Yapay zeka d\u00fcnyas\u0131nda, \u00f6zellikle de karma\u015f\u0131k sim\u00fclasyonlar s\u00f6z konusu oldu\u011funda, kaynak k\u0131s\u0131tlamalar\u0131 s\u0131k\u00e7a kar\u015f\u0131la\u015f\u0131lan bir engeldir. Peki, tek bir 8GB GPU ile, birden fazla ajan\u0131n bili\u015fsel s\u00fcre\u00e7lerini ve etkile\u015fimlerini bar\u0131nd\u0131ran \u00e7oklu ajan sim\u00fclasyonlar\u0131n\u0131 \u00e7al\u0131\u015ft\u0131rmak bir hayal mi? Bu makale, bili\u015fsel mimarilerin g\u00fcc\u00fcn\u00fc ve ak\u0131ll\u0131 optimizasyon stratejilerini kullanarak bu zorlu\u011fun \u00fcstesinden nas\u0131l gelinece\u011fini ad\u0131m ad\u0131m a\u00e7\u0131kl\u0131yor, b\u00f6ylece m\u00fctevaz\u0131 bir donan\u0131mla bile \u015fa\u015f\u0131rt\u0131c\u0131 sonu\u00e7lar elde edilebilece\u011fini g\u00f6steriyor.<\/p>\n
G\u00fcn\u00fcm\u00fcz yapay zeka ara\u015ft\u0131rmalar\u0131 ve uygulamalar\u0131, giderek daha karma\u015f\u0131k sistemlere do\u011fru evriliyor. \u00d6zellikle \u00e7oklu ajan sistemleri (Multi-Agent Systems – MAS), ger\u00e7ek d\u00fcnya senaryolar\u0131n\u0131 modellemek, karma\u015f\u0131k davran\u0131\u015flar\u0131 anlamak ve yeni \u00e7\u00f6z\u00fcmler geli\u015ftirmek i\u00e7in vazge\u00e7ilmez bir ara\u00e7 haline gelmi\u015ftir. Trafik ak\u0131\u015f\u0131ndan pazar ekonomilerine, sosyal dinamiklerden robotik s\u00fcr\u00fclere kadar pek \u00e7ok alanda, birbirleriyle etkile\u015fim halinde olan ajanlar\u0131n sim\u00fclasyonlar\u0131 b\u00fcy\u00fck de\u011fer ta\u015f\u0131r. Ancak bu t\u00fcr sim\u00fclasyonlar, her bir ajan\u0131n alg\u0131, karar verme ve eylem s\u00fcre\u00e7lerini modellemek i\u00e7in ciddi hesaplama kaynaklar\u0131 gerektirir.<\/p>\n
Geleneksel olarak, bu t\u00fcr b\u00fcy\u00fck \u00f6l\u00e7ekli ve bili\u015fsel olarak zengin sim\u00fclasyonlar i\u00e7in birden fazla y\u00fcksek performansl\u0131 GPU’ya veya geni\u015f \u00e7apl\u0131 CPU k\u00fcmelerine ihtiya\u00e7 duyuldu\u011fu d\u00fc\u015f\u00fcn\u00fcl\u00fcr. Her bir ajan\u0131n kendi i\u00e7inde bir t\u00fcr “beyin” bar\u0131nd\u0131rmas\u0131, yani bili\u015fsel bir mimariye sahip olmas\u0131, bellek ve i\u015flem g\u00fcc\u00fc taleplerini katlayarak art\u0131r\u0131r. Bu durum, \u00f6zellikle bireysel ara\u015ft\u0131rmac\u0131lar, k\u00fc\u00e7\u00fck ekipler veya b\u00fct\u00e7e k\u0131s\u0131tlamalar\u0131 olan kurumlar i\u00e7in \u00f6nemli bir engel te\u015fkil edebilir. Pahal\u0131 donan\u0131m yat\u0131r\u0131mlar\u0131 yapmadan, mevcut kaynaklarla bile bu t\u00fcr ileri d\u00fczey sim\u00fclasyonlar\u0131 geli\u015ftirebilmek ve \u00e7al\u0131\u015ft\u0131rabilmek, yapay zeka alan\u0131nda demokratikle\u015fmeyi sa\u011flayacak kritik bir ad\u0131md\u0131r.<\/p>\n
