running
sudo apt-get install nvidia-375
and then rebooting, from the instructions on getting started here
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
# Deploying Deep Learning
Welcome to our instructional guide for inference and realtime [DNN vision](#api-reference) library for NVIDIA **[Jetson Nano/TX1/TX2/Xavier NX/AGX Xavier/AGX Orin](http://www.nvidia.com/object/embedded-systems.html)**.
This repo uses NVIDIA **[TensorRT](https://developer.nvidia.com/tensorrt)** for efficiently deploying neural networks onto the embedded Jetson platform, improving performance and power efficiency using graph optimizations, kernel fusion, and FP16/INT8 precision.
Vision primitives, such as [`imageNet`](docs/imagenet-console-2.md) for image recognition, [`detectNet`](docs/detectnet-console-2.md) for object detection, [`segNet`](docs/segnet-console-2.md) for semantic segmentation, and [`poseNet`](docs/posenet.md) for pose estimation inherit from the shared [`tensorNet`](c/tensorNet.h) object. Examples are provided for streaming from live camera feed and processing images. See the **[API Reference](#api-reference)** section for detailed reference documentation of the C++ and Python libraries.
<img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/deep-vision-primitives.jpg">
Follow the [Hello AI World](#hello-ai-world) tutorial for running inference and transfer learning onboard your Jetson, including collecting your own datasets and training your own models. It covers image classification, object detection, semantic segmentation, pose estimation, and mono depth.
### Table of Contents
* [Hello AI World](#hello-ai-world)
* [Video Walkthroughs](#video-walkthroughs)
* [API Reference](#api-reference)
* [Code Examples](#code-examples)
* [Pre-Trained Models](#pre-trained-models)
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I get nothing with
lsmod | grep nvidia
I’m running a native ubuntu 14.04 LTS on a Samsung laptop. Any issues why this might be occurring?
I’ve also purged old drivers and reinstalled, as recommended here
In this tutorial, You'll learn how to install latest Nvidia drivers on Ubuntu and other Linux distros in a few steps.
Est. reading time: 4 minutes
still no luck.
I think the issue is that my host computer does not have an NVIDIA video card.
lspci -vnn | grep -i VGA -A 12
00:02.0 VGA compatible controller [0300]: Intel Corporation Haswell-ULT Integrated Graphics Controller [8086:0a16] (rev 09) (prog-if 00 [VGA controller])
Subsystem: Samsung Electronics Co Ltd Device [144d:c10e]
Flags: bus master, fast devsel, latency 0, IRQ 45
Memory at f7800000 (64-bit, non-prefetchable)
Memory at e0000000 (64-bit, prefetchable)
I/O ports at f000
Expansion ROM at [disabled]
Capabilities:
Kernel driver in use: i915
I am wondering why it is recommended / needed for the host to have this capability as directed in the jetson instructions.
When developing for a Jetson you can build and run CUDA or other GPU programs directly on the Jetson. You can also build first and run on your host PC if you have the NVIDIA video driver on Linux. Cross-compile for a Jetson from a PC without an NVIDIA video card should work, so should building natively on a Jetson. Because JetPack can install CUDA software on both host and Jetson the requirement for this is mentioned…but this requirement is only if you want to run the CUDA software on the host.
Thanks. I’m getting the impression that a more powerful host machine to build and do training sets was the intention. I’ve decided to go down that route and get set up on a dedicated DIGITS machine.