{"id":66242,"date":"2024-12-11T10:29:31","date_gmt":"2024-12-11T10:29:31","guid":{"rendered":"https:\/\/www.sardinasystems.com\/sin-categorizar\/fishos-powered-machine-learning-cloud-for-successful-scientific-research\/"},"modified":"2025-05-14T16:16:31","modified_gmt":"2025-05-14T16:16:31","slug":"fishos-powered-machine-learning-cloud-for-successful-scientific-research","status":"publish","type":"post","link":"https:\/\/www.sardinasystems.com\/es\/nuestro-blog\/fishos-powered-machine-learning-cloud-for-successful-scientific-research\/","title":{"rendered":"Nube de Aprendizaje Autom\u00e1tico Potenciada por FishOS para Investigaci\u00f3n Cient\u00edfica Exitosa"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Durante casi dos a\u00f1os de estrecha colaboraci\u00f3n con <strong>uno de los principales centros de investigaci\u00f3n de Alemania, la Universidad de T\u00fcbingen<\/strong>, Sardina Systems ha proporcionado un apoyo esencial para mejorar la eficiencia y productividad de la infraestructura de nube de aprendizaje autom\u00e1tico de la universidad. <br\/>En este estudio de caso, profundizaremos en c\u00f3mo <strong>FishOS, la plataforma de gesti\u00f3n de nube totalmente automatizada, escalable y rentable de Sardina Systems<\/strong>, ayuda a un peque\u00f1o equipo de expertos a manejar sus tareas diarias para que los cient\u00edficos de datos puedan concentrar sus esfuerzos en llevar a cabo investigaciones innovadoras en campos como la medicina, la agricultura, la conducci\u00f3n aut\u00f3noma y otras \u00e1reas, y ofrecer soluciones transformadoras.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Conociendo la Nube de ML<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\"><a href=\"https:\/\/uni-tuebingen.de\/en\/\" target=\"_blank\" rel=\"noopener\" title=\"La Nube de ML en la Universidad de T&#xFC;bingen\">La Nube de ML en la Universidad de T\u00fcbingen<\/a>,<\/mark><\/strong> que comprende el Centro de IA y el Cl\u00faster de Excelencia en ML, impulsa proyectos impactantes <strong>en imagen m\u00e9dica, incluyendo estudios de c\u00e1ncer y cerebro<\/strong>, as\u00ed como <strong>visi\u00f3n por computadora y an\u00e1lisis de movimiento humano<\/strong>, iniciativas de simulaci\u00f3n y predicci\u00f3n clim\u00e1tica, e incluso el entrenamiento de <strong>un robot de Boston Dynamics para tareas agr\u00edcolas<\/strong>. Con la participaci\u00f3n de <strong>48 grupos de investigaci\u00f3n<\/strong>, estos proyectos est\u00e1n empujando los l\u00edmites y <strong>haciendo contribuciones significativas<\/strong> en diversos dominios. <\/p>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1024x576.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1024x576.png\" alt=\"Esquema de la Nube de Aprendizaje Autom&#xE1;tico\" class=\"wp-image-66212\" srcset=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1024x576.png 1024w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-300x169.png 300w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-768x432.png 768w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1536x864.png 1536w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1390x782.png 1390w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1920x1080.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\">Actualmente, recursos de personal limitados est\u00e1n involucrados en el mantenimiento de <strong>un cl\u00faster que consta de aproximadamente 200 n\u00facleos<\/strong>, responsables de todas las operaciones de datos. Exploremos los desaf\u00edos que enfrentaron los especialistas antes de implementar FishOS, y c\u00f3mo la plataforma les ayud\u00f3 a superar estas dificultades. <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Abrazando el Desaf\u00edo<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">El equipo de TI de la universidad enfrenta desaf\u00edos distintivos inherentes a las organizaciones p\u00fablicas. Operando dentro de estrictas restricciones de financiamiento gubernamental, deben asegurar <strong>el funcionamiento sin problemas de un sistema de computaci\u00f3n cient\u00edfica en m\u00faltiples unidades<\/strong> mientras tambi\u00e9n abordan <strong>las diversas necesidades de computaci\u00f3n de varios grupos de investigaci\u00f3n.