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GTC ON-DEMAND

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Abstract:

Do you have a GPU cluster or air-gapped environment that you are responsible for but don't have an HPC background?   NVIDIA DGX POD is a new way of thinking about AI infrastructure, combining DGX servers with networking and storage to accelerate AI workflow deployment and time to insight. We'll discuss lessons learned about building, deploying, and managing AI infrastructure at scale from design to deployment to management and monitoring.   We will show how the DGX Pod Management software (DeepOps) along with our storage partner reference-architectures can be used for the deployment and management of multi-node GPU clusters for Deep Learning and HPC environments, in an on-premise, optionally air-gapped datacenter. The modular nature of the software also allows experienced administrators to pick and choose items that may be useful, making the process compatible with their existing software or infrastructure.  

Do you have a GPU cluster or air-gapped environment that you are responsible for but don't have an HPC background?   NVIDIA DGX POD is a new way of thinking about AI infrastructure, combining DGX servers with networking and storage to accelerate AI workflow deployment and time to insight. We'll discuss lessons learned about building, deploying, and managing AI infrastructure at scale from design to deployment to management and monitoring.   We will show how the DGX Pod Management software (DeepOps) along with our storage partner reference-architectures can be used for the deployment and management of multi-node GPU clusters for Deep Learning and HPC environments, in an on-premise, optionally air-gapped datacenter. The modular nature of the software also allows experienced administrators to pick and choose items that may be useful, making the process compatible with their existing software or infrastructure.  

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Topics:
Data Center & Cloud Infrastructure, AI Application, Deployment & Inference
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9334
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Abstract:
Learn how to get started deploying AI-based video analytic solutions using the NVIDIA DeepStream SDK. We'll walk through steps required to set up an environment on Jetson and Tesla and discuss best practices for selecting cameras and guiding design for data center and edge computing. We'll also provide an example that uses samples in the SDK to create a concurrent neural network in a multi-camera tracking environment.
Learn how to get started deploying AI-based video analytic solutions using the NVIDIA DeepStream SDK. We'll walk through steps required to set up an environment on Jetson and Tesla and discuss best practices for selecting cameras and guiding design for data center and edge computing. We'll also provide an example that uses samples in the SDK to create a concurrent neural network in a multi-camera tracking environment.  Back
 
Topics:
Intelligent Video Analytics, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9545
Streaming:
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