\u0130\u015fte tam da bu noktada, tek bir 8GB GPU’nun potansiyelini sorguluyoruz. Sadece 8GB belle\u011fe sahip bir grafik i\u015flem birimi, binlerce hatta on binlerce ajan\u0131n etkile\u015fimini, \u00f6\u011frenmesini ve karma\u015f\u0131k karar alma s\u00fcre\u00e7lerini y\u00f6netebilir mi? Bu soruya verilecek olumlu bir yan\u0131t, yapay zeka sim\u00fclasyonlar\u0131n\u0131n eri\u015filebilirli\u011fini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilir ve daha fazla ara\u015ft\u0131rmac\u0131n\u0131n ve geli\u015ftiricinin bu alana katk\u0131da bulunmas\u0131n\u0131n \u00f6n\u00fcn\u00fc a\u00e7abilir. Bu makale boyunca, bu iddial\u0131 hedefe ula\u015fmak i\u00e7in hangi tekniklerin ve stratejilerin kullan\u0131labilece\u011fini ke\u015ffedece\u011fiz. Bellek optimizasyonundan ak\u0131ll\u0131 bili\u015fsel modellemeye, paralel i\u015flemden hibrit yakla\u015f\u0131mlara kadar bir\u00e7ok konuyu ele alarak, tek bir GPU’nun s\u0131n\u0131rlar\u0131n\u0131 nas\u0131l zorlayabilece\u011fimizi g\u00f6sterece\u011fiz.<\/p>\n
Konunun derinliklerine inmeden \u00f6nce, temel kavramlar\u0131 netle\u015ftirmekte fayda var. \u00c7oklu ajan sistemleri ve bili\u015fsel mimariler, bu t\u00fcr sim\u00fclasyonlar\u0131n temel yap\u0131 ta\u015flar\u0131d\u0131r ve birbirleriyle yak\u0131ndan ili\u015fkilidir. Bu kavramlar\u0131 anlamak, tek bir 8GB GPU’da dahi nas\u0131l verimli sistemler kurabilece\u011fimizi kavramam\u0131za yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n
\u00c7oklu ajan sistemleri, ortak bir ortamda birbiriyle etkile\u015fim kuran, genellikle \u00f6zerk (autonomous) ve bazen de \u00f6\u011frenme yetene\u011fine sahip birden fazla ajandan olu\u015fan sistemlerdir. Her bir ajan, kendi alg\u0131lar\u0131na, hedeflerine ve eylem yeteneklerine sahiptir. Bu ajanlar aras\u0131ndaki etkile\u015fimler, sistem genelinde karma\u015f\u0131k ve bazen \u00f6ng\u00f6r\u00fclemeyen davran\u0131\u015flar\u0131n ortaya \u00e7\u0131kmas\u0131na neden olabilir. \u00d6rne\u011fin, bir trafik sim\u00fclasyonunda her ara\u00e7 bir ajan olarak kabul edilebilir; kendi h\u0131z\u0131n\u0131, \u015ferit se\u00e7imini ve di\u011fer ara\u00e7larla olan mesafesini dikkate alarak hareket eder. Bu bireysel kararlar\u0131n toplam\u0131, t\u00fcm trafik ak\u0131\u015f\u0131n\u0131 ve t\u0131kan\u0131kl\u0131klar\u0131 belirler.<\/p>\n
