EZ-0075: Difference between revisions
Line 17: | Line 17: | ||
==Features== | ==Features== | ||
[[File:EZ 0075 11.jpg|right|300px]] | |||
{| class="wikitable" style="text-align: center;" | {| class="wikitable" style="text-align: center;" | ||
|- | |- |
Revision as of 16:50, 27 May 2019
NVIDIA® Jetson Nano™ Developer Kit
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
Document Links
- 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
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
- L4T Kernel Development Guide
- Power Supply Considerations
- CUDA and VisionWorks Samples
- Preliminary 3D CAD Model
- Mounting a SWAP File
- GPIO Header Pin-out
- Reading Serial Number
- jetson_easy - automatic setup/scripting
- jetson_stats - jtop, service and other tools
Computer Vision
- RidgeRun's GstInterpipe (GStreamer plug-in for communication between two or more independent pipelines)
- RidgeRun's GstRRWebRTC (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)
- RidgeRun's GstRTSPSink (GStreamer element for high performance streaming to multiple computers using the RTSP/RTP protocols)
- RidgeRun's Gstreamer Daemon - GstD (GStreamer framework for controlling audio and video streaming using TCP connection messages)
- RidgeRun's GstCUDA (RidgeRun CUDA ZeroCopy for GStreamer)
- RidgerRun's GstPTZR (GStreamer Pan Tilt Zoom and Rotate Element)
- RidgeRun's GstColorTransfer (GStreamer plug-in that transfers the color scheme from a reference to a target image)
Deep Learning
- Hello AI World (jetson-inference)
- TensorFlow 1.13.1 Installer (pip wheel)
- PyTorch 1.1 Installer (pip wheel)
- MXNet 1.4 Installer (pip wheel)
- Deep Learning Inference Benchmarking Instructions
- TensorFlow Object Detection With TensorRT (TF-TRT)
- RidgeRun's GstInference
- RidgeRun's R2Inference
See the NVIDIA AI-IoT GitHub for other coding resources on deploying AI and deep learning.
Robotics
- NVIDIA JetBot (AI-powered robotics kit)
- jetbot_ros (ROS nodes for JetBot)
- ROS Melodic (ROS install guide)
- ros_deep_learning (jetson-inference nodes)
See the Jetson Nano Supported Components List for devices that have been qualified by NVIDIA to work with Jetson Nano.