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https://elinux.org/index.php?title=Jetson_TX2&diff=506866
Jetson TX2
2019-12-16T15:16:07Z
<p>Mschenk: /* Cameras */</p>
<hr />
<div><br />
NVIDIA [https://developer.nvidia.com/embedded/buy/jetson-tx2-devkit Jetson TX2] is an embedded system-on-module (SoM) with dual-core NVIDIA Denver2 + quad-core ARM Cortex-A57, 8GB 128-bit LPDDR4 and integrated 256-core Pascal GPU.<br />
<br />
Useful for deploying computer vision and deep learning, Jetson TX2 runs Linux and provides greater than 1TFLOPS of FP16 compute performance in less than 7.5 watts of power.<br />
<br />
Jetson TX2 is available as the '''[[#Jetson TX2 Module|module]]''', '''[[#Jetson TX2 Developer Kit|developer kit]]''', and in compatible '''[[#Ecosystem Products|ecosystem]]''' products. See the wiki of other Jetson's '''[[Jetson|here]]''', including the latest [[Jetson AGX Xavier]].<br />
<br />
{| style="color: black; background-color: #ffffff; width: 600px;"<br />
|-<br />
| style="width: 50px; background-color: white;"|<br />
| style="width: 550px; background-color: #76b900;"|<br />
{{spaces|1}} <span style="font-family: Trebuchet MS; color: white;">'''Parallel ForAll''' — </span>''[https://devblogs.nvidia.com/parallelforall/jetson-tx2-delivers-twice-intelligence-edge/ <span style="font-family: Trebuchet MS; color:white;">NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge</span>]''<br />
|}<br />
<br /><br />
<br />
[[File:NVIDIA_Jetson_TX2_Module_Devkit.png|800px|right|text-bottom]]<br />
<br />
= Jetson TX2 Module =<br />
The Jetson TX2 module contains all the active processing components. The ports are broken out through a carrier board.<br /><br />
<br />
Below is a partial list of the module's features. Please see the [https://developer.nvidia.com/embedded/dlc/jetson-tx2-module-datasheet Jetson TX2 Module Datasheet] for the complete specifications.<br />
<br />
[[File:Tegra_Parker_Block_Diagram.png|700px|right]]<br />
<br />
=== Processing Components ===<br />
* dual-core NVIDIA Denver2 + quad-core ARM Cortex-A57<br />
* 256-core Pascal GPU<br />
* 8GB LPDDR4, 128-bit interface<br />
* 32GB eMMC<br />
* 4kp60 H.264/H.265 encoder & decoder<br />
* Dual ISPs (Image Signal Processors)<br />
* 1.4 gigapixel/sec MIPI CSI camera ingest<br />
[[File:NVIDIA_Jetson_TX2_Module_TTP.png|323px|right]]<br />
<br />
=== Ports & Peripherals ===<br />
* HDMI 2.0<br />
* 802.11a/b/g/n/ac 2×2 867Mbps WiFi<br />
* Bluetooth 4.1<br />
* USB3, USB2<br />
* 10/100/1000 BASE-T Ethernet<br />
* 12 lanes MIPI CSI 2.0, 2.5 Gb/sec per lane<br />
* PCIe gen 2.0, 1×4 + 1×1 or 2×1 + 1×2<br />
* SATA, SDcard<br />
* dual CAN bus<br />
* UART, SPI, I2C, I2S, GPIOs<br />
<br />
=== Form-Factor ===<br />
* 400-pin Samtec board-to-board connector<br />
* dimensions: 50x87mm {{spaces|1}} (1.96" x 3.42")<br />
* Thermal Transfer Plate (TTP), -25C to 80C operating temperature<br />
* mass: 85 grams, including TTP<br />
* 5.5-19.6VDC input power (consuming 7.5W under typical load)<br />
<br />
=== Software Support ===<br />
<div style="width:40%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]<br />
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)<br />
* Linux kernel 4.9<br />
* Ubuntu 18.04 aarch64<br />
* [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] 10.0.326<br />
* [https://developer.nvidia.com/cudnn cuDNN] 7.5.0<br />
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6<br />
* [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html TensorFlow] 1.14.0<br />
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6<br />
* OpenCV 3.3.1<br />
* OpenGL 4.6 / OpenGL ES 3.2.5<br />
* Vulkan 1.1.1<br />
* [http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-32-1 L4T Multimedia API] (Argus 0.97)<br />
* GStreamer 1.14.1<br />
* V4L2 media controller support<br />
* [https://developer.nvidia.com/nsight-systems NVIDIA Nsight Systems] 2019.4<br />
* [https://developer.nvidia.com/nsight-graphics NVIDIA Nsight Graphics] 2019.2<br />
* [https://developer.nvidia.com/nsight-compute NVIDIA Nsight Compute] 1.0<br />
</div><br />
<br />
See the '''[[Jetson Zoo]]''' for more software packages to install on top of JetPack.<br />
<br />
{| style="color: black; background-color: #ffffff; width: 575px;"<br />
|-<br />
| style="width: 1px; background-color: white;"|<br />
| style="width: 550px; background-color: #76b900;"|<br />
{{spaces|1}} <span style="font-family: Trebuchet MS; color: white;">'''Parallel ForAll''' — </span>''[https://devblogs.nvidia.com/parallelforall/jetpack-doubles-jetson-inference-perf/ <span style="font-family: Trebuchet MS; color:white;">JetPack 3.1 Doubles Jetson's Low-Latency Inference Performance</span>]''<br />
|}<br />
<br />
== Jetson TX2i Module ==<br />
<br />
[[File:Jetson TX2i Module and TTP 800px.png|600px]]<br />
<br />
There's an extended-temperature variant of the TX2 module available called [https://developer.nvidia.com/embedded/buy/jetson-tx2i '''Jetson TX2i'''] that's intended for industrial environments. It has the same processing capabilities as TX2, with a rugged design.<br />
<br />
For more info, see the FAQ [https://developer.nvidia.com/embedded/faq#jetson-differences-tx2i "What changes for industrial environments does Jetson TX2i have compared to Jetson TX2?"]<br />
<br />
<br /><br />
<br />
= Jetson TX2 Developer Kit =<br />
<br />
The [https://developer.nvidia.com/embedded/buy/jetson-tx2-devkit Jetson TX2 Developer Kit] bundles together all the parts to get started, including:<br />
<br />
[[File:NVIDIA_Jetson_TX2_Devkit_Unbox.png|550px|right]]<br />
=== What's Included ===<br />
* mini-ITX Reference carrier board<br />
* Jetson TX2 Module<br />
** fan and heatsink (pre-assembled)<br />
* 5MP CSI camera module (with Omnivision OV5693)<br />
* WiFi/BT antennas<br />
* USB OTG adapter<br />
* 19VDC Power brick<br />
* AC Power cable<br />
<br />
The design files for the reference carrier board and camera module are freely available for [[Jetson_TX2#Platform_Documentation|download]].<br />
<br />
=== Getting Started ===<br />
* Get the latest development software for PC and TX2 by using '''[https://developer.nvidia.com/embedded/jetpack JetPack]'''.<br /><br />
* Plug in an HDMI display into Jetson, attach the antennas and USB keyboard & mouse, and apply power to boot it up. {{spaces|0}} ('''[http://developer.nvidia.com/embedded/dlc/l4t-quick-start-guide-27-1 User Guide]''')<br /><br />
* Visit the '''[https://developer.nvidia.com/embedded-computing Embedded Developer Zone]''' and '''[https://devtalk.nvidia.com/default/board/188/jetson-tx2/ Jetson TX2 Developer Forum]''' to access the latest documentation & downloads.<br />
<br />
=== Availability ===<br />
<br />
* The devkit is available through NVIDIA's '''[https://developer.nvidia.com/embedded/buy/jetson-tx2-devkit Jetson TX2 Developer Kit]''' webpage.<br />
* The individual module is available through NVIDIA's '''[https://devtalk.nvidia.com/default/topic/1006734/jetson-tx2/jetson-tx2-module-available-now Jetson TX2 Module]''' webpage.<br />
* Alternatively, use the [http://www.nvidia.com/embedded Region Selector] to find distributors of the devkit in your region. <br /><br />
* There's also an '''[http://www.nvidia.com/object/jetsontx2-edu-discount.html Academic Discount]''' available for those affiliated with an educational organization.<br />
<br /><br />
<br />
= Platform Documentation =<br />
<br />
NVIDIA has [https://developer.nvidia.com/embedded-computing released] comprehensive documentation and reference designs for the Jetson TX2 module and devkit. <br /><br />
<br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx2-module-datasheet Module Datasheet]''' {{spaces|8}} the official module features, ports, signal pin-out, and package specifications. <br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx2-oem-product-design-guide Design Guide]''' {{spaces|16}} detailed technical design and layout information for creating OEM products. <br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-carrier-board-specification DevKit Carrier Spec]''' {{spaces|6}} design info about the reference carrier board from the devkit.<br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-carrier-board-design-files DevKit Design Files]''' {{spaces|6}} schematics, layout, and design files for the devkit reference carrier board.<br />
* '''[http://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-3D-cad-step-model DevKit CAD Models]''' {{spaces|6}} 3D STEP file for reference carrier board, heatsink, camera board, and module.<br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-camera-module-design-files Camera Design Files]''' {{spaces|4}} schematics, layout, and design files for the devkit MIPI CSI-2 camera module.<br />
* '''[http://developer.nvidia.com/embedded/dlc/jetson-tx2-thermal-design-guide Thermal Design Guide]''' {{spaces|1}} mechanical specifications for designing active and passive cooling solutions.<br />
* '''[http://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-interface-comparison-and-migration TX1/TX2 Migration]''' {{spaces|8}} guide to porting applications and hardware between Jetson TX1 and TX2<br />
* '''[http://developer.nvidia.com/embedded/dlc/http://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-module-battery-and-charger-design-guide Battery Charger Guide]''' {{spaces|1}} document for the design of battery charger<br />
* '''[https://developer.nvidia.com/embedded/dlc/parker-series-trm Tegra X2 (Parker) TRM]''' {{spaces|1}} Technical Reference Manual for NVIDIA TX2 system-on-chip and register data.<br />
* '''[https://developer.nvidia.com/embedded/dlc/l4t-documentation-28-2 L4T Kernel Docs]''' {{spaces|11}} documentation for L4T kernel developers (including V4L2/camera drivers).<br />
* '''[https://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-28-2 Multimedia API Reference]''' {{spaces|8}} documentation to Argus camera API and V4L2 media codecs<br />
* '''[https://developer.nvidia.com/embedded/dlc/l4t-accelerated-gstreamer-guide-28-2 Accelerated GStreamer Guide]''' {{spaces|1}} example gstreamer pipelines for accessing H.264/H.265 hardware video codec.<br />
<br />
Above is a partial list of documents.<br />
Please visit the '''[https://developer.nvidia.com/embedded/downloads#?tx=$product,jetson_tx2 Downloads Center]''' at Embedded Developer Zone for the full list that's currently available.<br /><br />
<br /><br />
<br />
= Guides and Tutorials =<br />
<br />
This section contains recipes for following along on Jetson.<br />
<br />
=== System Tools ===<br />
<br />
Please see [http://elinux.org/Jetson_TX1#System_Tools Jetson TX1 Wiki] for similar entries that also apply to TX2.<br />
<br />
<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
:* [[Jetson/Clone|Cloning & Restore]]<br />
:* [https://github.com/jtagxhub/jetpack-agx-build Jetson TX2 Build Assistant Scripts]<br />
:* [[Jetson/FAQ/BSP|BSP FAQ]]<br />
:* [https://developer.nvidia.com/embedded/downloads#?search=Factory%20Image Factory Images]<br />
:* [[Jetson/TX2_DTB|Setting the DTB]]<br />
:* [[Jetson/TX2_SPI|Enabling the SPI Port]]<br />
:* [http://www.jetsonhacks.com/2017/03/25/build-kernel-and-modules-nvidia-jetson-tx2/ Building Kernel and Modules]<br />
:* [[Jetson/TX2_USB|Enabling USB on Custom Carriers]]<br />
:* [[Jetson/TX2_eMMC|Maximizing RootFS Partition on eMMC]]<br />
:* [http://www.jetsonhacks.com/2017/03/25/nvpmodel-nvidia-jetson-tx2-development-kit/ nvpmodel] - dynamic performance profiles<br />
:* [https://gist.github.com/JasonAtNvidia/e03e6675849d1d4049b85ea41efb2171 TX2 GPU support in Docker] - script for GPU from within Docker<br />
:* [https://github.com/Technica-Corporation/Tegra-Docker Tegra-Docker]<br />
