{"id":113469,"date":"2024-02-08T22:02:49","date_gmt":"2024-02-08T20:02:49","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/vypusk-savant-0-2-7-frejmvorka-kompyuternogo-zreniya-i-glubokogo-obucheniya"},"modified":"2024-02-08T22:02:49","modified_gmt":"2024-02-08T20:02:49","slug":"vypusk-savant-0-2-7-frejmvorka-kompyuternogo-zreniya-i-glubokogo-obucheniya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hr\/blog\/news\/vypusk-savant-0-2-7-frejmvorka-kompyuternogo-zreniya-i-glubokogo-obucheniya","title":{"rendered":"Izdanje Savant 0.2.7, okvira za ra\u010dunalni vid i duboko u\u010denje","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Objavljen je Python framework Savant 0.2.7, koji pojednostavljuje kori\u0161tenje NVIDIA DeepStream za rje\u0161avanje zadataka povezanih s strojno u\u010denje. Framework preuzima sav slo\u017eeni rad s GStreamerom ili FFmpegom, omogu\u0107uju\u0107i fokusiranje na izgradnju optimiziranih izlaznih kontejnera koriste\u0107i deklarativnu sintaksu (YAML) i funkcije Pythona. Savant omogu\u0107uje stvaranje pipelinea koji rade jednako na akceleratorima u data centru (NVIDIA Turing, Ampere, Hopper) i na edge ure\u0111ajima (NVIDIA Jetson NX, AGX Xavier, Orin NX, AGX Orin, New Nano). Uz Savant, mo\u017eete lako obraditi vi\u0161e video streamova istovremeno, brzo stvoriti gotove za rad kontejnera video analitike, koji koriste NVIDIA TensorRT. Kod projekta distribuira se pod licencom Apache 2.0.      <\/p>\n<p>Savant 0.2.7 \u2014 zadnje izdanje s promjenama funkcionalnosti u grani 0.2.X. Sljede\u0107a izdanja u grani 0.2.X uklju\u010divat \u0107e samo ispravke gre\u0161aka. Razvoj novih funkcija odvijat \u0107e se u grani 0.3.X, koja se temelji na DeepStream 6.4. Ova grana ne\u0107e podr\u017eavati obitelj ure\u0111aja Jetson Xavier, jer ih tvrtka NVIDIA ne podr\u017eava u DS 6.4.      <\/p>\n<p>Glavne novine:  <\/p>\n<ul>\n<li class=\"l\"> Novi primjeri kori\u0161tenja:\n<ul>\n<li class=\"l\"> Primjer rada s modelom detekcije temeljenim na transformatoru RT-DETR;\n<li class=\"l\"> CUDA postprocesiranje s CuPy za YOLOV8-Seg;\n<li class=\"l\"> Primjer integracije PyTorch CUDA u Savant pipeline;\n<li class=\"l\"> Demonstracija rada s orijentiranim objektima.  <\/ul>\n<p>  <center><img decoding=\"async\" alt=\"Izdanje Savant 0.2.7, okvira za ra\u010dunalni vid i duboko u\u010denje\" src=\"\/wp-content\/uploads\/2024\/02\/0c14b808a6767394352a4eafde317f67.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/center>    <\/p>\n<li class=\"l\">  Nove funkcije:\n<ul>\n<li class=\"l\"> Integracija s Prometheusom. Pipeline mo\u017ee izvoziti izvedbene metrike u Prometheus i Grafanu za pra\u0107enje i analizu performansi. Programeri mogu deklarirati korisni\u010dke metrike koje se izvoze zajedno sa sistemskim metrima.\n<li class=\"l\"> Buffor adapter \u2014 implementira trajni transakcijski buffer na disku za podatke koji se kre\u0107u izme\u0111u adaptera i modula. Pomo\u0107u njega se mogu razvijati visokointezivni cjevovodi koji nepredvidivo tro\u0161e resurse i izdr\u017eavaju vrhove prometa. Adapter izvozi svoje podatke o elementima i veli\u010dinama u Prometheus.\n<li class=\"l\"> Mod za kompilaciju modela. Moduli sada mogu kompilirati svoje modele u TensorRT bez pokretanja pipelinea.\n<li class=\"l\"> Handler za doga\u0111aje shutdown u PyFunc. Ovaj novi API omogu\u0107uje ispravno upravljanje operacijama zatvaranja pipelinea, osloba\u0111aju\u0107i resurse i obavje\u0161tavaju\u0107i vanjske sustave o zavr\u0161etku rada.