{"id":9035,"date":"2025-01-09T02:30:15","date_gmt":"2025-01-08T23:30:15","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/docker-ile-makine-ogrenmesi-modellerini-olusturma-kapsayiciliga-alma-ve-dagitim\/"},"modified":"2025-01-09T02:30:15","modified_gmt":"2025-01-08T23:30:15","slug":"docker-ile-makine-ogrenmesi-modellerini-olusturma-kapsayiciliga-alma-ve-dagitim","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/docker-ile-makine-ogrenmesi-modellerini-olusturma-kapsayiciliga-alma-ve-dagitim\/","title":{"rendered":"Docker ile Makine \u00d6\u011frenmesi Modellerini Olu\u015fturma, Kapsay\u0131c\u0131l\u0131\u011fa Alma ve Da\u011f\u0131t\u0131m"},"content":{"rendered":"<p><body><\/p>\n<p>Docker ile Makine \u00d6\u011frenmesi Modellerini Olu\u015fturma, Kapsay\u0131c\u0131l\u0131\u011fa Alma ve Da\u011f\u0131t\u0131m<\/p>\n<p>Merhaba! Fatih Soysal buraday\u0131m.  Bu kapsaml\u0131 k\u0131lavuzda, Docker Desktop kullanarak makine \u00f6\u011frenmesi (ML) modellerinizi olu\u015fturma, kapsay\u0131c\u0131l\u0131\u011fa alma ve da\u011f\u0131t\u0131m\u0131n\u0131 nas\u0131l kolayla\u015ft\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz.  Bu s\u00fcre\u00e7, geli\u015ftirmeden \u00fcretime kadar olan t\u00fcm a\u015famalar\u0131 kapsayacak ve modelinizin tutarl\u0131 ve sorunsuz bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacakt\u0131r.  \u00d6ncelikle, temel kavramlar\u0131 a\u00e7\u0131klayarak ba\u015flayal\u0131m. Daha sonra, pratik bir \u00f6rnek \u00fczerinde konuyu detayl\u0131 \u015fekilde inceleyece\u011fiz.<\/p>\n<h2>Neden Docker Kullanmal\u0131s\u0131n\u0131z?<\/h2>\n<p>Docker, uygulamalar\u0131n\u0131z\u0131 ve ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131 kapsay\u0131c\u0131lara paketlemenizi sa\u011flayan bir platformdur. Bu sayede, geli\u015ftirme ortam\u0131n\u0131zda \u00e7al\u0131\u015fan modeliniz, \u00fcretim ortam\u0131nda da ayn\u0131 \u015fekilde \u00e7al\u0131\u015facakt\u0131r.  Bu durum, farkl\u0131 ortamlar aras\u0131nda uyumluluk sorunlar\u0131n\u0131 ortadan kald\u0131r\u0131r ve da\u011f\u0131t\u0131m\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde kolayla\u015ft\u0131r\u0131r.  Ayr\u0131ca, kaynaklar\u0131 daha verimli kullanman\u0131za ve modelinizin \u00f6l\u00e7eklenebilirli\u011fini art\u0131rman\u0131za yard\u0131mc\u0131 olur. Ba\u015fka bir deyi\u015fle, Docker sayesinde  modelinizin ta\u015f\u0131nabilirli\u011fi ve y\u00f6netimi olduk\u00e7a kolayla\u015f\u0131r.<\/p>\n<h2>Ad\u0131m 1: Makine \u00d6\u011frenmesi Modelinizi Geli\u015ftirme<\/h2>\n<p>\u0130lk ad\u0131m, Python gibi bir programlama dili ve Scikit-learn, TensorFlow veya PyTorch gibi bir makine \u00f6\u011frenmesi k\u00fct\u00fcphanesi kullanarak modelinizi geli\u015ftirmektir.  \u00d6rne\u011fin, basit bir do\u011frusal regresyon modeli olu\u015fturabilirsiniz.  Bu a\u015famada, verilerinizi haz\u0131rlamak, modeli e\u011fitmek ve performans\u0131n\u0131 de\u011ferlendirmek \u00f6nemlidir.  Bu s\u00fcre\u00e7te, verilerinizi temizlemek, uygun \u00f6zellikler se\u00e7mek ve modeli do\u011frulamak gibi ad\u0131mlar\u0131 atmal\u0131s\u0131n\u0131z.  Daha sonra, bu modeli, Docker ile kapsay\u0131c\u0131l\u0131\u011fa alma i\u015flemine haz\u0131r hale getirece\u011fiz.