:* [[Jetson/TX2|Ubuntu Base]] Minimal footprint (500Mb with OS only) using [https://wiki.ubuntu.com/Base Ubuntu Base]<br />
:* [http://elinux.org/Boot_from_sd Boot from SD card]<br />
:* [[Jetson TX2/r28 Display debug|Display Driver Debugging]]<br />
:* [https://medium.com/@ynd/getting-swift-to-run-on-nvidia-jetson-tx2-ai-computing-platform-1d9bcd6559dc Getting Swift to Run on TX2]<br />
:* [https://github.com/rbonghi/jetson_easy jetson_easy] - automatic setup/scripting<br />
:* [https://github.com/rbonghi/jetson_stats jetson_stats] - jtop, service and other tools<br />
:* [https://sites.google.com/site/jetsontricks/ v4l2loopback,rtsp,screencapture,misc] <br />
:* [https://devtalk.nvidia.com/default/topic/1057158/jetson-tx2/guide-to-enabling-mcp251x-mcp2515-on-the-tx2-spi-can-/ Enabling MCP2515 SPI-CAN Device]<br />
</div><br />
<br />
=== Robotics ===<br />
<br />
:* [https://github.com/NVIDIA-Jetson NVIDIA Jetson GitHub] {{spaces|10}} (open-source robotics projects with deep learning)<br />
:* [https://github.com/NVIDIA-Jetson/redtail NVIDIA Redtail GitHub] {{spaces|9}} (end-to-end deep learning drone for ROS)<br />
:* [https://developer.nvidia.com/embedded/community/reference-platforms Jetson Reference Platforms] {{spaces|1}} (off-the-shelf robots with TX1/TX2)<br />
:* [[Jetson/FRC_Setup|FIRST FRC Configuration]] {{spaces|8}} (setup guide for FIRST Robotics)<br />
:* [https://www.chiefdelphi.com/media/papers/download/4758 FIRST FRC Neural Networks] (Zebracorns team #900 [https://www.chiefdelphi.com/media/papers/3274 object tracking])<br />
:* [https://www.chiefdelphi.com/media/papers/download/5169 ROS for FRC Whitepaper] {{spaces|5}} (Zebracorns team #900 Vision [https://github.com/FRC900/2017VisionCode GitHub])<br />
:* [http://www.jetsonhacks.com/2017/03/27/robot-operating-system-ros-nvidia-jetson-tx2/ Installing ROS Kinetic (TX2)] {{spaces|1}} (JetsonHacks guide)<br />
:* [https://github.com/mit-racecar/particle_filter Fast SLAM Particle Filter] {{spaces|6}} (Accelerated Localization using Raycasting)<br />
:* [http://ardupilot.org/dev/docs/companion-computer-nvidia-tx2.html Connecting Pixhawk and TX2] (Autopilot with MAVLink Interface)<br />
:* [https://github.com/DiegoHerrera1890/Pixhawk-connected-to-Jetson-Tx2-devkit Running MAVROS with TX2 and PixHawk 4] (TX2/ROS setup with MAVLink)<br />
<br />
=== Computer Vision ===<br />
<br />
:* NVIDIA [https://developer.nvidia.com/embedded/learn/tutorials#collapseOne OpenCV 101] - screencast tutorials<br />
:* [https://github.com/AastaNV/JEP/blob/master/script/install_opencv3.4.0.sh OpenCV-3.4.0 for TX2] building script<br />
:* [http://www.jetsonhacks.com/2017/04/05/build-opencv-nvidia-jetson-tx2/ Build OpenCV for TX2] (JetsonHacks)<br />
:* [http://docs.opencv.org/3.2.0/d6/d15/tutorial_building_tegra_cuda.html Building OpenCV 3.2 with CUDA for Tegra]<br />
:* [https://developer.nvidia.com/embedded/learn/tutorials#collapseVisionWorks VisionWorks training]<br />
<br />
=== Deep Learning ===<br />
<div style="width:80%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
:* [https://developer.nvidia.com/embedded/twodaystoademo NVIDIA Two Days to a Demo] {{spaces|1}} (DIGITS/TensorRT)<br />
:* Caffe {{spaces|1}} (BVLC [https://github.com/BVLC/caffe/wiki/Model-Zoo Model Zoo])<br />
:** [https://github.com/nvidia/caffe NVcaffe FP16] {{spaces|1}} ([https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-nvcaffe.md Install Guide])<br />
:** [http://www.jetsonhacks.com/2017/03/24/caffe-deep-learning-framework-nvidia-jetson-tx2/ Caffe Installation] {{spaces|1}} (JetsonHacks)<br />
:* Caffe2 {{spaces|1}} ([https://github.com/caffe2/caffe2 github.com/caffe2])<br />
:* [https://github.com/chitoku/installDeepvizJetson Deep Visualization Toolbox] install script<br />
:* [https://github.com/peterlee0127/tensorflow-tx2 TensorFlow] install for JetPack 3.1<br />
:* [http://www.jetsonhacks.com/2017/04/02/tensorflow-on-nvidia-jetson-tx2-development-kit/ TensorFlow] post for JetPack 3.0 {{spaces|1}} (JetsonHacks)<br />
:* [https://syed-ahmed.gitbooks.io/nvidia-jetson-tx2-recipes/content/first-question.html TensorFlow] install procedure {{spaces|1}} ([https://devtalk.nvidia.com/default/topic/1000717/jetson-tx2/tensorflow-on-jetson-tx2/post/5112792/#5112792 pip wheel])<br />
:* [https://developer.ridgerun.com/wiki/index.php?title=GstInference RidgeRun's GstInference]<br />
:* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]<br />
:* [https://github.com/AastaNV/JEP/tree/master/script/TensorFlow_1.6 TensorFlow] script and pip wheel for JetPack 3.2 DP<br />
:* [https://github.com/dusty-nv/jetson-reinforcement/blob/master/CMakePreBuild.sh Torch7] {{spaces|1}} install script<br />
:* [https://gist.github.com/dusty-nv/ef2b372301c00c0a9d3203e42fd83426 pyTorch] {{spaces|0}} install script<br />
:* [http://github.com/dusty-nv dusty-nv's Jetson GitHub] {{spaces|3}} [http://github.com/dusty-nv/jetson-inference jetson-inference] {{spaces|2}} [http://github.com/dusty-nv/jetson-inference jetson-reinforcement]<br />
:* [https://github.com/Abaco-Systems/jetson-inference-gv jetson-inference-gv] GigEVision / RTP streaming video (Ross Newman)<br />
:* [https://github.com/S4WRXTTCS/jetson-inference jetson-inference-cards] {{spaces|1}} (playing card recognition by S4WRXTTCS)<br />
:* [https://github.com/AastaNV/Face-Recognition face-recognition] {{spaces|0}} (face detection with TensorRT plugin API by AastaNV)<br />
:* [https://github.com/NVIDIA-Jetson/JEP_ChatBot ChatBot] {{spaces|2}} (TensorFlow→TensorRT inferencing workflow by AastaNV)<br />
:* [https://github.com/NVIDIA-Jetson NVIDIA GitHub] {{spaces|2}} (open-source robotics/DL projects)<br />
:* [https://github.com/NVIDIA-Jetson/redtail NVIDIA Redtail] {{spaces|2}} (end-to-end deep learning drone for ROS)<br />
:* Training a Fish Detector with DetectNet {{spaces|1}} [https://jkjung-avt.github.io/fisheries-dataset/ part 1] [https://jkjung-avt.github.io/detectnet-training/ part 2] {{spaces|1}} (jkjung)<br />
:* [https://heyjetson.com/ Hey, Jetson!] {{spaces|2}} (Automatic Speech Recognition using CNN/RNN)<br />
</div><br />
<br />
=== Multimedia ===<br />
* [https://developer.ridgerun.com/wiki/index.php?title=Xavier/GStreamer_Pipelines Gstreamer Pipelines for AGX Xavier]<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstInterpipe RidgeRun's GstInterpipe] (GStreamer plug-in for communication between two or more independent pipelines)<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstWebRTC RidgeRun's GstRRWebRTC] (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)<br />
* [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)<br />
* [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)<br />
* [http://developer.ridgerun.com/wiki/index.php?title=GstCUDA RidgeRun's GstCUDA] (RidgeRun CUDA ZeroCopy for GStreamer)<br />
* [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)<br />
* [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)<br />
<br />
=== Camera Info ===<br />
*USB3 - e-con Systems' [https://www.e-consystems.com/4k-usb-camera.asp See3CAM_CU135] was tested on Jetson TX2 with HD (1280X720) @ 46fps and FullHD (1920x1080) @ 36fps in MJPEG (compressed) format, as well as [https://elinux.org/Jetson/Cameras#USB_3.0_webcams_known_to_be_working other settings].<br />
*CSI-2 - [https://www.e-consystems.com/multiple-csi-cameras-for-nvidia-jetson-tx2.asp 6 MIPI CSI-2 Cameras] support for Jetson TX2 from e-con Systems<br />
*CSI-2 - [https://www.e-consystems.com/three-synchronized-4k-cameras-for-nvidia-jetson-tx2.asp 3 MIPI CSI-2 Cameras] support for Jetson TX2 from e-con Systems<br />
<br />
=== V4L2 drivers for cameras ===<br />
<br />
*RidgeRun has a [https://developer.ridgerun.com/wiki/index.php?title=V4L2_drivers_available_for_Jetson_SoCs list of drivers already supported in Jetson], please check if the driver that you need is already there. Otherwise, RidgeRun offers [https://developer.ridgerun.com/wiki/index.php?title=V4L2_driver_for_camera_sensor_or_capture_chip services to create the driver for you]<br />
<br />
=== Design FAQs ===<br />
<br />
There are some useful FAQs for Jetson TX2 design, link is [[Jetson_TX2/FAQ|here]].<br />
<br /><br />
<br /><br />
<br />
= Ecosystem Products =<br />
<br />
The following are 3rd-party carriers, enclosures, expansion boards, and accessories available for Jetson TX2. <br />
<br />
Please see additional backwards-compatible [[Jetson_TX1#Ecosystem_Products|Ecosystem Products for TX1]].<br />
<br /><br />
<br />
=== Cameras ===<br />
<br />
:* Allied Vision MIPI CSI-2 (one open-source CSI-2 driver for all cameras on [https://github.com/alliedvision/linux_nvidia_jetson Github.com])<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-050.html Alvium 1500 C-050] 0.5MP PYTHON 480<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-120.html Alvium 1500 C-120] 1.2MP AR0135CS<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-210.html Alvium 1500 C-210] 2.1MP AR0521<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-500.html Alvium 1500 C-500] 5MP AR0521<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-040.html Alvium 1800 C-040] 0.4MP Sony IMX287<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-158.html Alvium 1800 C-158] 1.6MP Sony IMX273<br />
:* Allied Vision USB3 Vision <br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-040.html Alvium 1800 U-040] 0.4MP Sony IMX287<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-050.html Alvium 1800 U-050] 0.5MP PYTHON 480<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-120.html Alvium 1800 U-120] 1.2MP AR0135CS<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-158.html Alvium 1800 U-158] 1.6MP Sony IMX273<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-500.html Alvium 1800 U-500] 5MP AR0521<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-501m%20NIR.html Alvium 1800 U-501m NIR] 5MP AR0522<br />
:* APPROPHO [http://www.appropho.com/products_en.html?type=36 TX1/TX2 Camera Solutions]<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-OV5640 ]] OV5640 M12 lens camera<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-OV5693 ]] OV5693 camera<br />
:* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-agx-xavier-cameras/stereo-camera.asp 3D MIPI Stereo camera for NVIDIA® Jetson AGX Xavier™/TX2]<br />
:* e-con Systems [https://www.e-consystems.com/13mp-autofocus-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 AF MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/3d-usb-stereo-camera-with-nvidia-accelerated-sdk.asp USB Stereo Camera for NVIDIA® Jetson AGX Xavier™/TX2] <br />
:* e-con Systems [https://www.e-consystems.com/2MP-HDR-Jetson-TX2-TX1-Camera-Board.asp 2MP AR0230AT MIPI Jetson TX1/TX2 Camera] <br />
:* e-con Systems [https://www.e-consystems.com/jetson-tx2-ultra-low-light-camera-board.asp 2MP SONY IMX290 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/13mp-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/autofocus-liquid-lens-nvidia-jetson-tx2-camera.asp 3.4 MP AF AR0330 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/13mp-nvidia-jetson-tx1-camera-board.asp 13MP AR1820 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/3MP-Jetson-TX1-Camera-board.asp 3.4 MP AR0330 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/gmsl-camera-for-nvidia-jetson-tx2.asp 3.4 MP AR0330 GMSL MIPI Jetson TX1/TX2 Camera]<br />
:* Leopard Imaging [https://leopardimaging.com/product-category/nvidia-jetson-cameras/nvidia-tx1tx2-mipi-camera-kits/csi-2-mipi-cameras/ TX1/TX2 camera kits]<br />
:* Stereolabs [https://www.stereolabs.com/ ZED] (stereo camera)<br />
<br />
=== Carriers === <br />
<br />
:* Aetina [http://www.aetina.com.tw/wp-content/uploads/2017/04/eDM_ACE-N620_carrier/index.html N620] nano-ITX carrier<br />
:* Auvidea [https://auvidea.com/j100/ J100] carrier<br />