MAS’lar\u0131n temel faydalar\u0131 aras\u0131nda, da\u011f\u0131t\u0131k problem \u00e7\u00f6zme yetene\u011fi ve ortaya \u00e7\u0131kan (emergent) davran\u0131\u015flar\u0131 modelleme imkan\u0131 bulunur. B\u00fcy\u00fck ve karma\u015f\u0131k bir problemi tek bir merkezi birim yerine, k\u00fc\u00e7\u00fck ve y\u00f6netilebilir par\u00e7alara b\u00f6lerek \u00e7\u00f6zebilirler. Bu da sistemin daha esnek, dayan\u0131kl\u0131 ve \u00f6l\u00e7eklenebilir olmas\u0131n\u0131 sa\u011flar. Ayr\u0131ca, bireysel ajanlar\u0131n basit kurallara g\u00f6re hareket etmesiyle bile, genel sistemde \u015fa\u015f\u0131rt\u0131c\u0131 derecede karma\u015f\u0131k ve ger\u00e7ek\u00e7i davran\u0131\u015f kal\u0131plar\u0131 g\u00f6zlemlenebilir. Bir ekonomideki bireylerin al\u0131m sat\u0131m kararlar\u0131, bir \u015fehrin geli\u015fimini veya bir salg\u0131n\u0131n yay\u0131lma h\u0131z\u0131n\u0131 etkileyen sosyal etkile\u015fimler, MAS ile modellenen di\u011fer \u00f6nemli senaryolard\u0131r. Bu sistemler, yapay zeka ara\u015ft\u0131rmac\u0131lar\u0131na, sosyologlara, ekonomistlere ve \u015fehir planc\u0131lar\u0131na, hipotezleri test etmek ve farkl\u0131 senaryolar\u0131n sonu\u00e7lar\u0131n\u0131 tahmin etmek i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7 sunar.<\/p>\n
Bir ajan\u0131n “beyni” olarak d\u00fc\u015f\u00fcnebilece\u011fimiz bili\u015fsel mimari, bir ajan\u0131n nas\u0131l alg\u0131lad\u0131\u011f\u0131n\u0131, d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fcn\u00fc, \u00f6\u011frendi\u011fini ve eyleme ge\u00e7ti\u011fini tan\u0131mlayan yap\u0131d\u0131r. Bu mimariler, ajana bir t\u00fcr zeka kazand\u0131rarak, basit kural tabanl\u0131 sistemlerin \u00f6tesine ge\u00e7mesini sa\u011flar. \u00d6rne\u011fin, ACT-R ve SOAR gibi klasik bili\u015fsel mimariler, insan bili\u015finin belirli y\u00f6nlerini modellemeye \u00e7al\u0131\u015f\u0131rken, modern yakla\u015f\u0131mlar genellikle derin \u00f6\u011frenme (deep learning) modellerini veya hibrit sistemleri i\u00e7erir. Bir bili\u015fsel mimari, genellikle \u015fu bile\u015fenleri i\u00e7erir:<\/p>\n
Bili\u015fsel mimarilerin \u00f6nemi, ajanlar\u0131n sadece \u00f6nceden programlanm\u0131\u015f davran\u0131\u015flar\u0131 sergilemekle kalmay\u0131p, ayn\u0131 zamanda de\u011fi\u015fen ko\u015fullara adapte olabilen, \u00f6\u011frenebilen ve karma\u015f\u0131k problemleri \u00e7\u00f6zebilen varl\u0131klar olmalar\u0131n\u0131 sa\u011flamas\u0131d\u0131r. Bu, sim\u00fclasyonlar\u0131n ger\u00e7ek\u00e7ili\u011fini ve de\u011ferini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r. Ancak, her bir ajan\u0131n bu kadar zengin bir i\u00e7 yap\u0131ya sahip olmas\u0131, GPU belle\u011fi ve i\u015flem g\u00fcc\u00fc \u00fczerinde ciddi bir y\u00fck olu\u015fturur. Bu nedenle, s\u0131n\u0131rl\u0131 kaynaklarla \u00e7al\u0131\u015f\u0131rken, bu mimarileri nas\u0131l optimize edece\u011fimizi anlamak kritik hale gelir.<\/p>\n
