Difference between revisions of "Jetson Nano"

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Useful for deploying computer vision and deep learning, Jetson Nano runs Linux and provides 472 GFLOPS of FP16 compute performance with 5-10W of power consumption.
 
Useful for deploying computer vision and deep learning, Jetson Nano runs Linux and provides 472 GFLOPS of FP16 compute performance with 5-10W of power consumption.
  
Jetson Nano is currently available as the '''[https://developer.nvidia.com/embedded/buy/jetson-nano-devkit Jetson Nano Developer Kit]''' for $99, with the production compute module coming in June 2019.  See the wiki of the other Jetson's '''[[Jetson|here]]'''.
+
Jetson Nano is currently available as the '''[https://developer.nvidia.com/embedded/buy/jetson-nano-devkit Jetson Nano Developer Kit]''' for $99, and the production '''[https://developer.nvidia.com/embedded/jetson-nano compute module]'''.  See the wiki of the other Jetson's '''[[Jetson|here]]'''.
  
 
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The [https://developer.nvidia.com/embedded/buy/jetson-nano-devkit Jetson Nano Developer Kit] is an easy way to get started using Jetson Nano, including the module, carrier board, and software.  It costs $99 and is available from distributors worldwide.
 
The [https://developer.nvidia.com/embedded/buy/jetson-nano-devkit Jetson Nano Developer Kit] is an easy way to get started using Jetson Nano, including the module, carrier board, and software.  It costs $99 and is available from distributors worldwide.
  
[[File:Jetson_Nano_Developer_Kit.png|450px|right]]
+
[[File:Jetson_Nano_Developer_Kit_P3449_B01.jpg|450px|right]]
  
 
=== What's Included ===
 
=== What's Included ===
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=== Availability ===
 
=== Availability ===
  
The devkit is available for $99 from the [https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/ NVIDIA webstore] and global distributors, including:
+
The Nano devkit and commercial module are available from distributors worldwide. Visit the '''[https://developer.nvidia.com/buy-jetson?product=jetson_nano&location=US Region Selector]''' webpage to find ordering information in your area.
 
 
<div style="width:25%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
 
* [https://www.amazon.com/NVIDIA-Jetson-Nano-Developer-Kit/dp/B07PZHBDKT/ Amazon]
 
* [https://www.arrow.com/en/products/945-13450-0000-000/nvidia Arrow]
 
* [https://www.newegg.com/Product/Product.aspx?Item=N82E16813190009 Newegg]
 
* [https://www.seeedstudio.com/NVIDIA-Jetson-Nano-Development-Kit-p-2916.html Seeed Studio]
 
* [https://www.siliconhighwaydirect.co.uk/ProductDetails.asp?ProductCode=945-13450-0000-000 Silicon Highway]
 
* [https://www.sparkfun.com/products/15297 SparkFun]
 
</div>
 
 
 
For the full list, refer to the [https://developer.nvidia.com/buy-jetson?product=jetson_nano&location=US Region Selector].
 
  
 
= Software Support =  
 
= Software Support =  
<div style="width:50%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
+
<div style="width:40%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
 
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]
 
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]
 
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)
 
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)
 
* Linux kernel 4.9
 
* Linux kernel 4.9
* Ubuntu 18.04 LTS aarch64
+
* Ubuntu 18.04 aarch64
* CUDA Toolkit 10.0.326
+
* [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] 10.0.326
* cuDNN 7.5.0
+
* [https://developer.nvidia.com/cudnn cuDNN] 7.5.0
 
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6
 
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6
* TensorFlow 1.31.1
+
* [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html TensorFlow] 1.14.0
 
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6
 
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6
 
* OpenCV 3.3.1
 
* OpenCV 3.3.1
* OpenGL 4.6  
+
* OpenGL 4.6 / OpenGL ES 3.2.5
* OpenGL ES 3.2
+
* Vulkan 1.1.1
* EGL 1.5
+
* [http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-32-1 L4T Multimedia API] (Argus 0.97)
* Vulkan 1.1
 
 
* GStreamer 1.14.1
 
* GStreamer 1.14.1
 
* V4L2 media controller support
 
* V4L2 media controller support
 +
* [https://developer.nvidia.com/nsight-systems NVIDIA Nsight Systems] 2019.4
 +
* [https://developer.nvidia.com/nsight-graphics NVIDIA Nsight Graphics] 2019.2
 +
* [https://developer.nvidia.com/nsight-compute NVIDIA Nsight Compute] 1.0
 
</div>
 
</div>
 +
 +
See the '''[[Jetson Zoo]]''' for more software packages to install on top of JetPack.
  
