u00fcn k\u0131lar.<\/p>\n
PyTorch ile Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131: Ad\u0131m Ad\u0131m \u00d6rnek<\/strong><\/p>\n
Bir basit do\u011frusal regresyon modelini ele alal\u0131m.  Bu model, ilk olarak tek bir GPU’da e\u011fitildikten sonra, \u00e7oklu GPU kullan\u0131m\u0131 ile e\u011fitilerek performans kar\u015f\u0131la\u015ft\u0131rmas\u0131 yap\u0131lacakt\u0131r.<\/p>\n
Yeni Ba\u015flayan:<\/strong><\/p>\n
import torch\nimport torch.nn as nn\n\n# Model\nmodel = nn.Linear(1, 1)\nmodel.to('cuda') # Modeli GPU'ya ta\u015f\u0131ma\n\n# ... e\u011fitim d\u00f6ng\u00fcs\u00fc ...<\/code><\/pre>\n
Orta Seviye:<\/strong><\/p>\n
import torch\nimport torch.nn as nn\nfrom torch.nn.parallel import DataParallel\n\n# Model\nmodel = nn.Linear(1, 1)\nif torch.cuda.device_count() > 1:\n    model = nn.DataParallel(model)\nmodel.to('cuda')\n\n# ... e\u011fitim d\u00f6ng\u00fcs\u00fc ...<\/code><\/pre>\n
\u0130leri Seviye:<\/strong><\/p>\n
import torch\nimport torch.nn as nn\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\n\n# ... da\u011f\u0131t\u0131k e\u011fitim kodu ...<\/code><\/pre>\n
Sonu\u00e7<\/strong><\/p>\n
PyTorch’ta bellek y\u00f6netimi ve \u00e7oklu GPU kullan\u0131m\u0131, derin \u00f6\u011frenme projelerinin ba\u015far\u0131s\u0131 i\u00e7in kritik \u00f6neme sahiptir.  Bu makalede ele al\u0131nan teknikler ve ipu\u00e7lar\u0131, bellek t\u00fcketimini azaltmak ve e\u011fitim s\u00fcresini k\u0131saltmak i\u00e7in kullan\u0131labilir.  Ancak, her projenin kendine \u00f6zg\u00fc gereksinimleri vard\u0131r ve en iyi yakla\u015f\u0131m, projenin \u00f6zel gereksinimlerine ba\u011fl\u0131 olarak belirlenmelidir.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular:<\/strong><\/p>\n
1. PyTorch’ta bellek s\u0131z\u0131nt\u0131lar\u0131 nas\u0131l tespit edilir?<\/strong>  Bellek s\u0131z\u0131nt\u0131lar\u0131n\u0131 tespit etmek i\u00e7in, bellek kullan\u0131m\u0131n\u0131 izlemek ve tensor’lar\u0131n ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc dikkatlice incelemek \u00f6nemlidir.  Profiling ara\u00e7lar\u0131 da kullan\u0131labilir.<\/p>\n
2. \u00c7oklu GPU kullan\u0131rken hangi y\u00f6ntem daha uygundur? DataParallel<\/code> mi yoksa DistributedDataParallel<\/code> m\u0131?<\/strong>  DataParallel<\/code>, basit ve kullan\u0131m\u0131 kolayd\u0131r, ancak daha b\u00fcy\u00fck \u00f6l\u00e7ekli modeller i\u00e7in DistributedDataParallel<\/code> daha uygun olabilir.<\/p>\n
3. torch.cuda.empty_cache()<\/code> fonksiyonu ne zaman kullan\u0131lmal\u0131d\u0131r?<\/strong>  Bu fonksiyon, GPU belle\u011finde kullan\u0131lmayan alanlar\u0131 serbest b\u0131rakmak i\u00e7in kullan\u0131labilir, ancak a\u015f\u0131r\u0131 kullan\u0131m performans\u0131 olumsuz etkileyebilir.  Gerekti\u011finde kullan\u0131lmal\u0131d\u0131r.<\/p>\n
4. PyTorch’ta bellek y\u00f6netimi i\u00e7in ba\u015fka hangi ara\u00e7lar mevcuttur?<\/strong>  torch.utils.checkpoint<\/code> ve pin_memory=True<\/code> gibi ara\u00e7lar, bellek y\u00f6netimini optimize etmek i\u00e7in kullan\u0131labilir.<\/p>\n
5. Da\u011f\u0131t\u0131k e\u011fitim i\u00e7in hangi yaz\u0131l\u0131m ve donan\u0131m gereklidir?<\/strong>  Da\u011f\u0131t\u0131k e\u011fitim i\u00e7in, birden fazla makine ve y\u00fcksek h\u0131zl\u0131 a\u011f ba\u011flant\u0131s\u0131 gereklidir.  Ayr\u0131ca, torch.distributed<\/code> paketini kullanman\u0131z gerekir.<\/p>\n
Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"# PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131 Derin \u00f6\u011frenme projelerinizde, \u00f6zellikle b\u00fcy\u00fck veri setleri ve karma\u015f\u0131k…","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-29970","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nPyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131<\/title>\n<meta name=\"description\" content=\"Hi\u00e7bir etiketi bulunamad\u0131.\" \/>\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\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131\" \/>\n<meta property=\"og:description\" content=\"Hi\u00e7bir etiketi bulunamad\u0131.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-22T06:40:26+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131\",\"datePublished\":\"2025-09-22T06:40:26+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\"},\"wordCount\":1340,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\",\"name\":\"PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-09-22T06:40:26+00:00\",\"description\":\"Hi\u00e7bir etiketi bulunamad\u0131.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pytorch-101-bellek-yonetimi-ve-coklu-gpu-kullanimi\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"PyTorch 101: Bellek Y\u00f6netimi ve \u00c7oklu GPU Kullan\u0131m\u0131\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - 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