{"id":53128,"date":"2019-11-24T00:00:00","date_gmt":"2019-11-23T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/google-vypustil-distributiv-mendel-linux-4-0-dlya-plat-coral"},"modified":"2020-02-18T14:00:59","modified_gmt":"2020-02-18T11:00:59","slug":"google-vypustil-distributiv-mendel-linux-4-0-dlya-plat-coral","status":"publish","type":"post","link":"https:\/\/prohoster.info\/es\/blog\/news\/google-vypustil-distributiv-mendel-linux-4-0-dlya-plat-coral","title":{"rendered":"Google lanz\u00f3 la distribuci\u00f3n Mendel Linux 4.0 para placas Coral","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>La empresa Google <noindex><a rel=\"nofollow\" href=\"https:\/\/developers.googleblog.com\/2019\/11\/updates-from-coral-mendel-linux-4.html\">ha presentado<\/a><\/noindex> actualizaci\u00f3n de la distribuci\u00f3n <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.ai\/software\/#mendel-linux\">Mendel Linux<\/a><\/noindex>, dise\u00f1ado para su uso en placas <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.ai\/\">Coral<\/a><\/noindex>, como <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.ai\/products\/dev-board\/\">Dev Board<\/a><\/noindex> y <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.ai\/products\/som\/\">SoM<\/a><\/noindex>. La placa Dev Board es una plataforma para el desarrollo r\u00e1pido de prototipos de sistemas de hardware basados en <noindex><a rel=\"nofollow\" href=\"https:\/\/ru.wikipedia.org\/wiki\/%D0%A2%D0%B5%D0%BD%D0%B7%D0%BE%D1%80%D0%BD%D1%8B%D0%B9_%D0%BF%D1%80%D0%BE%D1%86%D0%B5%D1%81%D1%81%D0%BE%D1%80_Google\">Google Edge TPU<\/a><\/noindex> (Unidad de Procesamiento Tensorial) para acelerar las operaciones relacionadas con el aprendizaje autom\u00e1tico y las redes neuronales. SoM (System-on-Module) es una de las soluciones listas para ejecutar aplicaciones relacionadas con el aprendizaje autom\u00e1tico.<\/p>\n<p>La distribuci\u00f3n Mendel Linux <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.googlesource.com\/docs\/+\/refs\/heads\/master\/ReadMe.md\">se basa<\/a><\/noindex> se basa en paquetes de Debian y es completamente compatible con los repositorios de este proyecto (se utilizan paquetes binarios no modificados y actualizaciones de los principales repositorios de Debian). Los cambios se limitan a la construcci\u00f3n de la imagen que se carga desde tarjetas eMMC y a la inclusi\u00f3n de componentes para soportar los componentes de hardware de la plataforma Coral. Componentes espec\u00edficos de Coral <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.googlesource.com\/\">se distribuyen<\/a><\/noindex> bajo la licencia Apache 2.0. <\/p>\n<p>Mendel Linux 4.0 <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.googlesource.com\/docs\/+\/refs\/heads\/master\/Releases.md\">fue<\/a><\/noindex> la primera versi\u00f3n, actualizada a Debian 10 (&#171;buster&#187;). La compilaci\u00f3n est\u00e1 optimizada para sistemas integrados y no contiene elementos innecesarios, incluidas las novedades de Debian 10 relacionadas con el soporte de SecureBoot y AppArmor. Entre las novedades se destaca el soporte para OpenCV y OpenCL, el uso de superposiciones Device Tree, as\u00ed como la actualizaci\u00f3n de GStreamer, Python 3.7, el n\u00facleo de Linux 4.14 y el cargador de arranque U-Boot 2017.03.3. <\/p>\n<p>Entre las novedades espec\u00edficas, se menciona la capacidad de usar la GPU instalada en las placas Coral (Vivante GC7000) para acelerar la conversi\u00f3n de datos de p\u00edxeles del modelo de color YUV a RGB con un rendimiento de hasta 130 fotogramas por segundo para videos en resoluci\u00f3n 1080p, lo cual puede ser \u00fatil al usar placas para el procesamiento de videos de c\u00e1maras que generan flujos en formato YUV. Para aplicar el aprendizaje autom\u00e1tico al procesamiento de video y audio en tiempo real, se propone utilizar el marco abierto <noindex><a rel=\"nofollow\" href=\"https:\/\/mediapipe.dev\/\">MediaPipe<\/a><\/noindex>. Por ejemplo, sobre esta base se puede <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/google\/mediapipe\/tree\/master\/mediapipe\/examples\/coral\">implementar<\/a><\/noindex> crear un sistema de reconocimiento y seguimiento de objetos o caras en video transmitido desde una c\u00e1mara de vigilancia.<\/p>\n<p>Modelos de aprendizaje autom\u00e1tico ya entrenados y listos, compilados para procesadores Edge TPU utilizados en las placas Coral, contin\u00faan siendo proporcionados en <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.ai\/models\/\">el sitio del proyecto<\/a><\/noindex>, pero gradualmente se trasladan al cat\u00e1logo general de modelos de acceso p\u00fablico <noindex><a rel=\"nofollow\" href=\"https:\/\/tfhub.dev\/\">TensorFlow Hub<\/a><\/noindex>. Para facilitar el desarrollo de soluciones propias basadas en placas Coral y Mendel Linux, se ha preparado <noindex><a rel=\"nofollow\" href=\"https:\/\/coral.ai\/projects\/teachable-sorter\/\">la gu\u00eda<\/a><\/noindex>, que muestra c\u00f3mo construir un clasificador inteligente que separa bolas de colores y blancas en diferentes cestas, utilizando Raspberry Pi y Coral USB Accelerator.<\/p>\n<p><center><div class=\"youtube-placeholder\" data-id=\"ydzJPeeMiMI\" onclick=\"loadVideo(this)\">\r\n        <img decoding=\"async\" src=\"https:\/\/img.youtube.com\/vi\/ydzJPeeMiMI\/hqdefault.jpg\" alt=\"Reproducir video\" loading=\"lazy\" width=\"480\" height=\"360\" style=\"width:100%;height:auto;\">\r\n        <div class=\"play-button\"><\/div>\r\n    <\/div><\/center><\/p>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Fuente: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=51924\">opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Google \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u0434\u0438\u0441\u0442\u0440\u0438\u0431\u0443\u0442\u0438\u0432\u0430 Mendel Linux, \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u0434\u043b\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u043d\u0430 \u043f\u043b\u0430\u0442\u0430\u0445 Coral, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a Dev Board \u0438 SoM. \u041f\u043b\u0430\u0442\u0430 Dev Board \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0443 \u0434\u043b\u044f \u0431\u044b\u0441\u0442\u0440\u043e\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u043f\u0440\u043e\u0442\u043e\u0442\u0438\u043f\u043e\u0432 \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u044b\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u043d\u0430 \u0431\u0430\u0437\u0435 Google Edge TPU (Tensor Processing Unit) \u0434\u043b\u044f \u0443\u0441\u043a\u043e\u0440\u0435\u043d\u0438\u044f \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439, \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 \u0438 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u043c\u0438 \u0441\u0435\u0442\u044f\u043c\u0438. SoM (System-on-Module) \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043e\u0434\u043d\u0438\u043c \u0438\u0437 \u0433\u043e\u0442\u043e\u0432\u044b\u0445 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-53128","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Google \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u0434\u0438\u0441\u0442\u0440\u0438\u0431\u0443\u0442\u0438\u0432\u0430\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta 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