:* Auvidea [https://auvidea.com/j106/ J106] carrier (6 camera)<br />
:* Auvidea [https://auvidea.com/j120/ J120] carrier<br />
:* Auvidea [https://auvidea.com/j130-with-4k-video-input/ J130] carrier (4K input)<br />
:* Auvidea [https://auvidea.com/j140-dual-gbe/ J140] dual-GbE carrier<br />
:* Auvidea [https://auvidea.com/j150/ J150] OpenGear blade<br />
:* Auvidea [http://www.auvidea.eu/index.php/2015-11-08-08-01-27/2016-02-03-12-30-02/j200-dual-jetson-tx1-carrier J200] carrier<br />
:* Avermedia [https://www.avermedia.com/professional/product/ex731_aa_n1/overview EX731-AA] carrier<br />
:* Avermedia [https://www.avermedia.com/professional/product/ex713_aa/overview EX713-AA] carrier<br />
:* Bluetechnix [https://www.bluetechnix.com/en/products/multi-tof-platform/product/multi-tof-platform/ Multi-ToF platform]<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG008.asp?l1=GPU&l2=ASG008 Sprocket] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG003.asp?l1=GPU&l2=ASG003 Orbitty] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG006.asp?l1=GPU&l2=ASG006 Spacely] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG007.asp?l1=GPU&l2=ASG007 Cogswell] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG002.asp?l1=GPU&l2=ASG002 Elroy] carrier<br />
:* ConnectTech [http://connecttech.com/sub/Products/VPG003_GraphiteVPXTX1.asp?l1=GPU&l2=VPXTX1 3U VPX] card<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-Lite carrier]] with eDP, CSI, DSI, MicroHDMI etc<br />
:* Gumstix [https://store.gumstix.com/aerocore2-for-nvidia-jetson.html Aerocore2] drone carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/9001.html RTSO-9001] carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/RTSO9002.html RTSO-9002] carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/rtso-9003.html RTSO-9003] carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/products-8-55.html RTSS-Z5O3U] enclosure<br />
<br />
=== Enclosures ===<br />
<br />
:* Aaeon [http://www.aaeon.com/en/p/fanless-embedded-computers-boxer-8120ai BOXER-8120AI] enclosure<br />
:* Abaco [https://www.abaco.com/products/gvc1000 GVC1000] enclosure<br />
:* ADLINK [https://www.adlinktech.com/Products/Deep_Learning_Accelerator_Platform_and_Server/Inference_Platform/DLAP-201-JT2?lang=en DLAP-201-JT2] enclosure<br />
:* Advantech [https://www.advantech.com/products/9140b94e-bcfa-4aa4-8df2-1145026ad613/mic-7200/mod_19d7f198-a3f3-4975-ac87-e8facd1045b3 MIC-720AI] enclosure<br />
:* Axiomtek [http://www.axiomtek.com/Default.aspx?MenuId=Products&FunctionId=ProductView&ItemId=24544&upcat=144&C=eBOX560-900-FL#/ eBOX560-900-FL]<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ESG501.asp?l1=GPU&l2=ESG501 Rosie] enclosure<br />
:* ConnectTech [http://connecttech.com/sub/Products/ESG503.asp?l1=GPU&l2=ESG503 Rudi] enclosure<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG001.asp?l1=GPU&l2=ASG001 Astro] carrier<br />
:* Curtiss-Wright [https://www.curtisswrightds.com/products/electronic-systems/rugged-mission-computing/duracor-mission-computers/duracor-312.html Parvus DuraCor-312] rugged enclosure<br />
:* MiiVii [http://www.miivii.com/en/index.html Brain S2] enclosure<br />
:* Silverstone [http://www.phoronix.com/scan.php?page=article&item=silverstone-pt13-mini&num=2 PT13] mini-ITX system<br />
:* Rebotnix [https://rebotnix.com/product/gustav/ GUSTAV] enclosure<br />
:* SMP Robotics [https://smprobotics.com/technology_autonomous_mobile_robot/video_analytics_security_system/ T9 System] enclosure<br />
:* Syslogic [https://www.syslogic.de/eng/ki-embedded-system-94630.shtml?parentPageId=94706 IPC/COMPACTA-2] TX2i enclosure<br />
:* Syslogic [https://www.syslogic.de/eng/deep-learning-rail-computer-92161.shtml IPC/COMPACTA-2] TX2i enclosure (railway system)<br />
:* Syslogic [https://www.syslogic.de/eng/ai-rugged-computer-jetson-tx2-99518.shtml?parentPageId=100092 RPC/COMPACTA-2] TX2i enclosure (IP67)<br />
:* [https://www.vision4ce.com/wp-content/uploads/2017/09/CHARM-100-170801.pdf Vision4CE CHARM-100] enclosure<br />
<br />
=== Expansion Boards ===<br />
<br />
:* Auvidea [http://auvidea.eu/j20/ J20] 6-camera module<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-H2C ]] HDMI to CSI expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-SDI ]] SDI to CSI expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-8eyes ]] 4 or 8 cameras ADAS expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-AUDIO ]] Sound card expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-DSPK ]] Digital speaker and MIC expansion board<br />
:* [[BeadaPanel#BeadaPanel_for_NVIDIA_Jetson_Dev._Board|BeadaPanel USB Display+WiFi+Storage 3-In-1 Companion Kit]]<br />
<br />
=== Other ===<br />
<br />
:* Aeryon [https://www.aeryon.com/skyranger/r80/ SkyRanger R80] drone<br />
:* [http://black.ai black.ai] perception platform<br />
:* RidgeRun [https://developer.ridgerun.com/wiki/index.php?title=Tegra_X2_or_TX2 GStreamer and Multimedia Solutions]<br />
:* [https://www.skydio.com/ Skydio 2] drone<br />
<br />
<br /><br />
<br />
= Getting Help = <br />
If you have a technical question or bug report, please visit the '''[https://devtalk.nvidia.com/default/board/188/jetson-tx2/ DevTalk Developer Forums]''' and search or start a topic.<br />
<br />
We summarize some useful topics in http://elinux.org/Jetson_TX2/TX2_Issue page.<br />
<br />
See the official '''[https://developer.nvidia.com/embedded/support Support]''' page on Embedded Developer Zone for warranty and RMA information: https://developer.nvidia.com/embedded/support<br />
<br />
For [https://store.nvidia.com NVIDIA webstore] Customer Service, please see the [https://store.nvidia.com/store/nvidia/en_US/help/ThemeID.326200 My Account] page or contact 1-800-797-6530.</div>
Mschenk
https://elinux.org/index.php?title=Jetson_Nano&diff=506861
Jetson Nano
2019-12-16T15:15:24Z
<p>Mschenk: /* Cameras */</p>
<hr />
<div>NVIDIA '''[https://developer.nvidia.com/embedded/buy/jetson-nano-devkit Jetson Nano]''' is an embedded system-on-module (SoM) and developer kit from the '''[https://developer.nvidia.com/buy-jetson 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. <br />
<br />
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.<br />
<br />
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]]'''.<br />
<br />
{| style="color: black; background-color: #ffffff; width: 460px;"<br />
|-<br />
| style="width: 50px; background-color: white;"|<br />
| style="width: 410px; background-color: #76b900;"|<br />
{{spaces|1}} <span style="font-family: Trebuchet MS; color: white;">'''Technical Blog''' — </span>''[https://devblogs.nvidia.com/jetson-nano-ai-computing <span style="font-family: Trebuchet MS; color:white;">NVIDIA Jetson Nano Brings AI to Everyone</span>]''<br />
|}<br />
<br /><br />
<br />
[[File:Jetson_Nano_Family.png|right|text-bottom]]<br />
<br />
= Jetson Nano Developer Kit =<br />
<br />
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.<br />
<br />
[[File:Jetson_Nano_Developer_Kit.png|450px|right]]<br />
<br />
=== What's Included ===<br />
* 80x100mm Reference Carrier Board<br />
* Jetson Nano Module with passive heatsink<br />
* Pop-Up Stand<br />
* Getting Started Guide<br />
(the complete devkit with module and heatsink weighs 138 grams)<br />
<br />
=== What You Will Need ===<br />
* Power Supply<br />
** 5V⎓2A Micro-USB adapter (see [https://www.adafruit.com/product/1995 Adafruit GEO151UB])<br />
** 5V⎓4A DC barrel jack adapter, 5.5mm OD x 2.1mm ID x 9.5mm length, center-positive (see [https://www.adafruit.com/product/1466 Adafruit 1446])<br />
** See the [[Jetson Nano#Power_Supplies|Power Supplies]] section below and [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ Power Supply Considerations] for more information.<br />
* MicroSD card (16GB UHS-1 recommended minimum)<br />
<br />
=== Ports & Interfaces ===<br />
* 4x USB 3.0 A (Host) <br />
* USB 2.0 Micro B (Device)<br />
* MIPI CSI-2 x2 (15-position Camera Flex Connector)<br />
* HDMI 2.0<br />
* DisplayPort<br />
* Gigabit Ethernet (RJ45)<br />
* M.2 Key-E with PCIe x1<br />
* MicroSD card slot<br />
* (3x) I2C, (2x) SPI, UART, I2S, GPIOs<br />
<br />
=== Getting Started ===<br />
* Follow the '''[https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit Getting Started with Jetson Nano Guide]''' to setup your devkit and format the MicroSD card.<br /><br />
* Plug in an HDMI display into Jetson, attach a USB keyboard & mouse, and apply power to boot it up. <br /><br />
* Visit the '''[https://developer.nvidia.com/embedded-computing Embedded Developer Zone]''' and '''[https://devtalk.nvidia.com/default/board/371/jetson-nano/ Jetson Nano Developer Forum]''' to access the latest documentation & downloads.<br />
<br />
=== Availability ===<br />
<br />
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:<br />
<br />
<div style="width:25%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://www.amazon.com/NVIDIA-Jetson-Nano-Developer-Kit/dp/B07PZHBDKT/ Amazon]<br />
* [https://www.arrow.com/en/products/945-13450-0000-000/nvidia Arrow]<br />
* [https://www.newegg.com/Product/Product.aspx?Item=N82E16813190009 Newegg]<br />
* [https://www.seeedstudio.com/NVIDIA-Jetson-Nano-Development-Kit-p-2916.html Seeed Studio]<br />
* [https://www.siliconhighwaydirect.co.uk/ProductDetails.asp?ProductCode=945-13450-0000-000 Silicon Highway]<br />
* [https://www.sparkfun.com/products/15297 SparkFun]<br />
</div><br />
<br />
For the full list, refer to the [https://developer.nvidia.com/buy-jetson?product=jetson_nano&location=US Region Selector].<br />
<br />
= Software Support = <br />
<div style="width:40%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]<br />
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)<br />
* Linux kernel 4.9<br />
* Ubuntu 18.04 aarch64<br />
* [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] 10.0.326<br />
* [https://developer.nvidia.com/cudnn cuDNN] 7.5.0<br />
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6<br />
* [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html TensorFlow] 1.14.0<br />
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6<br />
* OpenCV 3.3.1<br />
* OpenGL 4.6 / OpenGL ES 3.2.5<br />
* Vulkan 1.1.1<br />
* [http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-32-1 L4T Multimedia API] (Argus 0.97)<br />
* GStreamer 1.14.1<br />
* V4L2 media controller support<br />
* [https://developer.nvidia.com/nsight-systems NVIDIA Nsight Systems] 2019.4<br />
* [https://developer.nvidia.com/nsight-graphics NVIDIA Nsight Graphics] 2019.2<br />
* [https://developer.nvidia.com/nsight-compute NVIDIA Nsight Compute] 1.0<br />
</div><br />
<br />
See the '''[[Jetson Zoo]]''' for more software packages to install on top of JetPack.<br />
<br />
= Guides and Tutorials =<br />
<br />
This section contains recipes for following along on Jetson Nano.<br />
<br />
=== System Tools ===<br />
<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://docs.nvidia.com/jetson/l4t/index.html L4T Kernel Development Guide]<br />
* [[Jetson/Clone|Clone & Restore]]<br />
* [https://github.com/jtagxhub/jetpack-agx-build Jetson Nano Build Assistant Scripts]<br />
* [[Jetson/FAQ/BSP|BSP FAQ]]<br />
* [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ Power Supply Considerations]<br />
* [[Jetson/Nano/Upstream|Upstream Development Guide]]<br />
* [https://devtalk.nvidia.com/default/topic/1049811/jetson-nano/cuda-and-vision-works-demos/post/5328027/#5328027 CUDA and VisionWorks Samples]<br />
* [https://devtalk.nvidia.com/default/topic/1052324/jetson-nano/jetson-nano-aws-greengrass-/post/5341970/#5341970 Install AWS Greengrass] - IoT framework<br />
* [https://support.rackspace.com/how-to/create-a-linux-swap-file/ Mounting a SWAP File]<br />
