Difference between revisions of "Jetson TX2"

From eLinux.org
Jump to: navigation, search
(→‎Guides and Tutorials)
(→‎Cameras)
 
(103 intermediate revisions by 14 users not shown)
Line 1: Line 1:
  
−
NVIDIA's [https://developer.nvidia.com/embedded-computing 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.
+
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.
  
 
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.
 
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.
  
−
Jetson TX2 is available as the '''[[#Jetson TX2 Module|module]]''', '''[[#Jetson TX2 Developer Kit|developer kit]]''', and in compatible '''[[#Ecosystem Products|ecosystem]]''' products.  See wiki of previous Jetson's [[Jetson|here]].
+
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]].
  
 
{| style="color: black; background-color: #ffffff; width: 600px;"
 
{| style="color: black; background-color: #ffffff; width: 600px;"
Line 53: Line 53:
  
 
=== Software Support ===
 
=== Software Support ===
−
<div style="width:50%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
+
<div style="width:40%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
−
* [https://developer.nvidia.com/embedded/jetpack JetPack 3.1]
+
* [https://developer.nvidia.com/embedded/jetpack JetPack 4.2.2]
−
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R28.1] (L4T)
+
* [https://developer.nvidia.com/embedded/linux-tegra Linux4Tegra R32.2.1] (L4T)
−
* Linux kernel 4.4
+
* Linux kernel 4.9
−
* Ubuntu 16.04 aarch64
+
* Ubuntu 18.04 aarch64
−
* CUDA Toolkit 8
+
* [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] 10.0.326
−
* cuDNN v6.0
+
* [https://developer.nvidia.com/cudnn cuDNN] 7.5.0
−
* [https://developer.nvidia.com/tensorrt TensorRT] 2.1
+
* [https://developer.nvidia.com/tensorrt TensorRT] 5.1.6
 +
* [https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html TensorFlow] 1.14.0
 
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6
 
* [https://developer.nvidia.com/embedded/visionworks VisionWorks] 1.6
−
* OpenCV4Tegra 2.4.13-17
+
* OpenCV 3.3.1
−
* OpenGL 4.5 / OpenGL ES 3.1
+
* OpenGL 4.6 / OpenGL ES 3.2.5
 +
* Vulkan 1.1.1
 +
* [http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-32-1 L4T Multimedia API] (Argus 0.97)
 +
* GStreamer 1.14.1
 
* V4L2 media controller support
 
* V4L2 media controller support
−
* GStreamer 1.8.2
+
* [https://developer.nvidia.com/nsight-systems NVIDIA Nsight Systems] 2019.4
−
* [https://developer.nvidia.com/tegra-system-profiler Tegra System Profiler] 3.7
+
* [https://developer.nvidia.com/nsight-graphics NVIDIA Nsight Graphics] 2019.2
−
* [https://developer.nvidia.com/tegra-graphics-debugger Tegra Graphics Debugger] 2.3
+
* [https://developer.nvidia.com/nsight-compute NVIDIA Nsight Compute] 1.0
 
</div>
 
</div>
 +
 +
See the '''[[Jetson Zoo]]''' for more software packages to install on top of JetPack.
  
 
{| style="color: black; background-color: #ffffff; width: 575px;"
 
{| style="color: black; background-color: #ffffff; width: 575px;"
Line 76: Line 82:
 
{{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>]''
 
{{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>]''
 
|}
 
|}
 +
 +
== Jetson TX2i Module ==
 +
 +
[[File:Jetson TX2i Module and TTP 800px.png|600px]]
 +
 +
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.
 +
 +
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 />
Line 97: Line 111:
  
 
=== Getting Started ===
 
=== Getting Started ===
−
* Get the latest development software for PC and TX1 by using '''[https://developer.nvidia.com/embedded/jetpack JetPack]'''.<br />
+
* Get the latest development software for PC and TX2 by using '''[https://developer.nvidia.com/embedded/jetpack JetPack]'''.<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 />
 
* 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 />
 
* 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.
 
* 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.
Line 111: Line 125:
 
= Platform Documentation =
 
= Platform Documentation =
  
−
NVIDIA has [https://developer.nvidia.com/embedded-computing released] comprehensive documentation and reference designs for the Jetson TX1 module and devkit. <br />
+
NVIDIA has [https://developer.nvidia.com/embedded-computing released] comprehensive documentation and reference designs for the Jetson TX2 module and devkit. <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.  
 
