EZ-0075: Difference between revisions

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TO be continue...
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==Document Links==
==Document Links==
* Download Center:  [ https://developer.nvidia.com/embedded/downloads#?search=Jetson%20Nano ]  
* Download Center:  [ https://developer.nvidia.com/embedded/downloads#?search=Jetson%20Nano Download Center ]  
* Jetson Nano Developer Kit User Guide PDF file: [ File:Jetson Nano Developer Kit User Guide.pdf ]
* Jetson Nano Developer Kit User Guide PDF file: [ File:Jetson Nano Developer Kit User Guide.pdf ]
----
= Software Support =
<div style="width:50%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2]
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.1] (L4T)
* Linux kernel 4.9
* Ubuntu 18.04 LTS aarch64
* CUDA Toolkit 10.0
* cuDNN 7.3.1
* [https://developer.nvidia.com/tensorrt TensorRT] 5.0.6
* TensorFlow 1.31.1
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6
* OpenCV 3.3.1
* OpenGL 4.6
* OpenGL ES 3.2
* EGL 1.5
* Vulkan 1.1
* GStreamer 1.14.1
* V4L2 media controller support
</div>
= Guides and Tutorials =
This section contains recipes for following along on Jetson Nano.
=== System Tools ===
<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
* [https://docs.nvidia.com/jetson/l4t/index.html L4T Kernel Development Guide]
* [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ Power Supply Considerations]
* [[Jetson/Nano/Upstream|Upstream Development Guide]]
* [https://devtalk.nvidia.com/default/topic/1049811/jetson-nano/cuda-and-vision-works-demos/post/5328027/#5328027 CUDA and VisionWorks Samples]
* [https://devtalk.nvidia.com/default/topic/1048817/jetson-nano/3d-cad-step-model-for-jetson-nano/post/5325051/#5325051 Preliminary 3D CAD Model]
* [https://support.rackspace.com/how-to/create-a-linux-swap-file/ Mounting a SWAP File]
* [https://www.jetsonhacks.com/nvidia-jetson-nano-j41-header-pinout/ GPIO Header Pin-out]
* [https://devtalk.nvidia.com/default/topic/1050026/jetson-nano/read-serial-number-of-jetson-nano/post/5329191/#5329191 Reading Serial Number]
* [https://github.com/rbonghi/jetson_easy jetson_easy] - automatic setup/scripting
* [https://github.com/rbonghi/jetson_stats jetson_stats] - jtop, service and other tools
</div>
=== Computer Vision ===
* [https://developer.ridgerun.com/wiki/index.php?title=GstInterpipe RidgeRun's GstInterpipe] (GStreamer plug-in for communication between two or more independent pipelines)
* [https://developer.ridgerun.com/wiki/index.php?title=GstWebRTC RidgeRun's GstRRWebRTC] (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)
* [https://developer.ridgerun.com/wiki/index.php?title=GstRtspSink RidgeRun's GstRTSPSink] (GStreamer element for high performance streaming to multiple computers using the RTSP/RTP protocols)
* [https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Daemon RidgeRun's Gstreamer Daemon - GstD] (GStreamer framework for controlling audio and video streaming using TCP connection messages)
* [http://developer.ridgerun.com/wiki/index.php?title=GstCUDA RidgeRun's GstCUDA] (RidgeRun CUDA ZeroCopy for GStreamer)
* [https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Pan_Tilt_Zoom_and_Rotate_Element RidgerRun's GstPTZR] (GStreamer Pan Tilt Zoom and Rotate Element)
* [https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Color_Transfer RidgeRun's GstColorTransfer] (GStreamer plug-in that transfers the color scheme from a reference to a target image)
=== Deep Learning ===
* [https://github.com/dusty-nv/jetson-inference Hello AI World] (jetson-inference)
* [https://developer.nvidia.com/embedded/downloads#?search=TensorFlow TensorFlow 1.13.1 Installer] (pip wheel)
* [https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/ PyTorch 1.1 Installer] (pip wheel)
* [https://devtalk.nvidia.com/default/topic/1049293/jetson-nano/i-was-unable-to-compile-and-install-mxnet-on-the-jetson-nano-is-there-an-official-installation-tutorial-/post/5326170/#5326170 MXNet 1.4 Installer] (pip wheel)
* [https://devtalk.nvidia.com/default/topic/1050377/jetson-nano/deep-learning-inference-benchmarking-instructions/ Deep Learning Inference Benchmarking Instructions]
* [https://medium.com/swlh/how-to-run-tensorflow-object-detection-model-on-jetson-nano-8f8c6d4352e8 TensorFlow Object Detection With TensorRT] (TF-TRT)
* [https://developer.ridgerun.com/wiki/index.php?title=GstInference RidgeRun's GstInference]
* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]
See the [https://github.com/NVIDIA-AI-IOT/ NVIDIA AI-IoT GitHub] for other coding resources on deploying AI and deep learning.
=== Robotics ===
* [https://github.com/NVIDIA-AI-IOT/jetbot NVIDIA JetBot] (AI-powered robotics kit)
* [https://github.com/dusty-nv/jetbot_ros jetbot_ros] (ROS nodes for JetBot)
* [http://wiki.ros.org/melodic/Installation/Ubuntu ROS Melodic] (ROS install guide)
* [https://github.com/dusty-nv/ros_deep_learning ros_deep_learning] (jetson-inference nodes)
See the Jetson Nano '''[https://developer.nvidia.com/embedded/dlc/jetson-nano-supported-components-list Supported Components List]''' for devices that have been qualified by NVIDIA to work with Jetson Nano.