<\/p>\n<h2>Ad\u0131m 2: Dockerfile Olu\u015fturma<\/h2>\n<p>Dockerfile, Docker g\u00f6r\u00fcnt\u00fcn\u00fcz\u00fc olu\u015fturmak i\u00e7in gereken talimatlar\u0131 i\u00e7eren bir dosyad\u0131r. Bu dosya, temel bir imaj se\u00e7meyi, ba\u011f\u0131ml\u0131l\u0131klar\u0131 y\u00fcklemeyi ve modelinizi \u00e7al\u0131\u015ft\u0131rmak i\u00e7in gereken komutlar\u0131 belirlemeyi kapsar.  A\u015fa\u011f\u0131daki \u00f6rnek bir Dockerfile g\u00f6stermektedir:<\/p>\n<pre><code class=\"language-dockerfile\">\nFROM python:3.9-slim-buster\n\nWORKDIR \/app\n\nCOPY requirements.txt requirements.txt\nRUN pip install --no-cache-dir -r requirements.txt\n\nCOPY . .\n\nCMD [\"python\", \"app.py\"]\n<\/code><\/pre>\n<p>Bu Dockerfile, Python 3.9 tabanl\u0131 bir imaj kullan\u0131r, gerekli k\u00fct\u00fcphaneleri y\u00fckler ve  <code>app.py<\/code> dosyas\u0131n\u0131 \u00e7al\u0131\u015ft\u0131r\u0131r.  <code>requirements.txt<\/code> dosyas\u0131nda, modelinizin ihtiya\u00e7 duydu\u011fu t\u00fcm Python k\u00fct\u00fcphaneleri listelenmelidir.  Bu dosyay\u0131,  <code>pip freeze > requirements.txt<\/code> komutu ile olu\u015fturabilirsiniz.<\/p>\n<h2>Ad\u0131m 3: Docker G\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc Olu\u015fturma ve \u00c7al\u0131\u015ft\u0131rma<\/h2>\n<p>Dockerfile&#8217;\u0131n\u0131z\u0131 olu\u015fturduktan sonra,  <code>docker build -t my-ml-model .<\/code> komutu ile Docker g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc olu\u015fturabilirsiniz.  &#8220;.&#8221;, Dockerfile&#8217;\u0131n bulundu\u011fu dizini g\u00f6stermektedir.  Ard\u0131ndan,  <code>docker run -p 8000:8000 my-ml-model<\/code> komutu ile g\u00f6r\u00fcnt\u00fcy\u00fc \u00e7al\u0131\u015ft\u0131rabilirsiniz.  Bu komut, konteyn\u0131r\u0131n 8000 portunu ana makinenin 8000 portuna e\u015fler.  \u015eimdi, modeliniz Docker konteyneri i\u00e7inde \u00e7al\u0131\u015f\u0131yor ve herhangi bir yerden eri\u015filebilir hale geliyor.  Bu sayede, modelinizin da\u011f\u0131t\u0131m\u0131n\u0131 olduk\u00e7a kolayla\u015ft\u0131rm\u0131\u015f oluyoruz.<\/p>\n<h2>Ad\u0131m 4:  Docker Hub&#8217;a Da\u011f\u0131t\u0131m<\/h2>\n<p>Son olarak, olu\u015fturdu\u011funuz Docker g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc Docker Hub gibi bir konteyner kay\u0131t deposuna y\u00fckleyebilirsiniz. Bu sayede, g\u00f6r\u00fcnt\u00fcy\u00fc daha sonra kolayca indirip \u00e7al\u0131\u015ft\u0131rabilirsiniz.  Docker Hub&#8217;a g\u00f6r\u00fcnt\u00fcy\u00fc y\u00fcklemek i\u00e7in, \u00f6ncelikle bir Docker Hub hesab\u0131 olu\u015fturman\u0131z gerekmektedir.  Ard\u0131ndan,  <code>docker push your-username\/my-ml-model<\/code> komutu ile g\u00f6r\u00fcnt\u00fcy\u00fc Docker Hub&#8217;a y\u00fckleyebilirsiniz.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>Bu k\u0131lavuzda, Docker Desktop kullanarak makine \u00f6\u011frenmesi modellerini olu\u015fturma, kapsay\u0131c\u0131l\u0131\u011fa alma ve da\u011f\u0131t\u0131m s\u00fcrecini ad\u0131m ad\u0131m ele ald\u0131k.  Docker,  modelinizin tutarl\u0131l\u0131\u011f\u0131n\u0131, ta\u015f\u0131nabilirli\u011fini ve \u00f6l\u00e7eklenebilirli\u011fini art\u0131rman\u0131za yard\u0131mc\u0131 olur.  Umar\u0131m bu makale size yard\u0131mc\u0131 olmu\u015ftur.  Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz. Ayr\u0131ca, Docker resmi d\u00f6k\u00fcmantasyonuna <a href=\"https:\/\/docs.docker.com\/\">buradan<\/a> ula\u015fabilirsiniz.