<\/strong> La estabilidad es crucial para los investigadores que buscan resultados precisos, especialmente en campos como la investigaci\u00f3n del c\u00e1ncer y del cerebro, donde el poder de c\u00f3mputo es indispensable para las aplicaciones de aprendizaje autom\u00e1tico. <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Navegando los Obst\u00e1culos<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">En respuesta a estos desaf\u00edos, <strong>el equipo de la universidad opt\u00f3 por aprovechar la infraestructura local<\/strong> para maximizar la eficiencia del presupuesto y cumplir con los requisitos de capacidad. Desarrollaron <strong>una arquitectura de hardware adaptada para satisfacer las diversas necesidades<\/strong> de peque\u00f1os grupos de investigaci\u00f3n dispersos por toda Alemania. <strong>Esta arquitectura combina virtualizaci\u00f3n y metal desnudo de manera equilibrada<\/strong>, complementada por una instalaci\u00f3n de almacenamiento distribuido <strong>Ceph<\/strong> para fines de archivo. <\/p>\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1024x576.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1024x576.png\" alt=\"Visi&#xF3;n General de la Infraestructura de la Nube de Aprendizaje Autom&#xE1;tico\" class=\"wp-image-66221\" srcset=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1024x576.png 1024w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-300x169.png 300w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-768x432.png 768w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1536x864.png 1536w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1390x782.png 1390w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1920x1080.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\">Aproximadamente <strong>15-20% de los nodos del sistema est\u00e1n asignados a equilibradores de carga<\/strong>, con los nodos restantes dedicados a la potencia de c\u00f3mputo. Los hipervisores est\u00e1n equipados con configuraciones <strong>solo CPU, GPU V100 y GPU A100<\/strong>, con aproximadamente <strong>el 70% de los nodos actualmente utilizados a trav\u00e9s de metal desnudo.<\/strong> Los recursos de GPU, ubicados tanto en hipervisores como en metal desnudo, se priorizan <strong>para mejorar la capacidad de respuesta del sistema<\/strong>. Adem\u00e1s, el sistema acomoda tareas m\u00e1s convencionales proporcionando CPUs cl\u00e1sicas para funciones como la gesti\u00f3n de conjuntos de datos.  <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">FishOS: Apoyando una Infraestructura Estable y Simplificando Operaciones<\/mark><\/strong><\/p>\n\n<ul class=\"wp-block-list\">\n<li><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\"><em>Interfaz Unificada.<\/em> <\/mark>Con FishOS, gestionar la infraestructura de la Nube de ML, distribuida en varias \u00e1reas, a trav\u00e9s de una interfaz unificada se ha vuelto notablemente simplificado.<\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Flexibilidad.<\/mark><\/em> Quiz\u00e1s una de las ventajas m\u00e1s notables de la Nube de ML es la versatilidad de FishOS: puede ejecutarse tanto en nodos de metal desnudo como en GPUs. Esta flexibilidad aumenta enormemente el rendimiento y facilita sin problemas el logro de los objetivos de investigaci\u00f3n. <\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Adaptabilidad.<\/mark><\/em> Kristina Kapanova, Jefa de la Nube de ML, elogia a FishOS por su adaptabilidad. <\/li>\n<\/ul>\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><em>\u201cOfrece una amplia gama de opciones de servicio adaptadas a necesidades espec\u00edficas: ya sea gestionando cargas de trabajo m\u00e1s peque\u00f1as o m\u00e1s grandes, publicando conjuntos de datos o realizando benchmarks. Para m\u00ed, tambi\u00e9n es un resultado positivo, ya que podemos gestionar f\u00e1cilmente los cambios a trav\u00e9s de FishOS.