Grafik \u0130\u015flem Birimleri (GPU’lar), ba\u015flang\u0131\u00e7ta bilgisayar grafiklerini i\u015flemek i\u00e7in tasarlanm\u0131\u015f olsalar da, g\u00fcn\u00fcm\u00fczde yapay zeka ve bilimsel hesaplamalar i\u00e7in vazge\u00e7ilmez ara\u00e7lar haline gelmi\u015flerdir. Bunun temel nedeni, GPU’lar\u0131n binlerce k\u00fc\u00e7\u00fck i\u015flem \u00e7ekirde\u011fine sahip olmalar\u0131 ve bu sayede ayn\u0131 anda bir\u00e7ok i\u015flemi paralel olarak y\u00fcr\u00fctebilmeleridir. Derin \u00f6\u011frenme modellerinin e\u011fitimi, matris \u00e7arp\u0131mlar\u0131 gibi tekrarlayan ve yo\u011fun hesaplama gerektiren g\u00f6revler i\u00e7in bu paralel yap\u0131 idealdir.<\/p>\n
\u00c7oklu ajan sim\u00fclasyonlar\u0131nda, her bir ajan\u0131n alg\u0131lama, karar verme ve eylem s\u00fcre\u00e7leri, genellikle benzer hesaplama ad\u0131mlar\u0131 i\u00e7erir. Bu ad\u0131mlar, \u00f6zellikle ajan say\u0131s\u0131 artt\u0131k\u00e7a, GPU’nun paralel i\u015fleme yetene\u011finden b\u00fcy\u00fck \u00f6l\u00e7\u00fcde faydalanabilir. \u00d6rne\u011fin, y\u00fczlerce veya binlerce ajan\u0131n ayn\u0131 anda ortam\u0131 alg\u0131lay\u0131p, kendi bili\u015fsel modellerinden ge\u00e7erek karar almas\u0131 gerekti\u011finde, bu i\u015flemlerin her biri GPU \u00fczerinde e\u015fzamanl\u0131 olarak y\u00fcr\u00fct\u00fclebilir. Bu, sim\u00fclasyonlar\u0131n \u00e7ok daha h\u0131zl\u0131 \u00e7al\u0131\u015fmas\u0131n\u0131 ve daha b\u00fcy\u00fck \u00f6l\u00e7eklere ula\u015fmas\u0131n\u0131 sa\u011flar. Ancak, GPU’lar\u0131n g\u00fcc\u00fc, ayn\u0131 zamanda s\u0131n\u0131rl\u0131 bir bellekle (VRAM) gelir. Bir\u00e7ok modern derin \u00f6\u011frenme modeli, \u00f6zellikle b\u00fcy\u00fck dil modelleri veya g\u00f6r\u00fcnt\u00fc i\u015fleme modelleri, gigabaytlarca VRAM t\u00fcketebilir. Tek bir 8GB GPU ile \u00e7al\u0131\u015f\u0131rken, bu s\u0131n\u0131rl\u0131 belle\u011fi en verimli \u015fekilde kullanmak, sim\u00fclasyonun \u00f6l\u00e7eklenebilirli\u011fi ve performans\u0131 i\u00e7in anahtar fakt\u00f6r haline gelir. Bu durum, bizi bellek dostu algoritmalar ve modellemeler geli\u015ftirmeye iter.<\/p>\n
Tek bir 8GB GPU ile bili\u015fsel mimariye sahip \u00e7oklu ajan sim\u00fclasyonlar\u0131 \u00e7al\u0131\u015ft\u0131rmak, ak\u0131ll\u0131 bellek y\u00f6netimi ve performans optimizasyon stratejileri gerektirir. Bu b\u00f6l\u00fcmde, bu s\u0131n\u0131rl\u0131 kaynaklarla maksimum verimi nas\u0131l elde edebilece\u011fimize dair temel s\u0131rlar\u0131 ve teknikleri ele alaca\u011f\u0131z. Her gigabayt\u0131n ve her i\u015flem d\u00f6ng\u00fcs\u00fcn\u00fcn de\u011ferli oldu\u011fu bu senaryoda, her ayr\u0131nt\u0131 \u00f6nemlidir.<\/p>\n
