BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Pacific Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20191004T171845Z DESCRIPTION:We’ll demonstrate how to use GPUs and AI to build machine lea rning applications more easily. It’s not the data science that’s hard\ , but all the operations around it: deploying tools\, integrating hardware \, creating data and machine learning frameworks\, running jobs at scale\, and reproducing results. GPUs accelerate performance but pose problems su ch as resource sharing\, software dependencies\, and data bottlenecks. In a cloud-native era\, data scientists want a GPU-powered\, open source mach ine learning platform as a service such as AWS Sagemaker or Google AI\, wi thout vendor lock-ins or on-premises software. We’ll show how to integra te Kubernetes\, KubeFlow\, high-speed data layers\, and GPU-powered server s to build self-service\, multi-user machine learning platforms. We’ll a lso demonstrate how to pool GPUs to maximize utilization and increase scal ability\, use RAPIDS for 10x faster data processing\, and integrate GPUs i nto the rest of the machine learning stack.\n \nIf you haven’t registere d for GTC DC yet\, you can do so here .\n \n DTEND;TZID="Pacific Standard Time":20191105T142500 DTSTAMP:20191004T171845Z DTSTART;TZID="Pacific Standard Time":20191105T140000 LAST-MODIFIED:20191004T171845Z LOCATION:1300 Pennsylvania Ave NW\, Washington\, D.C. 20004 | Atrium Ballro om B PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:GPU and AI as a Service: Driving Productivity and In creasing Utilization TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000803958E19C7AD501000000000000000 0100000004708C0869FAA164BB269C1EC4A781046 X-ALT-DESC;FMTTYPE=text/html:
< p class=MsoNormal>We’\;ll demonstrate how t o use GPUs and AI to build machine learning applications more easily. It&# 8217\;s not the data science that’\;s hard\, but all the operations a round it: deploying tools\, integrating hardware\, creating data and machi ne learning frameworks\, running jobs at scale\, and reproducing results. GPUs accelerate performance but pose problems such as resource sharing\, s oftware dependencies\, and data bottlenecks. In a cloud-native era\, data scientists want a GPU-powered\, open source machine learning platform as a service such as AWS Sagemaker or Google AI\, wi thout vendor lock-ins or on-premises software. We’\;ll show how to in tegrate Kubernetes\, KubeFlow\, high-speed data layers\, and GPU-powered servers to build self-service\, multi-user machin e learning platforms. We’\;ll also demonstrate how to pool GPUs to ma ximize utilization and increase scalability\, use RAPIDS for 10x faster da ta processing\, and integrate GPUs into the rest of the machine learning s tack.

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If you haven’\;t registered for GTC DC yet\, you can do so here.

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