We think the best way to learn is by doing. And to help you get started, we have assembled a series of tutorials and instructional materials featuring the latest developer innovations. Find the details below.
In this webinar, we will cover the steps to perform inference on a pretrained network with DriveWorks. We will first review DriveWorks basics before exploring the DriveWorks DNN APIs and tools to convert, optimize and run inference. Finally we will walk through sample code that demonstrates how to integrate your DNN into your software pipeline.
In this webinar we introduce CUDA cores, threads, blocks, gird, and stream and the TensorRT workflow. We also cover CUDA memory management and TensorRT optimization, and how you can deploy optimized deep learning networks using TensorRT samples on NVIDIA DRIVE AGX.
The second installment of this webinar series explains how to extend TensorRT with custom operations, running custom layers through TensorRT using the plugin interface. For the fastest implementation of custom layers, it is necessary to use the same GPU by building CUDA kernels on which the optimized engine will run. We then cover TensorRT plugins and how to adapt CUDA kernel as a part of the TensorRT plugin for DNN model optimization with a sample application.
Concurrent execution of multiple GPU inferencing tasks provides potential performance optimization when compared to its serialized counterpart. As a real-world use case, we implement a multi-network inference pipeline for object detection and lane segmentation. In building this application, we show how to achieve kernel concurrency using multiple CUDA Streams and CUDA Graphs. We then introduce how to use NVIDIA NSight Systems to profile the application, showing the performance gains from implementing concurrency.
This webinar covers the steps to develop camera image processing software on the DriveWorks SDK. Using this platform, developers can implement a range of capabilities seamlessly and with high performance. This webinar includes DriveWorks image basics, low-level Computer Vision modules, and Feature Tracking and DNN samples.
This webinar covers how to implement and use the sensor plugins for different sensor types such as radar, lidar, and camera. Such plugins will make it possible for developers to bring new sensors into the DriveWorks SAL and to implement the transport and protocol layers necessary to communicate with the sensor.
Peek under the hood of NVIDIA DRIVE Software with our latest video series.
Getting Started with DRIVE™ AGX Development Platform
Go to NVIDIA DRIVE™ Downloads Page to download SDK Manager, to get set up with development workstation, to flash your Developer Kit with latest Software and to start developing software.
Interact from your host computer with the target device via minicom. This is particularly necessary if you do not have a screen attached to the DRIVE™ platform.
Log into your target platform via SSH.
Learn how to use x11vnc, xrdp and rdesktop as a best practice to remotely develop on your DRIVE platform.
Starting out with DriveWorks you will learn how to get your target information such as available GPUs and the idiomatic way of using our Logger instead of the standard output stream.
In this webinar recording, we cover how to implement and use the sensor plugins for different sensor types such as radar, lidar, and camera. Such plugins will make it possible for developers to bring new sensors into the DriveWorks SAL and to implement the transport and protocol layers necessary to communicate with the sensor.
In this Tutorial, we will write a CAN communication module using NVIDIA® DriveWorks Sensor abstraction layer (SAL) on the NVIDIA® DRIVE AGX system. We will develop a module that will send random CAN message to a CAN socket.
Deep Learning Institute (DLI) Workshops
In this workshop, you’ll learn how to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE™ AGX Development platform. Learn how to:
- Integrate sensor input using the DriveWorks software stack
- Train a semantic segmentation neural network
- Optimize, validate, and deploy a trained neural network using TensorRT
Upon completion, students will be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE™.
- Prerequisites: Experience with CNNs
- Frameworks: TensorFlow, DIGITS, TensorRT
- Languages: English, Chinese, Japanese