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GTC On-Demand

Presentation
Media
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 and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9198
Streaming:
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Abstract:
One of the key advantages of the cloud is that it allows users to scale out compute resources as required. We'll examine distributed training on deep learning models, which is tricky because it requires understanding the frameworks used and the computing infrastructure. We'll discuss benchmarking carried out on the Azure platform on different GPU architectures and across a number of frameworks.
One of the key advantages of the cloud is that it allows users to scale out compute resources as required. We'll examine distributed training on deep learning models, which is tricky because it requires understanding the frameworks used and the computing infrastructure. We'll discuss benchmarking carried out on the Azure platform on different GPU architectures and across a number of frameworks.  Back
 
Topics:
Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9212
Streaming:
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