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The document presents the solution architecture and covers components and provisioning workflows, with revisions and release notes dated February 2019.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/dell-emc-ready-solutions-for-ai-deep-learning-with-nvidia-architecture-guide/640206/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/dell-emc-ready-solutions-for-ai-deep-learning-with-nvidia-architecture-guide/640206.png","ImageObject",300,407,{"name":42,"@type":43},"Đào","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-10-22",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does this document address for deep learning deployments?","Question",{"text":63,"@type":64},"Designing deep learning solutions is complex and time-consuming due to the many available options. 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Dell EMC’s Ready Solutions for AI – Deep Learning with NVIDIA is a complete solution, designed to support all phases of Deep Learning , incorporates the latest CPU, GPU, memory, network, storage, and software technologies with impressive performance for both training and inference phases. The architecture of this Deep Learning solution is presented in this document.  \nFebruary 2019  \nRevisions  \n\n| Date | Description |\n| --- | --- |\n| February 2019 | Release 1.1 |\n| August 2018 | Initial release |\n\nThe information in this publication is provided “as is.” Dell Inc. makes no representations or warranties of any kind with respect to the information in this publication, and specifically disclaims implied warranties of merchantability or fitness for a particular purpose.  \nUse, copying, and distribution of any software described in this publication requires an applicable software license.  \n© February 2019 – v1.1 Dell Inc. or its subsidiaries. All Rights Reserved. Dell, EMC, Dell EMC and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be trademarks of their respective owners.  \nDell believes the information in this document is accurate as of its publication date. The information is subject to change without notice.  \nTable of Contents  \nRevisions.............................................................................................................................................................................2  \nTable of Contents ................................................................................................................................................................3  \nExecutive summary.............................................................................................................................................................4  \n1 Solution Overview ........................................................................................................................................................5  \n2 Solution Architecture ....................................................................................................................................................7  \n2.1 Head Node Configuration ...................................................................................................................................7  \n2.1.1 Shared Storage via NFS over InfiniBand ...........................................................................................................8  \n2.2 Compute Node Configuration .............................................................................................................................8  \n2.2.1 GPU ....................................................................................................................................................................9  \n2.3 Processor recommendation for Head Node and Compute Nodes ...................................................................10  \n2.4 Memory recommendation for Head Node and Compute Nodes ......................................................................10  \n2.5 Isilon Storage .................................................................................................................................................... 11  \n2.6 Network ............................................................................................................................................................. 12  \n2.7 Software............................................................................................................................................................ 13  \n2.8 NVIDIA DIGITS Tool and the Deep Learning Solution ..............","cbCainbaGBMEZ4vm","https://ap.wps.com/l/cbCainbaGBMEZ4vm","pdf",2388317,28,"English","# Executive summary\n# 1 Solution Overview\n# 2 Solution Architecture\n## 2.1 Head Node Configuration\n## 2.2 Compute Node Configuration\n## 2.3 Processor recommendation for Head Node and Compute Nodes\n## 2.4 Memory recommendation for Head Node and Compute Nodes\n## 2.5 Isilon Storage\n## 2.6 Network\n## 2.7 Software\n## 2.8 NVIDIA DIGITS Tool and the Deep Learning Solution\n# 3 Containers for Deep Learning\n## 3.1 Singularity Containers\n## 3.2 Running NVIDIA GPU Cloud with the Ready Solutions for AI-Deep Learning\n# 4 The Data Science Provisioning Portal\n## 4.1 Creating and Running a Notebook\n## 4.2 TensorBoard Integration\n## 4.3 Slurm Scheduler\n# 5 Conclusions","[{\"question\":\"What problem does this document address for deep learning deployments?\",\"answer\":\"Designing deep learning solutions is complex and time-consuming due to the many available options. The document provides an integrated architecture that supports the full deep learning workflow.\"},{\"question\":\"Which technologies are included in Dell EMC’s Ready Solutions for AI?\",\"answer\":\"The solution incorporates CPU, GPU, memory, network, storage, and software technologies, targeting strong performance for both training and inference.\"},{\"question\":\"What areas does the guide cover beyond the overall solution architecture?\",\"answer\":\"It includes container approaches for deep learning, plus a data science provisioning portal with notebook creation, TensorBoard integration, and Slurm scheduling.\"}]","Dell EMC Ready Solutions for AI - Deep Learning with NVIDIA - Architecture Guide | PDF",1792708137,71]