\u201d<\/em>, dice la Sra. Kapanova.<\/p>\n<\/blockquote>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Estabilidad.<\/mark><\/em> Otra ventaja crucial de la plataforma que se alinea perfectamente con los requisitos de la universidad es la estabilidad general del sistema.<\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Ahorro de Tiempo.<\/mark><\/em> Adem\u00e1s, el considerable tiempo ahorrado por FishOS para otras tareas y prop\u00f3sitos es de suma importancia, ya que todas las modificaciones necesarias pueden ejecutarse con unos pocos comandos. Por ejemplo, en solo 3-4 sencillos pasos, los expertos en el ML Cloud pueden configurar r\u00e1pidamente un sistema completamente operativo para los usuarios. El aspecto crucial aqu\u00ed radica en la efectividad y rapidez con la que se lleva a cabo esta tarea, satisfaciendo las crecientes demandas de los usuarios que siempre buscan m\u00e1s.   <\/li>\n<\/ul>\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><br\/><em>\u201cPude entregar un servicio a los usuarios en una semana que probablemente nos habr\u00eda llevado un mes. La r\u00e1pida provisi\u00f3n de funcionalidad para los usuarios y la consiguiente facilidad en mi carga de trabajo son invaluables,\u201d<\/em> destaca la Sra. Kapanova. <\/p>\n<\/blockquote>\n\n<p class=\"wp-block-paragraph\"><br\/>Al abordar las necesidades de los usuarios, el sistema de tickets del ML Cloud sirve como la principal herramienta de comunicaci\u00f3n interna de la organizaci\u00f3n, desempe\u00f1ando un papel vital en la pronta resoluci\u00f3n de cualquier problema emergente. Su buen funcionamiento tambi\u00e9n se atribuye a FishOS. <\/p>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Gesti\u00f3n de Redes.<\/mark><\/em> Adem\u00e1s de la estabilidad, la gesti\u00f3n de redes es una prioridad. La plataforma supervisa sin problemas todo el sistema asignando y gestionando direcciones IP, liberando a los expertos de otra pesada responsabilidad y permiti\u00e9ndoles centrarse en tareas de mayor nivel. <\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Sin Interrupciones.<\/mark><\/em> Kristina Kapanova no observa interrupciones en el uso de la plataforma. Aunque los especialistas del ML Cloud pueden encontrar problemas de hardware ocasionalmente, una vez resueltos, la infraestructura en la nube vuelve a estar operativa en un par de minutos. <em>\u00abFue la primera vez que experiment\u00e9 un retorno al servicio tan fluido,\u00bb<\/em> se\u00f1ala la Sra. Kapanova. <\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Soporte.<\/mark><\/em> Una ventaja innegable de FishOS es su soporte incre\u00edblemente r\u00e1pido y confiable proporcionado a la universidad. Desde el inicio de la adopci\u00f3n de la plataforma por parte del ML Cloud, el equipo t\u00e9cnico de Sardina Systems ofreci\u00f3 una gu\u00eda detallada. Esto permiti\u00f3 a los especialistas, incluso a aquellos no familiarizados con las operaciones rutinarias, dominarlas r\u00e1pidamente.   <\/li>\n<\/ul>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1024x576.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1024x576.png\" alt=\"\" class=\"wp-image-66194\" srcset=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1024x576.png 1024w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-300x169.png 300w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-768x432.png 768w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1536x864.png 1536w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1390x782.png 1390w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1920x1080.