 
= Guides and Tutorials =
 
= Guides and Tutorials =
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<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
 
<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://docs.nvidia.com/jetson/l4t/index.html L4T Kernel Development Guide]
 +
* [[Jetson/Clone|Clone & Restore]]
 
* [https://github.com/jtagxhub/jetpack-agx-build Jetson Nano Build Assistant Scripts]
 
* [https://github.com/jtagxhub/jetpack-agx-build Jetson Nano Build Assistant Scripts]
 +
* [[Jetson/FAQ/BSP|BSP FAQ]]
 
* [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ Power Supply Considerations]
 
* [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]]
 
* [[Jetson/Nano/Upstream|Upstream Development Guide]]
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=== Deep Learning ===
 
=== Deep Learning ===
 +
See the '''[[Jetson Zoo]]''' for more resources on deploying AI and deep learning.
 +
 
* [https://github.com/dusty-nv/jetson-inference Hello AI World] (jetson-inference)
 
* [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://developer.nvidia.com/embedded/downloads#?search=TensorFlow TensorFlow 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/1049071/jetson-nano/pytorch-for-jetson-nano/ PyTorch 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/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/1065203/jetson-nano/paddlepaddle-for-jetson-nano-version-1-5-2-now-available/ PaddlePaddle Installer] (pip wheel)
 
* [https://devtalk.nvidia.com/default/topic/1050377/jetson-nano/deep-learning-inference-benchmarking-instructions/ Deep Learning Inference Benchmarking Instructions]
 
* [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://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=GstInference RidgeRun's GstInference]
 
* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]
 
* [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 ===
 
=== Robotics ===
 +
* [https://developer.nvidia.com/isaac-sdk NVIDIA Isaac] (robotics SDK + simulator)
 +
* [https://docs.nvidia.com/isaac/isaac/doc/tutorials/assemble_kaya.html NVIDIA Kaya] (Isaac reference robot)
 
* [https://github.com/NVIDIA-AI-IOT/jetbot NVIDIA JetBot] (AI-powered robotics kit)
 
* [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)
 
* [https://github.com/dusty-nv/jetbot_ros jetbot_ros] (ROS nodes for JetBot)
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==== Cameras ====
 
==== Cameras ====
 +
* Allied Vision MIPI CSI-2 (one open-source CSI-2 driver for all cameras on [https://github.com/alliedvision/linux_nvidia_jetson Github.com])
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-050.html Alvium 1500 C-050] 0.5MP PYTHON 480
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-120.html Alvium 1500 C-120] 1.2MP AR0135CS
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-210.html Alvium 1500 C-210] 2.1MP AR0521
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-500.html Alvium 1500 C-500] 5MP AR0521
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-040.html Alvium 1800 C-040] 0.4MP Sony IMX287
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-158.html Alvium 1800 C-158] 1.6MP Sony IMX273
 +
* Allied Vision USB3 Vision
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-040.html Alvium 1800 U-040] 0.4MP Sony IMX287
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-050.html Alvium 1800 U-050] 0.5MP PYTHON 480
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-120.html Alvium 1800 U-120] 1.2MP AR0135CS
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-158.html Alvium 1800 U-158] 1.6MP Sony IMX273
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-500.html Alvium 1800 U-500] 5MP AR0521
 +
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-501m%20NIR.html Alvium 1800 U-501m NIR] 5MP AR0522
 +
* DCDZ(冬虫电子) [https://item.taobao.com/item.htm?ft=t&id=618819673262 H2C-NCB] (HDMI to CSI2 camera board)
 +
* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-nano-cameras/5mp-mipi-nano-camera.asp e-CAM50_CUNANO] (5MP AR0521 MIPI Camera)
 
* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-nano-cameras/3mp-mipi-camera.asp e-CAM30_CUNANO] (3.4 MP MIPI Camera)
 
* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-nano-cameras/3mp-mipi-camera.asp e-CAM30_CUNANO] (3.4 MP MIPI Camera)
 +
* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-agx-xavier-cameras/stereo-camera.asp STEEReoCAM™] (2.0 MP MIPI Stereo Camera)
 
* Logitech [https://www.logitech.com/en-us/product/hd-webcam-c270 C270] (USB webcam)
 
* Logitech [https://www.logitech.com/en-us/product/hd-webcam-c270 C270] (USB webcam)
 
* Logitech [https://www.amazon.com/Logitech-Widescreen-Calling-Recording-Desktop/dp/B006JH8T3S C920] (USB webcam)
 