GPU belle\u011fi (VRAM), \u00f6zellikle 8GB gibi s\u0131n\u0131rl\u0131 bir kapasiteye sahip oldu\u011funda, en kritik k\u0131s\u0131tlay\u0131c\u0131 fakt\u00f6rlerden biridir. Bu belle\u011fi etkin bir \u015fekilde y\u00f6netmek, sim\u00fclasyonunuzun ne kadar b\u00fcy\u00fck olabilece\u011fini ve ne kadar h\u0131zl\u0131 \u00e7al\u0131\u015fabilece\u011fini do\u011frudan etkiler. \u0130\u015fte uygulayabilece\u011finiz baz\u0131 stratejiler:<\/p>\n
.cpu()<\/code> metodu ile) veya tamamen silin (del<\/code> anahtar kelimesi ve torch.cuda.empty_cache()<\/code> ile). Python’\u0131n \u00e7\u00f6p toplama mekanizmas\u0131 her zaman an\u0131nda \u00e7al\u0131\u015fmad\u0131\u011f\u0131ndan, manuel bellek y\u00f6netimi bazen gerekli olabilir.<\/li>\n- Model Payla\u015f\u0131m\u0131:<\/strong> E\u011fer t\u00fcm ajanlar benzer bili\u015fsel mimarilere sahipse, her ajan i\u00e7in ayr\u0131 bir model \u00f6rne\u011fi olu\u015fturmak yerine, a\u011f\u0131rl\u0131klar\u0131 payla\u015f\u0131lan tek bir ana model kullan\u0131n. Bu, model a\u011f\u0131rl\u0131klar\u0131n\u0131n bellekte yaln\u0131zca bir kez depolanmas\u0131n\u0131 sa\u011flayarak \u00f6nemli \u00f6l\u00e7\u00fcde tasarruf sa\u011flar. Ajanlar\u0131n farkl\u0131l\u0131klar\u0131, modelin giri\u015fine eklenen ajana \u00f6zg\u00fc bir kimlik veya ba\u011flam vekt\u00f6r\u00fc ile temsil edilebilir.<\/li>\n<\/ul>\n
Ajan Davran\u0131\u015flar\u0131n\u0131 Etkin \u015eekilde Modellemek: Hafif Bili\u015fsel Modeller<\/h3>\n
Her ajana tam te\u015fekk\u00fcll\u00fc, karma\u015f\u0131k bir derin \u00f6\u011frenme modeli (\u00f6rne\u011fin, b\u00fcy\u00fck bir Transformer a\u011f\u0131) atamak, 8GB GPU i\u00e7in h\u0131zl\u0131ca bir darbo\u011faz olu\u015fturacakt\u0131r. Bunun yerine, bili\u015fsel mimarileri tasarlarken “hafiflik” ilkesini benimsemek \u00f6nemlidir:<\/p>\n
\n- Basit Kural Tabanl\u0131 Sistemler ve Sonlu Durum Makineleri:<\/strong> T\u00fcm ajan davran\u0131\u015flar\u0131 i\u00e7in derin \u00f6\u011frenmeye ba\u015fvurmak yerine, baz\u0131 temel veya reaktif davran\u0131\u015flar i\u00e7in basit kural tabanl\u0131 sistemler (
if-else<\/code> yap\u0131lar\u0131) veya sonlu durum makineleri (Finite State Machines – FSM) kullan\u0131n. Bu yakla\u015f\u0131mlar, neredeyse hi\u00e7 bellek t\u00fcketmez ve \u00e7ok h\u0131zl\u0131 \u00e7al\u0131\u015f\u0131r. \u00d6rne\u011fin, bir trafik sim\u00fclasyonunda, “\u00f6n\u00fcmdeki ara\u00e7 durursa ben de dururum” gibi basit kurallar, karma\u015f\u0131k bir sinir a\u011f\u0131na ihtiya\u00e7 duymadan uygulanabilir.<\/li>\n- K\u00fc\u00e7\u00fck ve \u00d6zelle\u015fmi\u015f Sinir A\u011flar\u0131:<\/strong> E\u011fer derin \u00f6\u011frenme gerekiyorsa, model boyutlar\u0131n\u0131 minimumda tutun. Daha az katman, daha az n\u00f6ron ve daha k\u00fc\u00e7\u00fck giri\u015f\/\u00e7\u0131k\u0131\u015f boyutlar\u0131na sahip a\u011flar kullan\u0131n. Her ajan\u0131n t\u00fcm ortam\u0131 i\u015flemesi yerine, yaln\u0131zca kendi yak\u0131n \u00e7evresindeki bilgileri alg\u0131layacak \u015fekilde tasarlay\u0131n. Bu, hem bellek t\u00fcketimini hem de hesaplama y\u00fck\u00fcn\u00fc azalt\u0131r.<\/li>\n