* [https://www.jetsonhacks.com/2019/04/25/jetson-nano-run-on-usb-drive/ Booting from SSD]<br />
* [https://www.jetsonhacks.com/nvidia-jetson-nano-j41-header-pinout/ GPIO Header Pin-out]<br />
* [https://github.com/jwatte/jetson-gpio-example GPIO Direct Access from C]<br />
* [https://github.com/rt-net/JetsonNano_DT_SPI Enabling SPI in DTS (R32.1)]<br />
* [https://github.com/gtjoseph/jetson-nano-support/tree/l4t_32.2.1 Enabling SPI in DTS (R32.2.1)]<br />
* [https://devtalk.nvidia.com/default/topic/1050026/jetson-nano/read-serial-number-of-jetson-nano/post/5329191/#5329191 Reading Serial Number]<br />
* [https://gist.github.com/dusty-nv/e4314241677cf38f40d556931d0c4a38 Reading MAC Address]<br />
* [https://github.com/rbonghi/jetson_easy jetson_easy] - automatic setup/scripting<br />
* [https://github.com/rbonghi/jetson_stats jetson_stats] - jtop, service and other tools<br />
* [https://github.com/pvaret/rtl8192cu-fixes rtl8192cu-fixes] - patched Edimax EW-7811 Wi-Fi driver<br />
</div><br />
<br />
=== Deep Learning ===<br />
See the '''[[Jetson Zoo]]''' for more resources on deploying AI and deep learning.<br />
<br />
* [https://github.com/dusty-nv/jetson-inference Hello AI World] (jetson-inference)<br />
* [https://developer.nvidia.com/embedded/downloads#?search=TensorFlow TensorFlow Installer] (pip wheel)<br />
* [https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/ PyTorch Installer] (pip wheel)<br />
* [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)<br />
* [https://devtalk.nvidia.com/default/topic/1065203/jetson-nano/paddlepaddle-for-jetson-nano-version-1-5-2-now-available/ PaddlePaddle Installer] (pip wheel)<br />
* [https://devtalk.nvidia.com/default/topic/1050377/jetson-nano/deep-learning-inference-benchmarking-instructions/ Deep Learning Inference Benchmarking Instructions]<br />
* [https://medium.com/swlh/how-to-run-tensorflow-object-detection-model-on-jetson-nano-8f8c6d4352e8 TensorFlow Object Detection With TensorRT] (TF-TRT)<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstInference RidgeRun's GstInference]<br />
* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]<br />
<br />
=== Robotics ===<br />
* [https://github.com/NVIDIA-AI-IOT/jetbot NVIDIA JetBot] (AI-powered robotics kit)<br />
* [https://github.com/dusty-nv/jetbot_ros jetbot_ros] (ROS nodes for JetBot)<br />
* [http://wiki.ros.org/melodic/Installation/Ubuntu ROS Melodic] (ROS install guide)<br />
* [https://github.com/dusty-nv/ros_deep_learning ros_deep_learning] (jetson-inference nodes)<br />
<br />
=== Multimedia ===<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstInterpipe RidgeRun's GstInterpipe] (GStreamer plug-in for communication between two or more independent pipelines)<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstWebRTC RidgeRun's GstRRWebRTC] (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)<br />
* [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)<br />
* [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)<br />
* [http://developer.ridgerun.com/wiki/index.php?title=GstCUDA RidgeRun's GstCUDA] (RidgeRun CUDA ZeroCopy for GStreamer)<br />
* [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)<br />
* [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)<br />
<br />
=== V4L2 drivers for cameras ===<br />
<br />
*RidgeRun has a [https://developer.ridgerun.com/wiki/index.php?title=V4L2_drivers_available_for_Jetson_SoCs list of drivers already supported in Jetson], please check if the driver that you need is already there. Otherwise, RidgeRun offers [https://developer.ridgerun.com/wiki/index.php?title=V4L2_driver_for_camera_sensor_or_capture_chip services to create the driver for you]<br />
<br />
=== Design FAQs ===<br />
<br />
There are some useful FAQs for Jetson Nano design, link is [[Jetson_Nano/FAQ|here]].<br />
<br /><br />
<br /><br />
<br />
= Ecosystem Products and Sensors =<br />
<br />
The following are 3rd-party accessories, peripherals, and cameras available for Jetson Nano.<br />
<br />
==== Cameras ====<br />
* Allied Vision MIPI CSI-2 (one open-source CSI-2 driver for all cameras on [https://github.com/alliedvision/linux_nvidia_jetson Github.com])<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-050.html Alvium 1500 C-050] 0.5MP PYTHON 480<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-120.html Alvium 1500 C-120] 1.2MP AR0135CS<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-210.html Alvium 1500 C-210] 2.1MP AR0521<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-500.html Alvium 1500 C-500] 5MP AR0521<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-040.html Alvium 1800 C-040] 0.4MP Sony IMX287<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-158.html Alvium 1800 C-158] 1.6MP Sony IMX273<br />
* Allied Vision USB3 Vision <br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-040.html Alvium 1800 U-040] 0.4MP Sony IMX287<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-050.html Alvium 1800 U-050] 0.5MP PYTHON 480<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-120.html Alvium 1800 U-120] 1.2MP AR0135CS<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-158.html Alvium 1800 U-158] 1.6MP Sony IMX273<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-500.html Alvium 1800 U-500] 5MP AR0521<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-501m%20NIR.html Alvium 1800 U-501m NIR] 5MP AR0522<br />
* 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)<br />
* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-agx-xavier-cameras/stereo-camera.asp STEEReoCAM™] (2.0 MP MIPI Stereo Camera)<br />
* Logitech [https://www.logitech.com/en-us/product/hd-webcam-c270 C270] (USB webcam)<br />
* Logitech [https://www.amazon.com/Logitech-Widescreen-Calling-Recording-Desktop/dp/B006JH8T3S C920] (USB webcam)<br />
* Leopard Imaging [https://leopardimaging.com/product/li-imx219-mipi-ff-nano/ LI-IMX219-MIPI-FF-NANO] (IMX219 sensor) <br />
* Raspberry Pi [https://www.raspberrypi.org/products/camera-module-v2/ Camera v2] (IMX219 sensor)<br />
* Appro [http://www.appropho.com/products_ii_en.html?id=187&type=36#pdb04 AP-IMX179-MIPIx1] (IMX179 sensor)<br />
* Appro [http://www.appropho.com/products_ii_en.html?id=187&type=36#pdc04 AP-IMX290-MIPIx1] (IMX290 sensor)<br />
* Stereolabs [https://www.stereolabs.com/ ZED] (stereo camera)<br />
<br />
==== Carriers ====<br />
* Antmicro [https://antmicro.com/blog/2019/03/nvidia-jetson-nano-antmicros-baseboard/ Jetson Nano Baseboard] (module carrier)<br />
* Auvidea [https://auvidea.eu/product/70780/ JN30] (module carrier)<br />
* Auvidea [https://auvidea.eu/product/70781/ JN30-LC] (module carrier)<br />
* Leopard Imaging [https://leopardimaging.com/product/li-nano-cb/ LI-NANO-CB] (module carrier)<br />
* Realtimes [http://www.realtimes.cn/cn/product/rtso-6001.html RTSO-6001] (module carrier)<br />
<br />
==== Enclosures ====<br />
* ConnectTech [http://connecttech.com/products/nvidia-jetson-nano/ Nano-Pac] (3D-printable enclosure)<br />
* Jetson [https://cults3d.com/en/3d-model/tool/jetson-nano-case Nano Case] (3D-printable enclosure)<br />
* Jetson [https://www.thingiverse.com/thing:3532828 NanoMesh] (3D-printable enclosure)<br />
* Jetson [https://www.thingiverse.com/thing:3547555 NanoMesh Mini] (3D-printable enclosure)<br />
* [https://github.com/57Bravo/jetson_nano_enc jetson_nano_enc] (3D-printable enclosure)<br />
* [https://github.com/dudasdavid/Jetson-nano-case Jetson-nano-case] (3D-printable enclosure)<br />
* [https://www.amazon.com/Geekworm-NVIDIA-Enclosure-Control-Developer/dp/B07RRRX121 Geekworm Jetson Nano Case] (metal enclosure)<br />
* [https://www.amazon.com/GeeekPi-NVIDIA-Cooling-Control-Developer/dp/B07VVJNXMB/ GeeekPi Jetson Nano Case] (metal enclosure)<br />
* [https://www.amazon.com/Case-Jetson-Nano-Compatible-Peripherals/dp/B07VTNSS4S Waveshare Jetson Nano Case] (metal enclosure)<br />
* [https://www.kksb-cases.us/collections/nvidia/products/kksb-jetson-nano-case-black# KKSB Jetson Nano Case] (metal enclosure)<br />
* [https://www.iotamy.com/Jetson-Nano-IP67-Fanless-Aluminium-Enclosure IP67 Fanless Aluminum Case] (metal enclosure)<br />
* [https://www.picocluster.com/collections/jeston-nano PicoCluster] (cluster chassis)<br />
<br />
==== Power Supplies ====<br />
See the [[Jetson_Nano#What_You_Will_Need|Power Supply]] section and this [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ forum post] for more information about selecting proper power adapters.<br />
<br />
* Adafruit [https://www.adafruit.com/product/1995 GEO151UB] (5V⎓2.5A MicroUSB adapter)<br />
* Adafruit [https://www.adafruit.com/product/1466 GEO241DA-0540] (5V⎓4A DC barrel jack adapter)<br />
* Geekworm [https://www.amazon.com/dp/B07413Q5Y4 5V⎓4A DC barrel jack adapter]<br />
* GeekPi [https://www.amazon.com/dp/B07CYZ9GZZ ABT025050] (5V⎓2.5A MicroUSB Adapter with ON/OFF Switch)<br />
* Pwr+ [https://www.amazon.com/dp/B00L88M8TE PWR-TA05035N] (5V⎓3.5A MicroUSB AC Adapter)<br />
* Raspberry Pi [https://www.raspberrypi.org/products/raspberry-pi-universal-power-supply/ DSA-13PFC-05 FCA 051250] (5.1V⎓2.5A Universal MicroUSB Power Supply)<br />
<br />
==== Battery Packs ====<br />
<br />
* INUI [https://www.amazon.com/gp/product/B07H6LB4J4 10000mAh] (dual 5V⎓3A Micro-USB)<br />
* Krisdonia [https://www.amazon.com/dp/B076GYGR6M 25000mAh] (5V⎓3A Micro-USB / DC barrel jack)<br />
<br />
==== Wireless ====<br />
* Edimax [https://www.edimax.com/edimax/merchandise/merchandise_detail/data/edimax/in/wireless_adapters_n150/ew-7811un/ EW-7811Un] (USB Wi-Fi wireless dongle)<br />
* Intel [https://www.newegg.com/Product/Product.aspx?Item=9SIAH718PH3221 8265NGW] (M.2 Key-E Wi-Fi/BT wireless card)<br />
* Geekworm [https://geekworm.com/products/geekworm-nvidia-jetson-nano-dual-band-wireless-usb-3-0-adapter-5ghz-2-4ghz-1200m Dual Band Wireless USB 3.0 Wi-Fi Adapter] (USB3 Wi-Fi dongle and SMA antenna)<br />
<br />
==== Storage ====<br />
* Geekworm [https://www.amazon.com/dp/B07T9FQ293 SATA SSD/HDD Shield] (USB3 SATA shield)<br />
* Geekworm [https://www.amazon.com/dp/B07TYKM7TCSATA NVMe SSD Shield] (USB3 SATA shield)<br />
<br />
==== Other ====<br />
* [https://www.seeedstudio.com/Grove-Base-Hat-for-Raspberry-Pi.html Grove Base Hat for Raspberry Pi] (support Jetson Nano)<br />
* [https://www.seeedstudio.com/Grove-Base-Hat-for-Raspberry-Pi-Zero-p-3187.html Grove Base Hat for Raspberry Pi Zero] (support Jetson Nano)<br />
* [https://auvidea.eu/product/heatsink-for-nvidia-jetson-nano/ Module Heatsink] (available from Auvidea)<br />
* [https://www.amazon.com/NGFF-Mini-PCI-Adapter-Cable/dp/B07JFYSNVL M.2 Key-E to Mini-PCIe] (PCIe adapter)<br />
* [https://www.amazon.com/gp/product/B07DZF1W55 M.2 Key-E to Key-M] (PCIe adapter)<br />
* Noctua [https://noctua.at/en/nf-a4x20-5v-pwm NF-A4x20 5V PWM] (optional fan)<br />
* [[BeadaPanel#BeadaPanel_for_NVIDIA_Jetson_Dev._Board|BeadaPanel]] USB Display+WiFi+Storage 3-In-1 Companion Kit<br />
* [https://www.iotamy.com/20W-PoE-Module-for-Jetson-Nano 20W PoE Module for Jetson Nano] (5.2V⎓4A PoE)<br />
<br />
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.<br />
<br />
= Getting Help = <br />
If you have a technical question or bug report, please visit the '''[https://devtalk.nvidia.com/default/board/371/jetson-nano/ Jetson Nano Developer Forum]''' and search or start a new topic.<br />
<br />
See the official '''[https://developer.nvidia.com/embedded/support Support]''' page on Embedded Developer Zone for warranty and RMA information.<br />
<br />
For [https://store.nvidia.com NVIDIA webstore] Customer Service, please see the [https://store.nvidia.com/store/nvidia/en_US/help/ThemeID.326200 My Account] page or contact 1-800-797-6530.</div>
Mschenk
https://elinux.org/index.php?title=Jetson_Nano&diff=506856
Jetson Nano
