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Abstract:
Learn how to develop faster, scalable, and better GPU-Accelerated distributed inference on multi-node and multi-GPU cluster environments. In most of the benchmark cases, linear scalability for throughput performance is not guaranteed with increasing the number of GPUs and servers. We'll discuss present an efficient scale-out method for deploying deep learning-based object detection models on multi-node and multi-GPU clusters using Apache Hadoop and Spark. We'll explain how to deploy 120 deep learning models (YOLOv2) on our own video datasets with NVIDIA Tesla M60 GPU (30EA) Hadoop cluster. Our approach achieved about 20 percent faster inference throughput with super-linear scalability from one GPU server to 30 GPU cluster. This session will be a combination of lecture and videos about our GPU-Accelerated distributed inference platform for large-scale streaming data analytics.
Learn how to develop faster, scalable, and better GPU-Accelerated distributed inference on multi-node and multi-GPU cluster environments. In most of the benchmark cases, linear scalability for throughput performance is not guaranteed with increasing the number of GPUs and servers. We'll discuss present an efficient scale-out method for deploying deep learning-based object detection models on multi-node and multi-GPU clusters using Apache Hadoop and Spark. We'll explain how to deploy 120 deep learning models (YOLOv2) on our own video datasets with NVIDIA Tesla M60 GPU (30EA) Hadoop cluster. Our approach achieved about 20 percent faster inference throughput with super-linear scalability from one GPU server to 30 GPU cluster. This session will be a combination of lecture and videos about our GPU-Accelerated distributed inference platform for large-scale streaming data analytics.  Back
 
Topics:
AI Application Deployment and Inference, Intelligent Video Analytics, Video and Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9343
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