- Payla\u015f\u0131ml\u0131 Temsiller ve G\u00f6mme (Embeddings):<\/strong> Ajanlar\u0131n durumlar\u0131n\u0131 veya ortam\u0131n belirli \u00f6zelliklerini temsil etmek i\u00e7in d\u00fc\u015f\u00fck boyutlu g\u00f6mme vekt\u00f6rleri kullan\u0131n. Bu g\u00f6mmeler, daha karma\u015f\u0131k bilgileri daha kompakt bir \u015fekilde saklaman\u0131z\u0131 sa\u011flar ve derin \u00f6\u011frenme modellerinin giri\u015f boyutlar\u0131n\u0131 k\u00fc\u00e7\u00fclt\u00fcr. E\u011fer ajanlar aras\u0131nda ortak \u00f6zellikler varsa, bu \u00f6zellikler i\u00e7in payla\u015f\u0131ml\u0131 g\u00f6mme katmanlar\u0131 kullanmak bellekten tasarruf sa\u011flar.<\/li>\n
- Hierar\u015fik (Hiyerar\u015fik) Bili\u015fsel Modeller:<\/strong> Ajan\u0131n bili\u015fsel s\u00fcre\u00e7lerini hiyerar\u015fik bir yap\u0131da d\u00fczenleyin. \u00d6rne\u011fin, d\u00fc\u015f\u00fck seviyeli, reaktif davran\u0131\u015flar i\u00e7in basit modeller kullan\u0131rken, daha y\u00fcksek seviyeli, stratejik kararlar i\u00e7in daha karma\u015f\u0131k ama daha az s\u0131kl\u0131kta \u00e7al\u0131\u015fan modeller kullan\u0131n. Bu, hesaplama y\u00fck\u00fcn\u00fc zamana yayarak GPU’yu a\u015f\u0131r\u0131 y\u00fcklemekten ka\u00e7\u0131nman\u0131z\u0131 sa\u011flar.<\/li>\n<\/ul>\n
Bu optimizasyon tekniklerini bir araya getirerek, tek bir 8GB GPU’da bile \u015fa\u015f\u0131rt\u0131c\u0131 derecede karma\u015f\u0131k ve b\u00fcy\u00fck \u00f6l\u00e7ekli \u00e7oklu ajan sim\u00fclasyonlar\u0131 \u00e7al\u0131\u015ft\u0131rmak m\u00fcmk\u00fcn hale gelir. Anahtar, kaynaklar\u0131n\u0131z\u0131 ak\u0131ll\u0131ca y\u00f6netmek ve her tasar\u0131m karar\u0131nda bellek ve i\u015flem verimlili\u011fini g\u00f6z \u00f6n\u00fcnde bulundurmakt\u0131r.<\/p>\n
Uygulamal\u0131 Bir Bak\u0131\u015f: Tek GPU’da \u00c7oklu Ajan Sim\u00fclasyonu Ad\u0131mlar\u0131: Kendi Sim\u00fclasyonunuzu Nas\u0131l Kurars\u0131n\u0131z?<\/h2>\n
Teorik bilgilerin \u00f6tesine ge\u00e7erek, \u015fimdi tek bir 8GB GPU’da bili\u015fsel ajan sim\u00fclasyonunu ad\u0131m ad\u0131m nas\u0131l kuraca\u011f\u0131m\u0131za dair pratik bir bak\u0131\u015f sunal\u0131m. Bu b\u00f6l\u00fcm, bir sim\u00fclasyon ortam\u0131n\u0131n nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131, bili\u015fsel ajan mimarisinin nas\u0131l tasarlanaca\u011f\u0131n\u0131 ve bellek dostu bir sim\u00fclasyon d\u00f6ng\u00fcs\u00fcn\u00fcn nas\u0131l uygulanaca\u011f\u0131n\u0131 g\u00f6steren basitle\u015ftirilmi\u015f bir \u00f6rnek i\u00e7erecektir. Amac\u0131m\u0131z, okuyucunun konuyu s\u0131f\u0131rdan \u00f6\u011frenebilece\u011fi ve kendi projelerine uygulayabilece\u011fi bir temel sa\u011flamakt\u0131r.<\/p>\n