2019-12-16T15:04:23Z
<p>Mschenk: /* Cameras */</p>
<hr />
<div>NVIDIA '''[https://developer.nvidia.com/embedded/buy/jetson-nano-devkit Jetson Nano]''' is an embedded system-on-module (SoM) and developer kit from the '''[https://developer.nvidia.com/buy-jetson 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. <br />
<br />
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.<br />
<br />
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]]'''.<br />
<br />
{| style="color: black; background-color: #ffffff; width: 460px;"<br />
|-<br />
| style="width: 50px; background-color: white;"|<br />
| style="width: 410px; background-color: #76b900;"|<br />
{{spaces|1}} <span style="font-family: Trebuchet MS; color: white;">'''Technical Blog''' — </span>''[https://devblogs.nvidia.com/jetson-nano-ai-computing <span style="font-family: Trebuchet MS; color:white;">NVIDIA Jetson Nano Brings AI to Everyone</span>]''<br />
|}<br />
<br /><br />
<br />
[[File:Jetson_Nano_Family.png|right|text-bottom]]<br />
<br />
= Jetson Nano Developer Kit =<br />
<br />
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.<br />
<br />
[[File:Jetson_Nano_Developer_Kit.png|450px|right]]<br />
<br />
=== What's Included ===<br />
* 80x100mm Reference Carrier Board<br />
* Jetson Nano Module with passive heatsink<br />
* Pop-Up Stand<br />
* Getting Started Guide<br />
(the complete devkit with module and heatsink weighs 138 grams)<br />
<br />
=== What You Will Need ===<br />
* Power Supply<br />
** 5V⎓2A Micro-USB adapter (see [https://www.adafruit.com/product/1995 Adafruit GEO151UB])<br />
** 5V⎓4A DC barrel jack adapter, 5.5mm OD x 2.1mm ID x 9.5mm length, center-positive (see [https://www.adafruit.com/product/1466 Adafruit 1446])<br />
** See the [[Jetson Nano#Power_Supplies|Power Supplies]] section below and [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ Power Supply Considerations] for more information.<br />
* MicroSD card (16GB UHS-1 recommended minimum)<br />
<br />
=== Ports & Interfaces ===<br />
* 4x USB 3.0 A (Host) <br />
* USB 2.0 Micro B (Device)<br />
* MIPI CSI-2 x2 (15-position Camera Flex Connector)<br />
* HDMI 2.0<br />
* DisplayPort<br />
* Gigabit Ethernet (RJ45)<br />
* M.2 Key-E with PCIe x1<br />
* MicroSD card slot<br />
* (3x) I2C, (2x) SPI, UART, I2S, GPIOs<br />
<br />
=== Getting Started ===<br />
* Follow the '''[https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit Getting Started with Jetson Nano Guide]''' to setup your devkit and format the MicroSD card.<br /><br />
* Plug in an HDMI display into Jetson, attach a USB keyboard & mouse, and apply power to boot it up. <br /><br />
* Visit the '''[https://developer.nvidia.com/embedded-computing Embedded Developer Zone]''' and '''[https://devtalk.nvidia.com/default/board/371/jetson-nano/ Jetson Nano Developer Forum]''' to access the latest documentation & downloads.<br />
<br />
=== Availability ===<br />
<br />
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:<br />
<br />
<div style="width:25%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://www.amazon.com/NVIDIA-Jetson-Nano-Developer-Kit/dp/B07PZHBDKT/ Amazon]<br />
* [https://www.arrow.com/en/products/945-13450-0000-000/nvidia Arrow]<br />
* [https://www.newegg.com/Product/Product.aspx?Item=N82E16813190009 Newegg]<br />
* [https://www.seeedstudio.com/NVIDIA-Jetson-Nano-Development-Kit-p-2916.html Seeed Studio]<br />
* [https://www.siliconhighwaydirect.co.uk/ProductDetails.asp?ProductCode=945-13450-0000-000 Silicon Highway]<br />
* [https://www.sparkfun.com/products/15297 SparkFun]<br />
</div><br />
<br />
For the full list, refer to the [https://developer.nvidia.com/buy-jetson?product=jetson_nano&location=US Region Selector].<br />
<br />
= Software Support = <br />
<div style="width:40%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]<br />
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)<br />
* Linux kernel 4.9<br />
* Ubuntu 18.04 aarch64<br />
* [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] 10.0.326<br />
* [https://developer.nvidia.com/cudnn cuDNN] 7.5.0<br />
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6<br />
* [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html TensorFlow] 1.14.0<br />
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6<br />
* OpenCV 3.3.1<br />
* OpenGL 4.6 / OpenGL ES 3.2.5<br />
* Vulkan 1.1.1<br />
* [http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-32-1 L4T Multimedia API] (Argus 0.97)<br />
* GStreamer 1.14.1<br />
* V4L2 media controller support<br />
* [https://developer.nvidia.com/nsight-systems NVIDIA Nsight Systems] 2019.4<br />
* [https://developer.nvidia.com/nsight-graphics NVIDIA Nsight Graphics] 2019.2<br />
* [https://developer.nvidia.com/nsight-compute NVIDIA Nsight Compute] 1.0<br />
</div><br />
<br />
See the '''[[Jetson Zoo]]''' for more software packages to install on top of JetPack.<br />
<br />
= Guides and Tutorials =<br />
<br />
This section contains recipes for following along on Jetson Nano.<br />
<br />
=== System Tools ===<br />
<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://docs.nvidia.com/jetson/l4t/index.html L4T Kernel Development Guide]<br />
* [[Jetson/Clone|Clone & Restore]]<br />
* [https://github.com/jtagxhub/jetpack-agx-build Jetson Nano Build Assistant Scripts]<br />
* [[Jetson/FAQ/BSP|BSP FAQ]]<br />
* [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ Power Supply Considerations]<br />
* [[Jetson/Nano/Upstream|Upstream Development Guide]]<br />
* [https://devtalk.nvidia.com/default/topic/1049811/jetson-nano/cuda-and-vision-works-demos/post/5328027/#5328027 CUDA and VisionWorks Samples]<br />
* [https://devtalk.nvidia.com/default/topic/1052324/jetson-nano/jetson-nano-aws-greengrass-/post/5341970/#5341970 Install AWS Greengrass] - IoT framework<br />
* [https://support.rackspace.com/how-to/create-a-linux-swap-file/ Mounting a SWAP File]<br />
* [https://www.jetsonhacks.com/2019/04/25/jetson-nano-run-on-usb-drive/ Booting from SSD]<br />
* [https://www.jetsonhacks.com/nvidia-jetson-nano-j41-header-pinout/ GPIO Header Pin-out]<br />
* [https://github.com/jwatte/jetson-gpio-example GPIO Direct Access from C]<br />
* [https://github.com/rt-net/JetsonNano_DT_SPI Enabling SPI in DTS (R32.1)]<br />
* [https://github.com/gtjoseph/jetson-nano-support/tree/l4t_32.2.1 Enabling SPI in DTS (R32.2.1)]<br />
* [https://devtalk.nvidia.com/default/topic/1050026/jetson-nano/read-serial-number-of-jetson-nano/post/5329191/#5329191 Reading Serial Number]<br />
* [https://gist.github.com/dusty-nv/e4314241677cf38f40d556931d0c4a38 Reading MAC Address]<br />
* [https://github.com/rbonghi/jetson_easy jetson_easy] - automatic setup/scripting<br />
* [https://github.com/rbonghi/jetson_stats jetson_stats] - jtop, service and other tools<br />
* [https://github.com/pvaret/rtl8192cu-fixes rtl8192cu-fixes] - patched Edimax EW-7811 Wi-Fi driver<br />
</div><br />
<br />
=== Deep Learning ===<br />
See the '''[[Jetson Zoo]]''' for more resources on deploying AI and deep learning.<br />
<br />
* [https://github.com/dusty-nv/jetson-inference Hello AI World] (jetson-inference)<br />
* [https://developer.nvidia.com/embedded/downloads#?search=TensorFlow TensorFlow Installer] (pip wheel)<br />
* [https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/ PyTorch Installer] (pip wheel)<br />
* [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)<br />
* [https://devtalk.nvidia.com/default/topic/1065203/jetson-nano/paddlepaddle-for-jetson-nano-version-1-5-2-now-available/ PaddlePaddle Installer] (pip wheel)<br />
* [https://devtalk.nvidia.com/default/topic/1050377/jetson-nano/deep-learning-inference-benchmarking-instructions/ Deep Learning Inference Benchmarking Instructions]<br />
* [https://medium.com/swlh/how-to-run-tensorflow-object-detection-model-on-jetson-nano-8f8c6d4352e8 TensorFlow Object Detection With TensorRT] (TF-TRT)<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstInference RidgeRun's GstInference]<br />
* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]<br />
<br />
=== Robotics ===<br />
* [https://github.com/NVIDIA-AI-IOT/jetbot NVIDIA JetBot] (AI-powered robotics kit)<br />
* [https://github.com/dusty-nv/jetbot_ros jetbot_ros] (ROS nodes for JetBot)<br />
* [http://wiki.ros.org/melodic/Installation/Ubuntu ROS Melodic] (ROS install guide)<br />
* [https://github.com/dusty-nv/ros_deep_learning ros_deep_learning] (jetson-inference nodes)<br />
<br />
=== Multimedia ===<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstInterpipe RidgeRun's GstInterpipe] (GStreamer plug-in for communication between two or more independent pipelines)<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstWebRTC RidgeRun's GstRRWebRTC] (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)<br />
* [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)<br />
* [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)<br />
* [http://developer.ridgerun.com/wiki/index.php?title=GstCUDA RidgeRun's GstCUDA] (RidgeRun CUDA ZeroCopy for GStreamer)<br />
* [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)<br />
* [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)<br />
<br />
=== V4L2 drivers for cameras ===<br />
<br />
*RidgeRun has a [https://developer.ridgerun.com/wiki/index.php?title=V4L2_drivers_available_for_Jetson_SoCs list of drivers already supported in Jetson], please check if the driver that you need is already there. Otherwise, RidgeRun offers [https://developer.ridgerun.com/wiki/index.php?title=V4L2_driver_for_camera_sensor_or_capture_chip services to create the driver for you]<br />
<br />
=== Design FAQs ===<br />
<br />
There are some useful FAQs for Jetson Nano design, link is [[Jetson_Nano/FAQ|here]].<br />
<br /><br />
<br /><br />
<br />
= Ecosystem Products and Sensors =<br />
<br />
The following are 3rd-party accessories, peripherals, and cameras available for Jetson Nano.<br />
<br />
==== Cameras ====<br />
* Allied Vision MIPI CSI-2 (one CSI-2 driver for all cameras [https://github.com/alliedvision/linux_nvidia_jetson linux_nvidia_jetson])<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-050.html Alvium 1500 C-050] 0.5MP PYTHON 480<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-120.html Alvium 1500 C-120] 1.2MP AR0135CS<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-210.html Alvium 1500 C-210] 2.1MP AR0521<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-500.html Alvium 1500 C-500] 5MP AR0521<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-040.html Alvium 1800 C-040] 0.4MP Sony IMX287<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-158.html Alvium 1800 C-158] 1.6MP Sony IMX273<br />
* Allied Vision USB3 Vision <br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-040.html Alvium 1800 U-040] 0.4MP Sony IMX287<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-050.html Alvium 1800 U-050] 0.5MP PYTHON 480<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-120.html Alvium 1800 U-120] 1.2MP AR0135CS<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-158.html Alvium 1800 U-158] 1.6MP Sony IMX273<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-500.html Alvium 1800 U-500] 5MP AR0521<br />
** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-501m%20NIR.html Alvium 1800 U-501m NIR] 5MP AR0522<br />
* 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)<br />
* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-agx-xavier-cameras/stereo-camera.asp STEEReoCAM™] (2.0 MP MIPI Stereo Camera)<br />
* Logitech [https://www.logitech.com/en-us/product/hd-webcam-c270 C270] (USB webcam)<br />
* Logitech [https://www.amazon.com/Logitech-Widescreen-Calling-Recording-Desktop/dp/B006JH8T3S C920] (USB webcam)<br />