\n<li class=\"l\"> Filtriranje kadrova na ulazu i izlazu. Po defaultu, cjevovod prima sve okvire koji sadr\u017ee video podatke. Pomo\u0107u filtriranja na ulazu i izlazu, programeri mogu filtrirati podatke kako bi ih izuzeli iz obrade.\n<li class=\"l\"> Postobrada modela na GPU-u. Zahvaljuju\u0107i novoj funkciji, programeri mogu pristupiti izlaznim tenzorima modela direktno iz memorije GPU-a, bez u\u010ditavanja u memoriju CPU-a, i obraditi ih pomo\u0107u CuPy, TorchVision ili OpenCV CUDA.\n<li class=\"l\"> Funkcije prikaza GPU memorije. U ovom izdanju pru\u017eili smo funkcije za konvertiranje memorijskih bafera izme\u0111u OpenCV GpuMat, GPU tenzora PyTorch i tenzora CuPy.\n<li class=\"l\"> API za pristup statistici kori\u0161tenja redova cjevovoda. Savant omogu\u0107ava dodavanje redova izme\u0111u PyFunc za implementaciju paralelne obrade i puferiranja. Dodani API programerima omogu\u0107ava pristup redovima raspore\u0111enim u cjevovodu i omogu\u0107ava upit o njihovom kori\u0161tenju.  <\/ul>\n<\/ul>\n<p>U sljede\u0107em izdanju (0.3.7) planira se pre\u0107i na DeepStream 6.4 bez pro\u0161irenja funkcionalnosti. Ideja je dobiti izdanje potpuno kompatibilno s 0.2.7, ali temeljeno na DeepStream 6.4 i pobolj\u0161anoj tehnologiji, bez prekida u kompatibilnosti na razini API-ja.<br \/>\n<br \/>Izvor: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=60570\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d \u0432\u044b\u043f\u0443\u0441\u043a Python-\u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 Savant 0.2.7, \u0443\u043f\u0440\u043e\u0449\u0430\u044e\u0449\u0435\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 NVIDIA DeepStream \u0434\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u0437\u0430\u0434\u0430\u0447, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0441 \u043c\u0430\u0448\u0438\u043d\u043d\u044b\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435\u043c. \u0424\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a \u0431\u0435\u0440\u0435\u0442 \u043d\u0430 \u0441\u0435\u0431\u044f \u0432\u0441\u044e \u0441\u043b\u043e\u0436\u043d\u0443\u044e \u0440\u0430\u0431\u043e\u0442\u0443 \u0441 GStreamer \u0438\u043b\u0438 FFmpeg, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044f \u0441\u043e\u0441\u0440\u0435\u0434\u043e\u0442\u043e\u0447\u0438\u0442\u044c\u0441\u044f \u043d\u0430 \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0438 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043a\u043e\u043d\u0432\u0435\u0439\u0435\u0440\u043e\u0432 \u0432\u044b\u0432\u043e\u0434\u0430 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0434\u0435\u043a\u043b\u0430\u0440\u0430\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441\u0430 (YAML) \u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 Python. Savant \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u0442\u044c \u043a\u043e\u043d\u0432\u0435\u0439\u0435\u0440\u044b (pipeline), \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u043e \u0440\u0430\u0431\u043e\u0442\u0430\u044e\u0442 \u043a\u0430\u043a \u043d\u0430 \u0443\u0441\u043a\u043e\u0440\u0438\u0442\u0435\u043b\u044f\u0445 \u0432 \u0434\u0430\u0442\u0430\u0446\u0435\u043d\u0442\u0440\u0435 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":113470,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-113469","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.4 - aioseo.com -->\n\t<meta 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