Ortam Kurulumu ve Gerekli K\u00fct\u00fcphaneler<\/h3>\n
\u00d6ncelikle, projemiz i\u00e7in gerekli olan temel ara\u00e7lar\u0131 ve k\u00fct\u00fcphaneleri kurmal\u0131y\u0131z. Python, yapay zeka ve sim\u00fclasyon geli\u015ftirmek i\u00e7in en pop\u00fcler dillerden biridir. Derin \u00f6\u011frenme i\u015flemleri i\u00e7in PyTorch veya TensorFlow k\u00fct\u00fcphaneleri, GPU h\u0131zland\u0131rmas\u0131ndan faydalanmak i\u00e7in vazge\u00e7ilmezdir.<\/p>\n
\n- Python:<\/strong> Genellikle 3.8 veya \u00fczeri bir s\u00fcr\u00fcm \u00f6nerilir.<\/li>\n
- PyTorch:<\/strong> GPU deste\u011fi ile birlikte kurulmal\u0131d\u0131r. Kurulum talimatlar\u0131 i\u00e7in PyTorch’un resmi web sitesine bakabilirsiniz.<\/li>\n
- NumPy:<\/strong> Say\u0131sal i\u015flemler i\u00e7in standart bir k\u00fct\u00fcphanedir.<\/li>\n
- Di\u011fer K\u00fct\u00fcphaneler (iste\u011fe ba\u011fl\u0131):<\/strong> Sim\u00fclasyon ortam\u0131 g\u00f6rselle\u015ftirmesi i\u00e7in Mesa (\u00e7oklu ajan sim\u00fclasyonlar\u0131 i\u00e7in bir framework) veya Matplotlib gibi k\u00fct\u00fcphaneler kullan\u0131labilir.<\/li>\n<\/ul>\n
Kurulum i\u00e7in tipik komutlar (GPU deste\u011fi i\u00e7in CUDA s\u00fcr\u00fcm\u00fcn\u00fcze g\u00f6re PyTorch komutunu ayarlay\u0131n):<\/p>\n
\n\n pip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118 # CUDA 11.8 i\u00e7in\n pip install numpy\n # pip install mesa # E\u011fer \u00e7oklu ajan ortam\u0131 i\u00e7in daha geli\u015fmi\u015f bir framework kullanacaksan\u0131z\n <\/pre>\n<\/code>\n <\/div>\n
Kurulumdan sonra, sisteminizin GPU'yu tan\u0131y\u0131p tan\u0131mad\u0131\u011f\u0131n\u0131 kontrol edebilirsiniz:<\/p>\n
\n\n import torch\n print(torch.cuda.is_available()) # True d\u00f6nmeli\n print(torch.cuda.get_device_name(0)) # GPU'nuzun ad\u0131n\u0131 g\u00f6stermeli\n <\/pre>\n<\/code>\n <\/div>\n
Bili\u015fsel Ajan Mimarisi Tasar\u0131m\u0131<\/h3>\n
\u015eimdi, sim\u00fclasyonumuzdaki ajanlar\u0131n nas\u0131l davranaca\u011f\u0131n\u0131 tan\u0131mlayan bili\u015fsel mimariyi tasarlayal\u0131m. Basit bir trafik sim\u00fclasyonunda, ajanlar\u0131m\u0131z (arabalar) \u00e7evrelerini alg\u0131layacak, h\u0131zlan<\/p>\n","protected":false},"excerpt":{"rendered":"Yapay zeka d\u00fcnyas\u0131nda, \u00f6zellikle de karma\u015f\u0131k sim\u00fclasyonlar s\u00f6z konusu oldu\u011funda, kaynak k\u0131s\u0131tlamalar\u0131 s\u0131k\u00e7a kar\u015f\u0131la\u015f\u0131lan bir engeldir.","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-41522","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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