<\/p>\n<p><strong>Not:<\/strong> Bu makalede verilen \u00f6rnekler, basit bir senaryoyu g\u00f6stermektedir.  Ger\u00e7ek d\u00fcnya uygulamalar\u0131nda daha karma\u015f\u0131k konfig\u00fcrasyonlara ihtiya\u00e7 duyabilirsiniz.<\/p>\n<p>#Etiketler: Docker, Makine \u00d6\u011frenmesi, ML, Model Da\u011f\u0131t\u0131m\u0131, Kapsay\u0131c\u0131, Docker Desktop, Python, TensorFlow, Scikit-learn, Dockerfile, Docker Hub<br \/>\n<\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"Docker ile Makine \u00d6\u011frenmesi Modellerini Olu\u015fturma, Kapsay\u0131c\u0131l\u0131\u011fa Alma ve Da\u011f\u0131t\u0131m Merhaba! Fatih Soysal buraday\u0131m. Bu kapsaml\u0131 k\u0131lavuzda, Docker&hellip;","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-9035","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Docker ile Makine \u00d6\u011frenmesi Modellerini Olu\u015fturma, Kapsay\u0131c\u0131l\u0131\u011fa Alma ve Da\u011f\u0131t\u0131m<\/title>\n<meta name=\"description\" content=\"Merhaba! Fatih Soysal buraday\u0131m. Bu kapsaml\u0131 k\u0131lavuzda, Docker Desktop kullanarak makine \u00f6\u011frenmesi (ML) modellerinizi olu\u015fturma, kapsay\u0131c\u0131l\u0131\u011fa alma ve da\u011f\u0131t\u0131m\u0131n\u0131 nas\u0131l kolayla\u015ft\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Bu s\u00fcre\u00e7, geli\u015ftirmeden \u00fcretime kadar olan t\u00fcm a\u015famalar\u0131 kapsayacak ve modelinizin tutarl\u0131 ve sorunsuz bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacakt\u0131r. \u00d6ncelikle, temel kavramlar\u0131 a\u00e7\u0131klayarak ba\u015flayal\u0131m. Daha sonra, pratik bir \u00f6rnek \u00fczerinde konuyu detayl\u0131 \u015fekilde inceleyece\u011fiz.\" \/>\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\/docker-ile-makine-ogrenmesi-modellerini-olusturma-kapsayiciliga-alma-ve-dagitim\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Docker ile Makine \u00d6\u011frenmesi Modellerini Olu\u015fturma, Kapsay\u0131c\u0131l\u0131\u011fa Alma ve Da\u011f\u0131t\u0131m\" \/>\n<meta property=\"og:description\" content=\"Merhaba! Fatih Soysal buraday\u0131m. Bu kapsaml\u0131 k\u0131lavuzda, Docker Desktop kullanarak makine \u00f6\u011frenmesi (ML) modellerinizi olu\u015fturma, kapsay\u0131c\u0131l\u0131\u011fa alma ve da\u011f\u0131t\u0131m\u0131n\u0131 nas\u0131l kolayla\u015ft\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Bu s\u00fcre\u00e7, geli\u015ftirmeden \u00fcretime kadar olan t\u00fcm a\u015famalar\u0131 kapsayacak ve modelinizin tutarl\u0131 ve sorunsuz bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacakt\u0131r. \u00d6ncelikle, temel kavramlar\u0131 a\u00e7\u0131klayarak ba\u015flayal\u0131m. 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Fatih Soysal buraday\u0131m. Bu kapsaml\u0131 k\u0131lavuzda, Docker Desktop kullanarak makine \u00f6\u011frenmesi (ML) modellerinizi olu\u015fturma, kapsay\u0131c\u0131l\u0131\u011fa alma ve da\u011f\u0131t\u0131m\u0131n\u0131 nas\u0131l kolayla\u015ft\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Bu s\u00fcre\u00e7, geli\u015ftirmeden \u00fcretime kadar olan t\u00fcm a\u015famalar\u0131 kapsayacak ve modelinizin tutarl\u0131 ve sorunsuz bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacakt\u0131r. \u00d6ncelikle, temel kavramlar\u0131 a\u00e7\u0131klayarak ba\u015flayal\u0131m. 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