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GTC On-Demand

AI Application Deployment and Inference
Presentation
Media
Deploying Deep Neural Networks as a Service Using TensorRT and NVIDIA-Docker
Learn how you can utilize TensorRT and NVIDIA Docker to quickly configure and deploy a GPU-accelerated inference server and start gaining insights from your trained deep neural network (DNN) models. TensorRT is a high-performance tool for low-latency, high-throughput DNN inference. The latest release of TensorRT introduces a novel, framework-agnostic network definition format called universal framework format, which allows TensorRT to support and optimize DNN models trained in multiple deep learning frameworks. We'll leverage the TensorRT Python API to create a lightweight Python Flask application capable of serving multiple DNN models trained using TensorFlow, PyTorch, and Caffe, and also discuss how to containerize this inference service using NVIDIA Docker for ease of deployment at scale. This session will consist of a lecture, live demos, and detailed instructions.
Learn how you can utilize TensorRT and NVIDIA Docker to quickly configure and deploy a GPU-accelerated inference server and start gaining insights from your trained deep neural network (DNN) models. TensorRT is a high-performance tool for low-latency, high-throughput DNN inference. The latest release of TensorRT introduces a novel, framework-agnostic network definition format called universal framework format, which allows TensorRT to support and optimize DNN models trained in multiple deep learning frameworks. We'll leverage the TensorRT Python API to create a lightweight Python Flask application capable of serving multiple DNN models trained using TensorFlow, PyTorch, and Caffe, and also discuss how to containerize this inference service using NVIDIA Docker for ease of deployment at scale. This session will consist of a lecture, live demos, and detailed instructions.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8495
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Leveraging GPUs for Bayesian Inference
We'll present results on speeding up Bayesian inference in NVIDIA DGX-1 server for medical diagnostics. Bayesian inference is an AI technique to reason under uncertainty that is computationally and data intensive. We'll discuss the implications for both inference and training of Bayesian networks.
We'll present results on speeding up Bayesian inference in NVIDIA DGX-1 server for medical diagnostics. Bayesian inference is an AI technique to reason under uncertainty that is computationally and data intensive. We'll discuss the implications for both inference and training of Bayesian networks.  Back
 
Keywords:
AI Application Deployment and Inference, Accelerated Analytics, GTC Silicon Valley 2018 - ID S8488
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AI and DL Research
Presentation
Media
Quick and Easy DL Workflow Proof of Concept
Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, AMI, and NGC on Tesla P100, Tesla V100, and NVIDIA DGX-1 servers. This tutorial will be a combination of lecture, live demos, and detailed instructions.
Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, AMI, and NGC on Tesla P100, Tesla V100, and NVIDIA DGX-1 servers. This tutorial will be a combination of lecture, live demos, and detailed instructions.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, GTC Silicon Valley 2018 - ID S8286
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