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GTC ON-DEMAND

Presentation
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Abstract:
A common deep learning workload is batch processing of videos to identify objects an image. We'll show examples of how to deploy a style-transfer and object-detection model on a cluster of V100 GPUs using Dask. Dask allows us develop the logic of our processing pipeline locally and deploy it on a cluster without having to rewrite anything. We'll discuss how we integrate it into Azure ML pipelines, as well as how to deploy it on a Kubernetes cluster for a scalable solution.
A common deep learning workload is batch processing of videos to identify objects an image. We'll show examples of how to deploy a style-transfer and object-detection model on a cluster of V100 GPUs using Dask. Dask allows us develop the logic of our processing pipeline locally and deploy it on a cluster without having to rewrite anything. We'll discuss how we integrate it into Azure ML pipelines, as well as how to deploy it on a Kubernetes cluster for a scalable solution.  Back
 
Topics:
AI Application, Deployment & Inference, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9198
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