* Leopard Imaging [https://leopardimaging.com/product/li-imx219-mipi-ff-nano/ LI-IMX219-MIPI-FF-NANO] (IMX219 sensor) <br />
* Raspberry Pi [https://www.raspberrypi.org/products/camera-module-v2/ Camera v2] (IMX219 sensor)<br />
* Appro [http://www.appropho.com/products_ii_en.html?id=187&type=36#pdb04 AP-IMX179-MIPIx1] (IMX179 sensor)<br />
* Appro [http://www.appropho.com/products_ii_en.html?id=187&type=36#pdc04 AP-IMX290-MIPIx1] (IMX290 sensor)<br />
* Stereolabs [https://www.stereolabs.com/ ZED] (stereo camera)<br />
<br />
==== Carriers ====<br />
* Antmicro [https://antmicro.com/blog/2019/03/nvidia-jetson-nano-antmicros-baseboard/ Jetson Nano Baseboard] (module carrier)<br />
* Auvidea [https://auvidea.eu/product/70780/ JN30] (module carrier)<br />
* Auvidea [https://auvidea.eu/product/70781/ JN30-LC] (module carrier)<br />
* Leopard Imaging [https://leopardimaging.com/product/li-nano-cb/ LI-NANO-CB] (module carrier)<br />
* Realtimes [http://www.realtimes.cn/cn/product/rtso-6001.html RTSO-6001] (module carrier)<br />
<br />
==== Enclosures ====<br />
* ConnectTech [http://connecttech.com/products/nvidia-jetson-nano/ Nano-Pac] (3D-printable enclosure)<br />
* Jetson [https://cults3d.com/en/3d-model/tool/jetson-nano-case Nano Case] (3D-printable enclosure)<br />
* Jetson [https://www.thingiverse.com/thing:3532828 NanoMesh] (3D-printable enclosure)<br />
* Jetson [https://www.thingiverse.com/thing:3547555 NanoMesh Mini] (3D-printable enclosure)<br />
* [https://github.com/57Bravo/jetson_nano_enc jetson_nano_enc] (3D-printable enclosure)<br />
* [https://github.com/dudasdavid/Jetson-nano-case Jetson-nano-case] (3D-printable enclosure)<br />
* [https://www.amazon.com/Geekworm-NVIDIA-Enclosure-Control-Developer/dp/B07RRRX121 Geekworm Jetson Nano Case] (metal enclosure)<br />
* [https://www.amazon.com/GeeekPi-NVIDIA-Cooling-Control-Developer/dp/B07VVJNXMB/ GeeekPi Jetson Nano Case] (metal enclosure)<br />
* [https://www.amazon.com/Case-Jetson-Nano-Compatible-Peripherals/dp/B07VTNSS4S Waveshare Jetson Nano Case] (metal enclosure)<br />
* [https://www.kksb-cases.us/collections/nvidia/products/kksb-jetson-nano-case-black# KKSB Jetson Nano Case] (metal enclosure)<br />
* [https://www.iotamy.com/Jetson-Nano-IP67-Fanless-Aluminium-Enclosure IP67 Fanless Aluminum Case] (metal enclosure)<br />
* [https://www.picocluster.com/collections/jeston-nano PicoCluster] (cluster chassis)<br />
<br />
==== Power Supplies ====<br />
See the [[Jetson_Nano#What_You_Will_Need|Power Supply]] section and this [https://devtalk.nvidia.com/default/topic/1048640/jetson-nano/power-supply-considerations-for-jetson-nano-developer-kit/ forum post] for more information about selecting proper power adapters.<br />
<br />
* Adafruit [https://www.adafruit.com/product/1995 GEO151UB] (5V⎓2.5A MicroUSB adapter)<br />
* Adafruit [https://www.adafruit.com/product/1466 GEO241DA-0540] (5V⎓4A DC barrel jack adapter)<br />
* Geekworm [https://www.amazon.com/dp/B07413Q5Y4 5V⎓4A DC barrel jack adapter]<br />
* GeekPi [https://www.amazon.com/dp/B07CYZ9GZZ ABT025050] (5V⎓2.5A MicroUSB Adapter with ON/OFF Switch)<br />
* Pwr+ [https://www.amazon.com/dp/B00L88M8TE PWR-TA05035N] (5V⎓3.5A MicroUSB AC Adapter)<br />
* Raspberry Pi [https://www.raspberrypi.org/products/raspberry-pi-universal-power-supply/ DSA-13PFC-05 FCA 051250] (5.1V⎓2.5A Universal MicroUSB Power Supply)<br />
<br />
==== Battery Packs ====<br />
<br />
* INUI [https://www.amazon.com/gp/product/B07H6LB4J4 10000mAh] (dual 5V⎓3A Micro-USB)<br />
* Krisdonia [https://www.amazon.com/dp/B076GYGR6M 25000mAh] (5V⎓3A Micro-USB / DC barrel jack)<br />
<br />
==== Wireless ====<br />
* Edimax [https://www.edimax.com/edimax/merchandise/merchandise_detail/data/edimax/in/wireless_adapters_n150/ew-7811un/ EW-7811Un] (USB Wi-Fi wireless dongle)<br />
* Intel [https://www.newegg.com/Product/Product.aspx?Item=9SIAH718PH3221 8265NGW] (M.2 Key-E Wi-Fi/BT wireless card)<br />
* Geekworm [https://geekworm.com/products/geekworm-nvidia-jetson-nano-dual-band-wireless-usb-3-0-adapter-5ghz-2-4ghz-1200m Dual Band Wireless USB 3.0 Wi-Fi Adapter] (USB3 Wi-Fi dongle and SMA antenna)<br />
<br />
==== Storage ====<br />
* Geekworm [https://www.amazon.com/dp/B07T9FQ293 SATA SSD/HDD Shield] (USB3 SATA shield)<br />
* Geekworm [https://www.amazon.com/dp/B07TYKM7TCSATA NVMe SSD Shield] (USB3 SATA shield)<br />
<br />
==== Other ====<br />
* [https://www.seeedstudio.com/Grove-Base-Hat-for-Raspberry-Pi.html Grove Base Hat for Raspberry Pi] (support Jetson Nano)<br />
* [https://www.seeedstudio.com/Grove-Base-Hat-for-Raspberry-Pi-Zero-p-3187.html Grove Base Hat for Raspberry Pi Zero] (support Jetson Nano)<br />
* [https://auvidea.eu/product/heatsink-for-nvidia-jetson-nano/ Module Heatsink] (available from Auvidea)<br />
* [https://www.amazon.com/NGFF-Mini-PCI-Adapter-Cable/dp/B07JFYSNVL M.2 Key-E to Mini-PCIe] (PCIe adapter)<br />
* [https://www.amazon.com/gp/product/B07DZF1W55 M.2 Key-E to Key-M] (PCIe adapter)<br />
* Noctua [https://noctua.at/en/nf-a4x20-5v-pwm NF-A4x20 5V PWM] (optional fan)<br />
* [[BeadaPanel#BeadaPanel_for_NVIDIA_Jetson_Dev._Board|BeadaPanel]] USB Display+WiFi+Storage 3-In-1 Companion Kit<br />
* [https://www.iotamy.com/20W-PoE-Module-for-Jetson-Nano 20W PoE Module for Jetson Nano] (5.2V⎓4A PoE)<br />
<br />
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.<br />
<br />
= Getting Help = <br />
If you have a technical question or bug report, please visit the '''[https://devtalk.nvidia.com/default/board/371/jetson-nano/ Jetson Nano Developer Forum]''' and search or start a new topic.<br />
<br />
See the official '''[https://developer.nvidia.com/embedded/support Support]''' page on Embedded Developer Zone for warranty and RMA information.<br />
<br />
For [https://store.nvidia.com NVIDIA webstore] Customer Service, please see the [https://store.nvidia.com/store/nvidia/en_US/help/ThemeID.326200 My Account] page or contact 1-800-797-6530.</div>
Mschenk
https://elinux.org/index.php?title=Jetson_TX2&diff=506851
Jetson TX2
2019-12-16T14:59:12Z
<p>Mschenk: /* Cameras */</p>
<hr />
<div><br />
NVIDIA [https://developer.nvidia.com/embedded/buy/jetson-tx2-devkit Jetson TX2] is an embedded system-on-module (SoM) with dual-core NVIDIA Denver2 + quad-core ARM Cortex-A57, 8GB 128-bit LPDDR4 and integrated 256-core Pascal GPU.<br />
<br />
Useful for deploying computer vision and deep learning, Jetson TX2 runs Linux and provides greater than 1TFLOPS of FP16 compute performance in less than 7.5 watts of power.<br />
<br />
Jetson TX2 is available as the '''[[#Jetson TX2 Module|module]]''', '''[[#Jetson TX2 Developer Kit|developer kit]]''', and in compatible '''[[#Ecosystem Products|ecosystem]]''' products. See the wiki of other Jetson's '''[[Jetson|here]]''', including the latest [[Jetson AGX Xavier]].<br />
<br />
{| style="color: black; background-color: #ffffff; width: 600px;"<br />
|-<br />
| style="width: 50px; background-color: white;"|<br />
| style="width: 550px; background-color: #76b900;"|<br />
{{spaces|1}} <span style="font-family: Trebuchet MS; color: white;">'''Parallel ForAll''' — </span>''[https://devblogs.nvidia.com/parallelforall/jetson-tx2-delivers-twice-intelligence-edge/ <span style="font-family: Trebuchet MS; color:white;">NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge</span>]''<br />
|}<br />
<br /><br />
<br />
[[File:NVIDIA_Jetson_TX2_Module_Devkit.png|800px|right|text-bottom]]<br />
<br />
= Jetson TX2 Module =<br />
The Jetson TX2 module contains all the active processing components. The ports are broken out through a carrier board.<br /><br />
<br />
Below is a partial list of the module's features. Please see the [https://developer.nvidia.com/embedded/dlc/jetson-tx2-module-datasheet Jetson TX2 Module Datasheet] for the complete specifications.<br />
<br />
[[File:Tegra_Parker_Block_Diagram.png|700px|right]]<br />
<br />
=== Processing Components ===<br />
* dual-core NVIDIA Denver2 + quad-core ARM Cortex-A57<br />
* 256-core Pascal GPU<br />
* 8GB LPDDR4, 128-bit interface<br />
* 32GB eMMC<br />
* 4kp60 H.264/H.265 encoder & decoder<br />
* Dual ISPs (Image Signal Processors)<br />
* 1.4 gigapixel/sec MIPI CSI camera ingest<br />
[[File:NVIDIA_Jetson_TX2_Module_TTP.png|323px|right]]<br />
<br />
=== Ports & Peripherals ===<br />
* HDMI 2.0<br />
* 802.11a/b/g/n/ac 2×2 867Mbps WiFi<br />
* Bluetooth 4.1<br />
* USB3, USB2<br />
* 10/100/1000 BASE-T Ethernet<br />
* 12 lanes MIPI CSI 2.0, 2.5 Gb/sec per lane<br />
* PCIe gen 2.0, 1×4 + 1×1 or 2×1 + 1×2<br />
* SATA, SDcard<br />
* dual CAN bus<br />
* UART, SPI, I2C, I2S, GPIOs<br />
<br />
=== Form-Factor ===<br />
* 400-pin Samtec board-to-board connector<br />
* dimensions: 50x87mm {{spaces|1}} (1.96" x 3.42")<br />
* Thermal Transfer Plate (TTP), -25C to 80C operating temperature<br />
* mass: 85 grams, including TTP<br />
* 5.5-19.6VDC input power (consuming 7.5W under typical load)<br />
<br />
=== Software Support ===<br />
<div style="width:40%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]<br />
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)<br />
* Linux kernel 4.9<br />
* Ubuntu 18.04 aarch64<br />
* [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] 10.0.326<br />
* [https://developer.nvidia.com/cudnn cuDNN] 7.5.0<br />
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6<br />
* [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html TensorFlow] 1.14.0<br />
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6<br />
* OpenCV 3.3.1<br />
* OpenGL 4.6 / OpenGL ES 3.2.5<br />
* Vulkan 1.1.1<br />
* [http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-32-1 L4T Multimedia API] (Argus 0.97)<br />
* GStreamer 1.14.1<br />
* V4L2 media controller support<br />
* [https://developer.nvidia.com/nsight-systems NVIDIA Nsight Systems] 2019.4<br />
* [https://developer.nvidia.com/nsight-graphics NVIDIA Nsight Graphics] 2019.2<br />
* [https://developer.nvidia.com/nsight-compute NVIDIA Nsight Compute] 1.0<br />
</div><br />
<br />
See the '''[[Jetson Zoo]]''' for more software packages to install on top of JetPack.<br />
<br />
{| style="color: black; background-color: #ffffff; width: 575px;"<br />
|-<br />
| style="width: 1px; background-color: white;"|<br />
| style="width: 550px; background-color: #76b900;"|<br />
{{spaces|1}} <span style="font-family: Trebuchet MS; color: white;">'''Parallel ForAll''' — </span>''[https://devblogs.nvidia.com/parallelforall/jetpack-doubles-jetson-inference-perf/ <span style="font-family: Trebuchet MS; color:white;">JetPack 3.1 Doubles Jetson's Low-Latency Inference Performance</span>]''<br />
|}<br />
<br />
== Jetson TX2i Module ==<br />
<br />
[[File:Jetson TX2i Module and TTP 800px.png|600px]]<br />
<br />
There's an extended-temperature variant of the TX2 module available called [https://developer.nvidia.com/embedded/buy/jetson-tx2i '''Jetson TX2i'''] that's intended for industrial environments. It has the same processing capabilities as TX2, with a rugged design.<br />
<br />
For more info, see the FAQ [https://developer.nvidia.com/embedded/faq#jetson-differences-tx2i "What changes for industrial environments does Jetson TX2i have compared to Jetson TX2?"]<br />
<br />
<br /><br />
<br />
= Jetson TX2 Developer Kit =<br />
<br />
The [https://developer.nvidia.com/embedded/buy/jetson-tx2-devkit Jetson TX2 Developer Kit] bundles together all the parts to get started, including:<br />
<br />
[[File:NVIDIA_Jetson_TX2_Devkit_Unbox.png|550px|right]]<br />
=== What's Included ===<br />
* mini-ITX Reference carrier board<br />
* Jetson TX2 Module<br />
** fan and heatsink (pre-assembled)<br />
* 5MP CSI camera module (with Omnivision OV5693)<br />
* WiFi/BT antennas<br />
* USB OTG adapter<br />
* 19VDC Power brick<br />
* AC Power cable<br />
<br />