Revision as of 16:02, 24 May 2019

NVIDIA® Jetson Nano™ Developer Kit

Jetson Nano Family.png

Description

NVIDIA® Jetson Nano™ Developer Kit is a small,
powerful computer that lets you run run multiple neural networks in parallel for applications like image classification,
object detection, segmentation, and speech processing.
All in an easy-to-use platform that runs in as little as 5 watts.

It’s simpler than ever to get started!
Just insert a microSD card with the system image, boot the developer kit, and begin using the latest NVIDIA JetPack SDK.
JetPack is compatible with NVIDIA’s world-leading AI platform for training and deploying AI software.

The same JetPack SDK is used across the entire NVIDIA Jetson™ family of products.
Plus, it’s compatible with NVIDIA’s world-leading AI platform for training and deploying AI software,
reducing complexity and effort for developers.

Features

Features Details Note
GPU 128-core Maxwell
CPU Quad-core ARM A57 @ 1.43 GHz
Memory 4 GB 64-bit LPDDR4 25.6 GB/s
Storage microSD (not included)
Video Encode 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)
Video Decode 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265)
Camera 1x MIPI CSI-2 DPHY lanes
Connectivity Gigabit Ethernet, M.2 Key E
Display HDMI 2.0 and eDP 1.4
USB 4x USB 3.0, USB 2.0 Micro-B
Others GPIO, I2C, I2S, SPI, UART
Mechanical 100 mm x 80 mm x 29 mm

Package Include

  • 80x100mm Reference Carrier Board
  • Jetson Nano Module with passive heatsink
  • Pop-Up Stand
  • Getting Started Guide

(the complete devkit with module and heatsink weighs 138 grams)

Gallery

TO be continue...

Document Links

Software Support

  • JetPack 4.2
  • Linux4Tegra R32.1 (L4T)
  • Linux kernel 4.9
  • Ubuntu 18.04 LTS aarch64
  • CUDA Toolkit 10.0
  • cuDNN 7.3.1
  • TensorRT 5.0.6
  • TensorFlow 1.31.1
  • VisionWorks 1.6
  • OpenCV 3.3.1
  • OpenGL 4.6
  • OpenGL ES 3.2
  • EGL 1.5
  • Vulkan 1.1
  • GStreamer 1.14.1
  • V4L2 media controller support

Guides and Tutorials

This section contains recipes for following along on Jetson Nano.

System Tools

Computer Vision

Deep Learning

See the NVIDIA AI-IoT GitHub for other coding resources on deploying AI and deep learning.

Robotics

See the Jetson Nano Supported Components List for devices that have been qualified by NVIDIA to work with Jetson Nano.

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