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\"><br\/>Cuando varias m\u00e1quinas virtuales experimentaron una desaceleraci\u00f3n repentina durante el L2L Challenge, una de las competiciones de aprendizaje autom\u00e1tico m\u00e1s significativas para cient\u00edficos, el problema de la insuficiencia de RAM fue identificado y resuelto r\u00e1pidamente en unos pocos d\u00edas. Los esfuerzos efectivos y bien coordinados de los expertos de Sardina Systems fueron fundamentales para prevenir incidentes similares en el futuro. <em>\u00abConsiderando su excelente soporte, funcionalidad y beneficios de ahorro de tiempo, as\u00ed como la disponibilidad continua de todos los sistemas, FishOS parece ofrecer una excelente relaci\u00f3n calidad-precio,\u00bb<\/em> a\u00f1ade la Sra. Kapanova. <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">\u00bfQu\u00e9 sigue?: Curso de Innovaci\u00f3n y Crecimiento de T\u00fcbingen<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">En cuanto a los planes futuros,<strong> el Centro de IA dentro del ML Cloud est\u00e1 previsto que duplique su tama\u00f1o <\/strong>en los pr\u00f3ximos a\u00f1os. En consecuencia, el ML Cloud est\u00e1 expandiendo su infraestructura <strong>para satisfacer las crecientes demandas de computaci\u00f3n<\/strong>. La universidad pretende ampliar sus capacidades incorporando <strong>procesadores no GPU para cargas de trabajo espec\u00edficas de IA y ML <\/strong>en diferentes \u00e1reas. Esta iniciativa tiene como objetivo<strong> desarrollar soluciones personalizadas para reducir el consumo de energ\u00eda mientras se mantiene el rendimiento. <\/strong>    <\/p>\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><br\/><em>\u00abEstoy encantada de que Sardina Systems haya probado FishOS para <\/em><a href=\"https:\/\/www.sardinasystems.com\/es\/noticias-es\/sardina-systems-anuncia-compatibilidad-sin-fisuras-con-los-nuevos-procesadores-gaudi2\/\"><em>compatibilidad con Gaudi2<\/em><\/a><em>, y los primeros benchmarks parecen prometedores. Adem\u00e1s, planeo usar FPGAs con la plataforma para ofrecer m\u00e1s opciones a los usuarios. La oportunidad de implementaciones bare-metal con estos nodos es particularmente gratificante,\u00bb  <\/em> comenta la Sra. Kapanova.<\/p>\n<\/blockquote>\n\n<p class=\"wp-block-paragraph\">Adem\u00e1s, el ML Cloud est\u00e1 aumentando su utilizaci\u00f3n de almacenamiento Ceph, y Sardina Systems proporcionar\u00e1 soporte para esto, dado que FishOS est\u00e1 construido sobre la tecnolog\u00eda Ceph.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">El Futuro Paso para el \u00c9xito del Machine Learning Cloud<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">FishOS se presenta como un cambio de juego para las instituciones acad\u00e9micas. Simplificando los flujos de trabajo y liberando a los investigadores de las complejidades t\u00e9cnicas, empodera a universidades como T\u00fcbingen para innovar sin problemas mientras optimizan las actividades operativas. <\/p>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/es\/sobre-nosotros\/contactos\/\"><img decoding=\"async\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/Banner&#x2013;CTA-offer-1024x231.png\" alt=\"\" class=\"wp-image-66203\"\/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Durante casi dos a\u00f1os de estrecha colaboraci\u00f3n con uno de los principales centros de investigaci\u00f3n de Alemania, la Universidad de T\u00fcbingen, Sardina Systems ha proporcionado un apoyo esencial para mejorar la eficiencia y productividad de la infraestructura de nube de aprendizaje autom\u00e1tico de la universidad. En este estudio de caso, profundizaremos en c\u00f3mo FishOS, la [&hellip;]<\/p>\n","protected":false},"author":20,"featured_media":66293,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1285],"tags":[3195,3196,3197,1346,1497],"class_list":["post-66242","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nuestro-blog","tag-ai-cloud-es","tag-cloud-for-researchers-es","tag-machine-learning-cloud-es","tag-infraestructura-informatica","tag-cloud_sardinasystems-es","grve-entry-item","grve-blog-item"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"Discover how FishOS powers AI and Machine Learning cloud, enabling efficient and successful scientific research for the University of T\u00fcbingen.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" 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