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GPU accelerated IoT workloads at the edge

%3CLINGO-SUB%20id%3D%22lingo-sub-887475%22%20slang%3D%22en-US%22%3EGPU%20accelerated%20IoT%20workloads%20at%20the%20edge%3C%2FLINGO-SUB%3E%3CLINGO-BODY%20id%3D%22lingo-body-887475%22%20slang%3D%22en-US%22%3EIn%20this%20session%2Fworkshop%2C%20we%20look%20at%20how%20to%20leverage%20GPU%20accelerated%20IoT%20Edge%20workloads%20on%20Nvidia%20Jetson%20devices.%20These%20small%20form-factor%20IoT%20devices%20come%20equipped%20with%20an%20onboard%20GPU%20to%20enable%20complex%20video%2C%20machine%20learning%2C%20and%20AI%20powered%20solutions.%20We%20show%20how%20we%20can%20build%20out%20containerized%20software%20packages%20as%20modules%20for%20these%20devices%20using%20Azure%20IoT%20Edge%20to%20produce%20configurations%20that%20can%20be%20deployed%20from%20Microsoft%20Azure%20to%20devices%20in%20the%20field%20using%20tooling%20in%20Visual%20Studio%20Code.%26nbsp%3BThis%20culminates%20with%20an%20intelligent%20edge%20hands-on%20lab%20that%20walks%20through%20the%20process%20of%20deploying%20an%20IoT%20Edge%20module%20to%20an%20Nvidia%20Jetson%20Nano%20device%20to%20allow%20for%20detection%20of%20objects%20in%20YouTube%20videos%2C%20RTSP%20streams%2C%20HoloLens%20Mixed%20Reality%20Capture%2C%20or%20an%20attached%20web%20cam.%20Attendees%20leave%20with%20the%20ability%20to%20develop%20and%20deploy%20intelligent%20video%20applications%20backed%20by%20Microsoft%20Azure%20IoT%20services%20to%20real-world%20devices.%3C%2FLINGO-BODY%3E%3CLINGO-LABS%20id%3D%22lingo-labs-887475%22%20slang%3D%22en-US%22%3E%3CLINGO-LABEL%3EAI%20Machine%20Learning%3C%2FLINGO-LABEL%3E%3CLINGO-LABEL%3EContainers%3C%2FLINGO-LABEL%3E%3CLINGO-LABEL%3EDeveloper%20Tools%3C%2FLINGO-LABEL%3E%3CLINGO-LABEL%3EInternet%20of%20Things%3C%2FLINGO-LABEL%3E%3CLINGO-LABEL%3EWRK3031%3C%2FLINGO-LABEL%3E%3C%2FLINGO-LABS%3E
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Community Manager
In this session/workshop, we look at how to leverage GPU accelerated IoT Edge workloads on NVIDIA Jetson devices. These small form-factor IoT devices come equipped with an onboard GPU to enable complex video, machine learning, and AI powered solutions. We show how we can build out containerized software packages as modules for these devices using Azure IoT Edge to produce configurations that can be deployed from Microsoft Azure to devices in the field using tooling in Visual Studio Code. This culminates with an intelligent edge hands-on lab that walks through the process of deploying an IoT Edge module to an NVIDA Jetson Nano device to allow for detection of objects in YouTube videos, RTSP streams, HoloLens Mixed Reality Capture, or an attached web cam. Attendees leave with the ability to develop and deploy intelligent video applications backed by Microsoft Azure IoT services to real-world devices.

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