* Logitech [https://www.amazon.com/Logitech-Widescreen-Calling-Recording-Desktop/dp/B006JH8T3S C920] (USB webcam)
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* Auvidea [https://auvidea.eu/product/70780/ JN30] (module carrier)
 
* Auvidea [https://auvidea.eu/product/70780/ JN30] (module carrier)
 
* Auvidea [https://auvidea.eu/product/70781/ JN30-LC] (module carrier)
 
* Auvidea [https://auvidea.eu/product/70781/ JN30-LC] (module carrier)
 +
* ConnectTech [http://connecttech.com/product/photon-jetson-nano-ai-camera-platform/ Photon] (module carrier)
 +
* DCDZ(冬虫电子) [[Jetson_Nano/ncb00 | NCB00]] (module carrier)
 +
* Leopard Imaging [https://leopardimaging.com/product/li-nano-cb/ LI-NANO-CB] (module carrier)
 +
* Realtimes [http://www.realtimes.cn/cn/product/rtso-6001.html RTSO-6001] (module carrier)
 +
* SmartCow [https://smartcow.ai/en/products/pluto/ Pluto] (module carrier)
  
 
==== Enclosures ====
 
==== Enclosures ====
* ConnectTech [http://connecttech.com/products/nvidia-jetson-nano/ Nano-Pac] (3D-printable enclosure)
+
3D Printable
* Jetson [https://cults3d.com/en/3d-model/tool/jetson-nano-case Nano Case] (3D-printable enclosure)
+
* ConnectTech [http://connecttech.com/products/nvidia-jetson-nano/ Nano-Pac]
* Jetson [https://www.thingiverse.com/thing:3532828 NanoMesh] (3D-printable enclosure)
+
* Jetson [https://cults3d.com/en/3d-model/tool/jetson-nano-case Nano Case]
* Jetson [https://www.thingiverse.com/thing:3547555 NanoMesh Mini] (3D-printable enclosure)
+
* Jetson [https://www.thingiverse.com/thing:3532828 NanoMesh]
* [https://github.com/57Bravo/jetson_nano_enc jetson_nano_enc] (3D-printable enclosure)
+
* Jetson [https://www.thingiverse.com/thing:3547555 NanoMesh Mini]
* [https://github.com/dudasdavid/Jetson-nano-case Jetson-nano-case] (3D-printable enclosure)
+
* [https://github.com/57Bravo/jetson_nano_enc jetson_nano_enc]
* [https://www.amazon.com/Geekworm-NVIDIA-Enclosure-Control-Developer/dp/B07RRRX121 Geekworm Jetson Nano Case] (metal enclosure)
+
* [https://github.com/dudasdavid/Jetson-nano-case Jetson-nano-case]
* [https://www.kksb-cases.us/collections/nvidia/products/kksb-jetson-nano-case-black# KKSB Jetson Nano Case] (metal enclosure)
+
* [https://www.picocluster.com/collections/jeston-nano PicoCluster]
* [https://www.iotamy.com/Jetson-Nano-IP67-Fanless-Aluminium-Enclosure IP67 Fanless Aluminum Case] (metal enclosure)
+
 
 +
Metal
 +
* Geekworm [https://www.amazon.com/Geekworm-NVIDIA-Enclosure-Control-Developer/dp/B07RRRX121 Jetson Nano Case]
 +
* GeeekPi [https://www.amazon.com/GeeekPi-NVIDIA-Cooling-Control-Developer/dp/B07VVJNXMB/ Jetson Nano Case]
 +
* KKSB [https://www.kksb-cases.us/collections/nvidia/products/kksb-jetson-nano-case-black# Jetson Nano Case]
 +
* Waveshare [https://www.amazon.com/Case-Jetson-Nano-Compatible-Peripherals/dp/B07VTNSS4S Jetson Nano Case]
 +
 
 +
Metal (Fanless)
 +
* AAEON [https://www.aaeon.com/en/p/edge-ai-box-pc-nvidia-jetson-nano-boxer-8220ai?dl=image BOXER-8820AI]
 +
* IoTamy [https://www.iotamy.com/Jetson-Nano-IP67-Fanless-Aluminium-Enclosure IP67 Fanless Aluminum Case]
 +
* MiiVii [https://en.miivii.com/index.php?s=index/category/index&id=102 Nano Lite]
 +
* RapidProto [https://www.hazcam.io/collections/hazcam-products Hazcam]
  
 
==== Power Supplies ====
 
==== Power Supplies ====
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* Adafruit [https://www.adafruit.com/product/1995 GEO151UB] (5V⎓2.5A MicroUSB adapter)
 