The design files for the reference carrier board and camera module are freely available for [[Jetson_TX2#Platform_Documentation|download]].<br />
<br />
=== Getting Started ===<br />
* Get the latest development software for PC and TX2 by using '''[https://developer.nvidia.com/embedded/jetpack JetPack]'''.<br /><br />
* Plug in an HDMI display into Jetson, attach the antennas and USB keyboard & mouse, and apply power to boot it up. {{spaces|0}} ('''[http://developer.nvidia.com/embedded/dlc/l4t-quick-start-guide-27-1 User Guide]''')<br /><br />
* Visit the '''[https://developer.nvidia.com/embedded-computing Embedded Developer Zone]''' and '''[https://devtalk.nvidia.com/default/board/188/jetson-tx2/ Jetson TX2 Developer Forum]''' to access the latest documentation & downloads.<br />
<br />
=== Availability ===<br />
<br />
* The devkit is available through NVIDIA's '''[https://developer.nvidia.com/embedded/buy/jetson-tx2-devkit Jetson TX2 Developer Kit]''' webpage.<br />
* The individual module is available through NVIDIA's '''[https://devtalk.nvidia.com/default/topic/1006734/jetson-tx2/jetson-tx2-module-available-now Jetson TX2 Module]''' webpage.<br />
* Alternatively, use the [http://www.nvidia.com/embedded Region Selector] to find distributors of the devkit in your region. <br /><br />
* There's also an '''[http://www.nvidia.com/object/jetsontx2-edu-discount.html Academic Discount]''' available for those affiliated with an educational organization.<br />
<br /><br />
<br />
= Platform Documentation =<br />
<br />
NVIDIA has [https://developer.nvidia.com/embedded-computing released] comprehensive documentation and reference designs for the Jetson TX2 module and devkit. <br /><br />
<br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx2-module-datasheet Module Datasheet]''' {{spaces|8}} the official module features, ports, signal pin-out, and package specifications. <br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx2-oem-product-design-guide Design Guide]''' {{spaces|16}} detailed technical design and layout information for creating OEM products. <br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-carrier-board-specification DevKit Carrier Spec]''' {{spaces|6}} design info about the reference carrier board from the devkit.<br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-carrier-board-design-files DevKit Design Files]''' {{spaces|6}} schematics, layout, and design files for the devkit reference carrier board.<br />
* '''[http://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-3D-cad-step-model DevKit CAD Models]''' {{spaces|6}} 3D STEP file for reference carrier board, heatsink, camera board, and module.<br />
* '''[https://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-developer-kit-camera-module-design-files Camera Design Files]''' {{spaces|4}} schematics, layout, and design files for the devkit MIPI CSI-2 camera module.<br />
* '''[http://developer.nvidia.com/embedded/dlc/jetson-tx2-thermal-design-guide Thermal Design Guide]''' {{spaces|1}} mechanical specifications for designing active and passive cooling solutions.<br />
* '''[http://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-interface-comparison-and-migration TX1/TX2 Migration]''' {{spaces|8}} guide to porting applications and hardware between Jetson TX1 and TX2<br />
* '''[http://developer.nvidia.com/embedded/dlc/http://developer.nvidia.com/embedded/dlc/jetson-tx1-tx2-module-battery-and-charger-design-guide Battery Charger Guide]''' {{spaces|1}} document for the design of battery charger<br />
* '''[https://developer.nvidia.com/embedded/dlc/parker-series-trm Tegra X2 (Parker) TRM]''' {{spaces|1}} Technical Reference Manual for NVIDIA TX2 system-on-chip and register data.<br />
* '''[https://developer.nvidia.com/embedded/dlc/l4t-documentation-28-2 L4T Kernel Docs]''' {{spaces|11}} documentation for L4T kernel developers (including V4L2/camera drivers).<br />
* '''[https://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-28-2 Multimedia API Reference]''' {{spaces|8}} documentation to Argus camera API and V4L2 media codecs<br />
* '''[https://developer.nvidia.com/embedded/dlc/l4t-accelerated-gstreamer-guide-28-2 Accelerated GStreamer Guide]''' {{spaces|1}} example gstreamer pipelines for accessing H.264/H.265 hardware video codec.<br />
<br />
Above is a partial list of documents.<br />
Please visit the '''[https://developer.nvidia.com/embedded/downloads#?tx=$product,jetson_tx2 Downloads Center]''' at Embedded Developer Zone for the full list that's currently available.<br /><br />
<br /><br />
<br />
= Guides and Tutorials =<br />
<br />
This section contains recipes for following along on Jetson.<br />
<br />
=== System Tools ===<br />
<br />
Please see [http://elinux.org/Jetson_TX1#System_Tools Jetson TX1 Wiki] for similar entries that also apply to TX2.<br />
<br />
<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
:* [[Jetson/Clone|Cloning & Restore]]<br />
:* [https://github.com/jtagxhub/jetpack-agx-build Jetson TX2 Build Assistant Scripts]<br />
:* [[Jetson/FAQ/BSP|BSP FAQ]]<br />
:* [https://developer.nvidia.com/embedded/downloads#?search=Factory%20Image Factory Images]<br />
:* [[Jetson/TX2_DTB|Setting the DTB]]<br />
:* [[Jetson/TX2_SPI|Enabling the SPI Port]]<br />
:* [http://www.jetsonhacks.com/2017/03/25/build-kernel-and-modules-nvidia-jetson-tx2/ Building Kernel and Modules]<br />
:* [[Jetson/TX2_USB|Enabling USB on Custom Carriers]]<br />
:* [[Jetson/TX2_eMMC|Maximizing RootFS Partition on eMMC]]<br />
:* [http://www.jetsonhacks.com/2017/03/25/nvpmodel-nvidia-jetson-tx2-development-kit/ nvpmodel] - dynamic performance profiles<br />
:* [https://gist.github.com/JasonAtNvidia/e03e6675849d1d4049b85ea41efb2171 TX2 GPU support in Docker] - script for GPU from within Docker<br />
:* [https://github.com/Technica-Corporation/Tegra-Docker Tegra-Docker]<br />
:* [[Jetson/TX2|Ubuntu Base]] Minimal footprint (500Mb with OS only) using [https://wiki.ubuntu.com/Base Ubuntu Base]<br />
:* [http://elinux.org/Boot_from_sd Boot from SD card]<br />
:* [[Jetson TX2/r28 Display debug|Display Driver Debugging]]<br />
:* [https://medium.com/@ynd/getting-swift-to-run-on-nvidia-jetson-tx2-ai-computing-platform-1d9bcd6559dc Getting Swift to Run on TX2]<br />
:* [https://github.com/rbonghi/jetson_easy jetson_easy] - automatic setup/scripting<br />
:* [https://github.com/rbonghi/jetson_stats jetson_stats] - jtop, service and other tools<br />
:* [https://sites.google.com/site/jetsontricks/ v4l2loopback,rtsp,screencapture,misc] <br />
:* [https://devtalk.nvidia.com/default/topic/1057158/jetson-tx2/guide-to-enabling-mcp251x-mcp2515-on-the-tx2-spi-can-/ Enabling MCP2515 SPI-CAN Device]<br />
</div><br />
<br />
=== Robotics ===<br />
<br />
:* [https://github.com/NVIDIA-Jetson NVIDIA Jetson GitHub] {{spaces|10}} (open-source robotics projects with deep learning)<br />
:* [https://github.com/NVIDIA-Jetson/redtail NVIDIA Redtail GitHub] {{spaces|9}} (end-to-end deep learning drone for ROS)<br />
:* [https://developer.nvidia.com/embedded/community/reference-platforms Jetson Reference Platforms] {{spaces|1}} (off-the-shelf robots with TX1/TX2)<br />
:* [[Jetson/FRC_Setup|FIRST FRC Configuration]] {{spaces|8}} (setup guide for FIRST Robotics)<br />
:* [https://www.chiefdelphi.com/media/papers/download/4758 FIRST FRC Neural Networks] (Zebracorns team #900 [https://www.chiefdelphi.com/media/papers/3274 object tracking])<br />
:* [https://www.chiefdelphi.com/media/papers/download/5169 ROS for FRC Whitepaper] {{spaces|5}} (Zebracorns team #900 Vision [https://github.com/FRC900/2017VisionCode GitHub])<br />
:* [http://www.jetsonhacks.com/2017/03/27/robot-operating-system-ros-nvidia-jetson-tx2/ Installing ROS Kinetic (TX2)] {{spaces|1}} (JetsonHacks guide)<br />
:* [https://github.com/mit-racecar/particle_filter Fast SLAM Particle Filter] {{spaces|6}} (Accelerated Localization using Raycasting)<br />
:* [http://ardupilot.org/dev/docs/companion-computer-nvidia-tx2.html Connecting Pixhawk and TX2] (Autopilot with MAVLink Interface)<br />
:* [https://github.com/DiegoHerrera1890/Pixhawk-connected-to-Jetson-Tx2-devkit Running MAVROS with TX2 and PixHawk 4] (TX2/ROS setup with MAVLink)<br />
<br />
=== Computer Vision ===<br />
<br />
:* NVIDIA [https://developer.nvidia.com/embedded/learn/tutorials#collapseOne OpenCV 101] - screencast tutorials<br />
:* [https://github.com/AastaNV/JEP/blob/master/script/install_opencv3.4.0.sh OpenCV-3.4.0 for TX2] building script<br />
:* [http://www.jetsonhacks.com/2017/04/05/build-opencv-nvidia-jetson-tx2/ Build OpenCV for TX2] (JetsonHacks)<br />
:* [http://docs.opencv.org/3.2.0/d6/d15/tutorial_building_tegra_cuda.html Building OpenCV 3.2 with CUDA for Tegra]<br />
:* [https://developer.nvidia.com/embedded/learn/tutorials#collapseVisionWorks VisionWorks training]<br />
<br />
=== Deep Learning ===<br />
<div style="width:80%;column-count:2;-moz-column-count:2;-webkit-column-count:2"><br />
:* [https://developer.nvidia.com/embedded/twodaystoademo NVIDIA Two Days to a Demo] {{spaces|1}} (DIGITS/TensorRT)<br />
:* Caffe {{spaces|1}} (BVLC [https://github.com/BVLC/caffe/wiki/Model-Zoo Model Zoo])<br />
:** [https://github.com/nvidia/caffe NVcaffe FP16] {{spaces|1}} ([https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-nvcaffe.md Install Guide])<br />
:** [http://www.jetsonhacks.com/2017/03/24/caffe-deep-learning-framework-nvidia-jetson-tx2/ Caffe Installation] {{spaces|1}} (JetsonHacks)<br />
:* Caffe2 {{spaces|1}} ([https://github.com/caffe2/caffe2 github.com/caffe2])<br />
:* [https://github.com/chitoku/installDeepvizJetson Deep Visualization Toolbox] install script<br />
:* [https://github.com/peterlee0127/tensorflow-tx2 TensorFlow] install for JetPack 3.1<br />
:* [http://www.jetsonhacks.com/2017/04/02/tensorflow-on-nvidia-jetson-tx2-development-kit/ TensorFlow] post for JetPack 3.0 {{spaces|1}} (JetsonHacks)<br />
:* [https://syed-ahmed.gitbooks.io/nvidia-jetson-tx2-recipes/content/first-question.html TensorFlow] install procedure {{spaces|1}} ([https://devtalk.nvidia.com/default/topic/1000717/jetson-tx2/tensorflow-on-jetson-tx2/post/5112792/#5112792 pip wheel])<br />
:* [https://developer.ridgerun.com/wiki/index.php?title=GstInference RidgeRun's GstInference]<br />
:* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]<br />
:* [https://github.com/AastaNV/JEP/tree/master/script/TensorFlow_1.6 TensorFlow] script and pip wheel for JetPack 3.2 DP<br />
:* [https://github.com/dusty-nv/jetson-reinforcement/blob/master/CMakePreBuild.sh Torch7] {{spaces|1}} install script<br />
:* [https://gist.github.com/dusty-nv/ef2b372301c00c0a9d3203e42fd83426 pyTorch] {{spaces|0}} install script<br />
:* [http://github.com/dusty-nv dusty-nv's Jetson GitHub] {{spaces|3}} [http://github.com/dusty-nv/jetson-inference jetson-inference] {{spaces|2}} [http://github.com/dusty-nv/jetson-inference jetson-reinforcement]<br />
:* [https://github.com/Abaco-Systems/jetson-inference-gv jetson-inference-gv] GigEVision / RTP streaming video (Ross Newman)<br />
:* [https://github.com/S4WRXTTCS/jetson-inference jetson-inference-cards] {{spaces|1}} (playing card recognition by S4WRXTTCS)<br />
:* [https://github.com/AastaNV/Face-Recognition face-recognition] {{spaces|0}} (face detection with TensorRT plugin API by AastaNV)<br />
:* [https://github.com/NVIDIA-Jetson/JEP_ChatBot ChatBot] {{spaces|2}} (TensorFlow→TensorRT inferencing workflow by AastaNV)<br />
:* [https://github.com/NVIDIA-Jetson NVIDIA GitHub] {{spaces|2}} (open-source robotics/DL projects)<br />
:* [https://github.com/NVIDIA-Jetson/redtail NVIDIA Redtail] {{spaces|2}} (end-to-end deep learning drone for ROS)<br />
:* Training a Fish Detector with DetectNet {{spaces|1}} [https://jkjung-avt.github.io/fisheries-dataset/ part 1] [https://jkjung-avt.github.io/detectnet-training/ part 2] {{spaces|1}} (jkjung)<br />
