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GTC On-Demand

AI Application Deployment and Inference
Presentation
Media
Accelerate TensorFlow Inference with New TensorRT Integration
TensorFlow is an open source software library for numerical computation using data flow graphs. NVIDIA TensorRT is an inference optimizer and runtime for runtime deployment. TensorRT provides optimizations for deep neural networks and uses reduced precision to increase throughput, reduce latency, while maintaining accuracy. Today we announced tighter integration in TensorFlow for TensorRT through with new TensorFlow APIs, sub-graph optimizations and INT8 calibration to automatically leverage Tensor Cores on Volta GPUs. TensorRT delivers 2.5x faster inference throughput compared to inference without TensorRT. In this session, NVIDIA developers will use an example based workflow to show how to use this new capability.
TensorFlow is an open source software library for numerical computation using data flow graphs. NVIDIA TensorRT is an inference optimizer and runtime for runtime deployment. TensorRT provides optimizations for deep neural networks and uses reduced precision to increase throughput, reduce latency, while maintaining accuracy. Today we announced tighter integration in TensorFlow for TensorRT through with new TensorFlow APIs, sub-graph optimizations and INT8 calibration to automatically leverage Tensor Cores on Volta GPUs. TensorRT delivers 2.5x faster inference throughput compared to inference without TensorRT. In this session, NVIDIA developers will use an example based workflow to show how to use this new capability.  Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S81009
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Deep Learning and AI
Presentation
Media
From Workstation to Embedded: Accelerated Deep Learning on NVIDIA Jetson™ TX1

Running deep learning inference tasks on embedded platforms often requires deployment of pretrained models. Finding the best hyper-parameters and training are usually performed on a workstation or large-scale system to obtain the best model. In this talk, we'll show through examples using frameworks how to train models on a workstation and deploy models on embedded platforms such as the NVIDIA® Jetson™ TX1 or NVIDIA Drive™ PX. We'll also show dedicated tools and how to monitor performance and debug issues on embedded platforms for easy demo setup. This talk will include a live demo session.

Running deep learning inference tasks on embedded platforms often requires deployment of pretrained models. Finding the best hyper-parameters and training are usually performed on a workstation or large-scale system to obtain the best model. In this talk, we'll show through examples using frameworks how to train models on a workstation and deploy models on embedded platforms such as the NVIDIA® Jetson™ TX1 or NVIDIA Drive™ PX. We'll also show dedicated tools and how to monitor performance and debug issues on embedded platforms for easy demo setup. This talk will include a live demo session.

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Keywords:
Deep Learning and AI, Embedded, Aerospace and Defense, GTC Silicon Valley 2016 - ID S6474
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