* Adafruit [https://www.adafruit.com/product/1995 GEO151UB] (5V⎓2.5A MicroUSB adapter)
 
* Adafruit [https://www.adafruit.com/product/1466 GEO241DA-0540] (5V⎓4A DC barrel jack adapter)
 
* Adafruit [https://www.adafruit.com/product/1466 GEO241DA-0540] (5V⎓4A DC barrel jack adapter)
 +
* Geekworm [https://www.amazon.com/dp/B07413Q5Y4 5V⎓4A DC barrel jack adapter]
 
* GeekPi [https://www.amazon.com/dp/B07CYZ9GZZ ABT025050] (5V⎓2.5A MicroUSB Adapter with ON/OFF Switch)
 
* GeekPi [https://www.amazon.com/dp/B07CYZ9GZZ ABT025050] (5V⎓2.5A MicroUSB Adapter with ON/OFF Switch)
 
* Pwr+ [https://www.amazon.com/dp/B00L88M8TE PWR-TA05035N] (5V⎓3.5A MicroUSB AC Adapter)
 
* Pwr+ [https://www.amazon.com/dp/B00L88M8TE PWR-TA05035N] (5V⎓3.5A MicroUSB AC Adapter)
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==== Storage ====
 
==== Storage ====
* Geekworm [https://geekworm.com/products/nvidia-jetson-nano-25-inch-sata-ssd-hdd-shield SATA SSD/HDD Shield] (USB3 SATA shield)
+
* Geekworm [https://www.amazon.com/dp/B07T9FQ293 SATA SSD/HDD Shield] (USB3 SATA shield)
 +
* Geekworm [https://www.amazon.com/dp/B07TYKM7TCSATA NVMe SSD Shield] (USB3 SATA shield)
  
 
==== Other ====
 
==== Other ====

Revision as of 09:21, 22 June 2020

NVIDIA Jetson Nano is an embedded system-on-module (SoM) and developer kit from the NVIDIA Jetson family, including an integrated 128-core Maxwell GPU, quad-core ARM A57 64-bit CPU, 4GB LPDDR4 memory, along with support for MIPI CSI-2 and PCIe Gen2 high-speed I/O.

Useful for deploying computer vision and deep learning, Jetson Nano runs Linux and provides 472 GFLOPS of FP16 compute performance with 5-10W of power consumption.

Jetson Nano is currently available as the Jetson Nano Developer Kit for $99, and the production compute module. See the wiki of the other Jetson's here.

  Technical BlogNVIDIA Jetson Nano Brings AI to Everyone


Jetson Nano Family.png

Jetson Nano Developer Kit

The Jetson Nano Developer Kit is an easy way to get started using Jetson Nano, including the module, carrier board, and software. It costs $99 and is available from distributors worldwide.

Jetson Nano Developer Kit P3449 B01.jpg

What's Included

  • 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)

What You Will Need

Ports & Interfaces

  • 4x USB 3.0 A (Host)
  • USB 2.0 Micro B (Device)
  • MIPI CSI-2 x2 (15-position Camera Flex Connector)
  • HDMI 2.0
  • DisplayPort
  • Gigabit Ethernet (RJ45)
  • M.2 Key-E with PCIe x1
  • MicroSD card slot
  • (3x) I2C, (2x) SPI, UART, I2S, GPIOs

Getting Started

Availability

The Nano devkit and commercial module are available from distributors worldwide. Visit the Region Selector webpage to find ordering information in your area.

Software Support

See the Jetson Zoo for more software packages to install on top of JetPack.

Guides and Tutorials

This section contains recipes for following along on Jetson Nano.

System Tools

Deep Learning

See the Jetson Zoo for more resources on deploying AI and deep learning.

Robotics

Multimedia

V4L2 drivers for cameras

Design FAQs

There are some useful FAQs for Jetson Nano design, link is here.

Ecosystem Products and Sensors

The following are 3rd-party accessories, peripherals, and cameras available for Jetson Nano.

Cameras

Carriers

Enclosures

3D Printable

Metal

Metal (Fanless)

Power Supplies

See the Power Supply section and this forum post for more information about selecting proper power adapters.

Battery Packs

  • INUI 10000mAh (dual 5V⎓3A Micro-USB)
  • Krisdonia 25000mAh (5V⎓3A Micro-USB / DC barrel jack)

Wireless

Storage

Other

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

Getting Help

If you have a technical question or bug report, please visit the Jetson Nano Developer Forum and search or start a new topic.

See the official Support page on Embedded Developer Zone for warranty and RMA information.

For NVIDIA webstore Customer Service, please see the My Account page or contact 1-800-797-6530.