:* [https://heyjetson.com/ Hey, Jetson!] {{spaces|2}} (Automatic Speech Recognition using CNN/RNN)<br />
</div><br />
<br />
=== Multimedia ===<br />
* [https://developer.ridgerun.com/wiki/index.php?title=Xavier/GStreamer_Pipelines Gstreamer Pipelines for AGX Xavier]<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstInterpipe RidgeRun's GstInterpipe] (GStreamer plug-in for communication between two or more independent pipelines)<br />
* [https://developer.ridgerun.com/wiki/index.php?title=GstWebRTC RidgeRun's GstRRWebRTC] (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)<br />
* [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)<br />
* [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)<br />
* [http://developer.ridgerun.com/wiki/index.php?title=GstCUDA RidgeRun's GstCUDA] (RidgeRun CUDA ZeroCopy for GStreamer)<br />
* [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)<br />
* [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)<br />
<br />
=== Camera Info ===<br />
*USB3 - e-con Systems' [https://www.e-consystems.com/4k-usb-camera.asp See3CAM_CU135] was tested on Jetson TX2 with HD (1280X720) @ 46fps and FullHD (1920x1080) @ 36fps in MJPEG (compressed) format, as well as [https://elinux.org/Jetson/Cameras#USB_3.0_webcams_known_to_be_working other settings].<br />
*CSI-2 - [https://www.e-consystems.com/multiple-csi-cameras-for-nvidia-jetson-tx2.asp 6 MIPI CSI-2 Cameras] support for Jetson TX2 from e-con Systems<br />
*CSI-2 - [https://www.e-consystems.com/three-synchronized-4k-cameras-for-nvidia-jetson-tx2.asp 3 MIPI CSI-2 Cameras] support for Jetson TX2 from e-con Systems<br />
<br />
=== V4L2 drivers for cameras ===<br />
<br />
*RidgeRun has a [https://developer.ridgerun.com/wiki/index.php?title=V4L2_drivers_available_for_Jetson_SoCs list of drivers already supported in Jetson], please check if the driver that you need is already there. Otherwise, RidgeRun offers [https://developer.ridgerun.com/wiki/index.php?title=V4L2_driver_for_camera_sensor_or_capture_chip services to create the driver for you]<br />
<br />
=== Design FAQs ===<br />
<br />
There are some useful FAQs for Jetson TX2 design, link is [[Jetson_TX2/FAQ|here]].<br />
<br /><br />
<br /><br />
<br />
= Ecosystem Products =<br />
<br />
The following are 3rd-party carriers, enclosures, expansion boards, and accessories available for Jetson TX2. <br />
<br />
Please see additional backwards-compatible [[Jetson_TX1#Ecosystem_Products|Ecosystem Products for TX1]].<br />
<br /><br />
<br />
=== Cameras ===<br />
<br />
:* Allied Vision MIPI CSI-2 (one CSI-2 driver for all cameras [https://github.com/alliedvision/linux_nvidia_jetson linux_nvidia_jetson])<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-050.html Alvium 1500 C-050] 0.5MP PYTHON 480<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-120.html Alvium 1500 C-120] 1.2MP AR0135CS<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-210.html Alvium 1500 C-210] 2.1MP AR0521<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-500.html Alvium 1500 C-500] 5MP AR0521<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-040.html Alvium 1800 C-040] 0.4MP Sony IMX287<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-158.html Alvium 1800 C-158] 1.6MP Sony IMX273<br />
:* Allied Vision USB3 Vision <br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-040.html Alvium 1800 U-040] 0.4MP Sony IMX287<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-050.html Alvium 1800 U-050] 0.5MP PYTHON 480<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-120.html Alvium 1800 U-120] 1.2MP AR0135CS<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-158.html Alvium 1800 U-158] 1.6MP Sony IMX273<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-500.html Alvium 1800 U-500] 5MP AR0521<br />
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-501m%20NIR.html Alvium 1800 U-501m NIR] 5MP AR0522<br />
:* APPROPHO [http://www.appropho.com/products_en.html?type=36 TX1/TX2 Camera Solutions]<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-OV5640 ]] OV5640 M12 lens camera<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-OV5693 ]] OV5693 camera<br />
:* e-con Systems [https://www.e-consystems.com/nvidia-cameras/jetson-agx-xavier-cameras/stereo-camera.asp 3D MIPI Stereo camera for NVIDIA® Jetson AGX Xavier™/TX2]<br />
:* e-con Systems [https://www.e-consystems.com/13mp-autofocus-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 AF MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/3d-usb-stereo-camera-with-nvidia-accelerated-sdk.asp USB Stereo Camera for NVIDIA® Jetson AGX Xavier™/TX2] <br />
:* e-con Systems [https://www.e-consystems.com/2MP-HDR-Jetson-TX2-TX1-Camera-Board.asp 2MP AR0230AT MIPI Jetson TX1/TX2 Camera] <br />
:* e-con Systems [https://www.e-consystems.com/jetson-tx2-ultra-low-light-camera-board.asp 2MP SONY IMX290 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/13mp-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/autofocus-liquid-lens-nvidia-jetson-tx2-camera.asp 3.4 MP AF AR0330 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/13mp-nvidia-jetson-tx1-camera-board.asp 13MP AR1820 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/3MP-Jetson-TX1-Camera-board.asp 3.4 MP AR0330 MIPI Jetson TX1/TX2 Camera]<br />
:* e-con Systems [https://www.e-consystems.com/gmsl-camera-for-nvidia-jetson-tx2.asp 3.4 MP AR0330 GMSL MIPI Jetson TX1/TX2 Camera]<br />
:* Leopard Imaging [https://leopardimaging.com/product-category/nvidia-jetson-cameras/nvidia-tx1tx2-mipi-camera-kits/csi-2-mipi-cameras/ TX1/TX2 camera kits]<br />
:* Stereolabs [https://www.stereolabs.com/ ZED] (stereo camera)<br />
<br />
=== Carriers === <br />
<br />
:* Aetina [http://www.aetina.com.tw/wp-content/uploads/2017/04/eDM_ACE-N620_carrier/index.html N620] nano-ITX carrier<br />
:* Auvidea [https://auvidea.com/j100/ J100] carrier<br />
:* Auvidea [https://auvidea.com/j106/ J106] carrier (6 camera)<br />
:* Auvidea [https://auvidea.com/j120/ J120] carrier<br />
:* Auvidea [https://auvidea.com/j130-with-4k-video-input/ J130] carrier (4K input)<br />
:* Auvidea [https://auvidea.com/j140-dual-gbe/ J140] dual-GbE carrier<br />
:* Auvidea [https://auvidea.com/j150/ J150] OpenGear blade<br />
:* Auvidea [http://www.auvidea.eu/index.php/2015-11-08-08-01-27/2016-02-03-12-30-02/j200-dual-jetson-tx1-carrier J200] carrier<br />
:* Avermedia [https://www.avermedia.com/professional/product/ex731_aa_n1/overview EX731-AA] carrier<br />
:* Avermedia [https://www.avermedia.com/professional/product/ex713_aa/overview EX713-AA] carrier<br />
:* Bluetechnix [https://www.bluetechnix.com/en/products/multi-tof-platform/product/multi-tof-platform/ Multi-ToF platform]<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG008.asp?l1=GPU&l2=ASG008 Sprocket] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG003.asp?l1=GPU&l2=ASG003 Orbitty] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG006.asp?l1=GPU&l2=ASG006 Spacely] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG007.asp?l1=GPU&l2=ASG007 Cogswell] carrier<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG002.asp?l1=GPU&l2=ASG002 Elroy] carrier<br />
:* ConnectTech [http://connecttech.com/sub/Products/VPG003_GraphiteVPXTX1.asp?l1=GPU&l2=VPXTX1 3U VPX] card<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-Lite carrier]] with eDP, CSI, DSI, MicroHDMI etc<br />
:* Gumstix [https://store.gumstix.com/aerocore2-for-nvidia-jetson.html Aerocore2] drone carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/9001.html RTSO-9001] carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/RTSO9002.html RTSO-9002] carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/rtso-9003.html RTSO-9003] carrier<br />
:* Realtimes [http://www.realtimes.cn/en/product/products-8-55.html RTSS-Z5O3U] enclosure<br />
<br />
=== Enclosures ===<br />
<br />
:* Aaeon [http://www.aaeon.com/en/p/fanless-embedded-computers-boxer-8120ai BOXER-8120AI] enclosure<br />
:* Abaco [https://www.abaco.com/products/gvc1000 GVC1000] enclosure<br />
:* ADLINK [https://www.adlinktech.com/Products/Deep_Learning_Accelerator_Platform_and_Server/Inference_Platform/DLAP-201-JT2?lang=en DLAP-201-JT2] enclosure<br />
:* Advantech [https://www.advantech.com/products/9140b94e-bcfa-4aa4-8df2-1145026ad613/mic-7200/mod_19d7f198-a3f3-4975-ac87-e8facd1045b3 MIC-720AI] enclosure<br />
:* Axiomtek [http://www.axiomtek.com/Default.aspx?MenuId=Products&FunctionId=ProductView&ItemId=24544&upcat=144&C=eBOX560-900-FL#/ eBOX560-900-FL]<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ESG501.asp?l1=GPU&l2=ESG501 Rosie] enclosure<br />
:* ConnectTech [http://connecttech.com/sub/Products/ESG503.asp?l1=GPU&l2=ESG503 Rudi] enclosure<br />
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG001.asp?l1=GPU&l2=ASG001 Astro] carrier<br />
:* Curtiss-Wright [https://www.curtisswrightds.com/products/electronic-systems/rugged-mission-computing/duracor-mission-computers/duracor-312.html Parvus DuraCor-312] rugged enclosure<br />
:* MiiVii [http://www.miivii.com/en/index.html Brain S2] enclosure<br />
:* Silverstone [http://www.phoronix.com/scan.php?page=article&item=silverstone-pt13-mini&num=2 PT13] mini-ITX system<br />
:* Rebotnix [https://rebotnix.com/product/gustav/ GUSTAV] enclosure<br />
:* SMP Robotics [https://smprobotics.com/technology_autonomous_mobile_robot/video_analytics_security_system/ T9 System] enclosure<br />
:* Syslogic [https://www.syslogic.de/eng/ki-embedded-system-94630.shtml?parentPageId=94706 IPC/COMPACTA-2] TX2i enclosure<br />
:* Syslogic [https://www.syslogic.de/eng/deep-learning-rail-computer-92161.shtml IPC/COMPACTA-2] TX2i enclosure (railway system)<br />
:* Syslogic [https://www.syslogic.de/eng/ai-rugged-computer-jetson-tx2-99518.shtml?parentPageId=100092 RPC/COMPACTA-2] TX2i enclosure (IP67)<br />
:* [https://www.vision4ce.com/wp-content/uploads/2017/09/CHARM-100-170801.pdf Vision4CE CHARM-100] enclosure<br />
<br />
=== Expansion Boards ===<br />
<br />
:* Auvidea [http://auvidea.eu/j20/ J20] 6-camera module<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-H2C ]] HDMI to CSI expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-SDI ]] SDI to CSI expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-8eyes ]] 4 or 8 cameras ADAS expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-AUDIO ]] Sound card expansion board<br />
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-DSPK ]] Digital speaker and MIC expansion board<br />
:* [[BeadaPanel#BeadaPanel_for_NVIDIA_Jetson_Dev._Board|BeadaPanel USB Display+WiFi+Storage 3-In-1 Companion Kit]]<br />
<br />
=== Other ===<br />
<br />
:* Aeryon [https://www.aeryon.com/skyranger/r80/ SkyRanger R80] drone<br />
:* [http://black.ai black.ai] perception platform<br />
:* RidgeRun [https://developer.ridgerun.com/wiki/index.php?title=Tegra_X2_or_TX2 GStreamer and Multimedia Solutions]<br />
:* [https://www.skydio.com/ Skydio 2] drone<br />
<br />
<br /><br />
<br />
= Getting Help = <br />
If you have a technical question or bug report, please visit the '''[https://devtalk.nvidia.com/default/board/188/jetson-tx2/ DevTalk Developer Forums]''' and search or start a topic.<br />
<br />
We summarize some useful topics in http://elinux.org/Jetson_TX2/TX2_Issue page.<br />
<br />
See the official '''[https://developer.nvidia.com/embedded/support Support]''' page on Embedded Developer Zone for warranty and RMA information: https://developer.nvidia.com/embedded/support<br />
<br />
For [https://store.nvidia.com NVIDIA webstore] Customer Service, please see the [https://store.nvidia.com/store/nvidia/en_US/help/ThemeID.326200 My Account] page or contact 1-800-797-6530.</div>
Mschenk