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GTC On-Demand

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Abstract:
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deploy convolutional architectures for tracking plant stress levels using time-lapse vision models. We'll outline how we try to use best practices for edge-to-cloud AI and how we built a workflow to train models on EC2.P3 instances (NVIDIA Tesla V100 GPUs on AWS SageMaker). We'll also discuss how we optimize models for inference using TensorRT and subsequently deploy those models on NVIDIA Jetson TX2s for the biosphere.
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deploy convolutional architectures for tracking plant stress levels using time-lapse vision models. We'll outline how we try to use best practices for edge-to-cloud AI and how we built a workflow to train models on EC2.P3 instances (NVIDIA Tesla V100 GPUs on AWS SageMaker). We'll also discuss how we optimize models for inference using TensorRT and subsequently deploy those models on NVIDIA Jetson TX2s for the biosphere.  Back
 
Topics:
AI and DL Research, Intelligent Machines and IoT, AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9627
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