* '''[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.  
Line 122: Line 136:
 
* '''[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
 
* '''[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
 
* '''[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
 
* '''[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
−
* '''[https://developer.nvidia.com/embedded/dlc/l4t-documentation-27-1 L4T Kernel Docs]''' {{spaces|11}} documentation for L4T kernel developers (including V4L2/camera drivers).
+
* '''[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.
−
* '''[http://developer.nvidia.com/embedded/dlc/l4t-multimedia-api-reference-27-1 Multimedia API Reference]''' {{spaces|8}} documentation to Argus camera API and V4L2 media codecs
+
* '''[https://developer.nvidia.com/embedded/dlc/l4t-documentation-28-2 L4T Kernel Docs]''' {{spaces|11}} documentation for L4T kernel developers (including V4L2/camera drivers).
−
* '''[http://developer.nvidia.com/embedded/dlc/l4t-accerated-gstreamer-guide-27-1 Accelerated GStreamer Guide]''' {{spaces|1}} example gstreamer pipelines for accessing H.264/H.265 hardware video codec.
+
* '''[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
 +
* '''[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.
  
 
Above is a partial list of documents.
 
Above is a partial list of documents.
 
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 />
 
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 />
  
 
= Guides and Tutorials =
 
= Guides and Tutorials =
Line 138: Line 153:
 
Please see [http://elinux.org/Jetson_TX1#System_Tools Jetson TX1 Wiki] for similar entries that also apply to TX2.
 
Please see [http://elinux.org/Jetson_TX1#System_Tools Jetson TX1 Wiki] for similar entries that also apply to TX2.
  
−
:* [[Jetson/TX2_Cloning|Cloning & Restore]]
+
<div style="width:75%;column-count:2;-moz-column-count:2;-webkit-column-count:2">
 +
:* [[Jetson/Clone|Cloning & Restore]]
 +
:* [https://github.com/jtagxhub/jetpack-agx-build Jetson TX2 Build Assistant Scripts]
 +
:* [[Jetson/FAQ/BSP|BSP FAQ]]
 +
:* [https://developer.nvidia.com/embedded/downloads#?search=Factory%20Image Factory Images]
 
:* [[Jetson/TX2_DTB|Setting the DTB]]
 
:* [[Jetson/TX2_DTB|Setting the DTB]]
 +
:* [[Jetson/TX2_SPI|Enabling the SPI Port]]
 
:* [http://www.jetsonhacks.com/2017/03/25/build-kernel-and-modules-nvidia-jetson-tx2/ Building Kernel and Modules]
 
:* [http://www.jetsonhacks.com/2017/03/25/build-kernel-and-modules-nvidia-jetson-tx2/ Building Kernel and Modules]
 
:* [[Jetson/TX2_USB|Enabling USB on Custom Carriers]]
 
:* [[Jetson/TX2_USB|Enabling USB on Custom Carriers]]
 
:* [[Jetson/TX2_eMMC|Maximizing RootFS Partition on eMMC]]
 
:* [[Jetson/TX2_eMMC|Maximizing RootFS Partition on eMMC]]
 
:* [http://www.jetsonhacks.com/2017/03/25/nvpmodel-nvidia-jetson-tx2-development-kit/ nvpmodel] - dynamic performance profiles
 
:* [http://www.jetsonhacks.com/2017/03/25/nvpmodel-nvidia-jetson-tx2-development-kit/ nvpmodel] - dynamic performance profiles
 +
:* [https://gist.github.com/JasonAtNvidia/e03e6675849d1d4049b85ea41efb2171 TX2 GPU support in Docker] - script for GPU from within Docker
 
:* [https://github.com/Technica-Corporation/Tegra-Docker Tegra-Docker]
 
:* [https://github.com/Technica-Corporation/Tegra-Docker Tegra-Docker]
 
:* [[Jetson/TX2|Ubuntu Base]] Minimal footprint (500Mb with OS only) using [https://wiki.ubuntu.com/Base Ubuntu Base]
 
:* [[Jetson/TX2|Ubuntu Base]] Minimal footprint (500Mb with OS only) using [https://wiki.ubuntu.com/Base Ubuntu Base]
 
:* [http://elinux.org/Boot_from_sd Boot from SD card]
 
:* [http://elinux.org/Boot_from_sd Boot from SD card]
 +
:* [[Jetson TX2/r28 Display debug|Display Driver Debugging r28]]
 +
:* [https://medium.com/@ynd/getting-swift-to-run-on-nvidia-jetson-tx2-ai-computing-platform-1d9bcd6559dc Getting Swift to Run on TX2]
 +
:* [https://github.com/rbonghi/jetson_easy jetson_easy] - automatic setup/scripting
 +
:* [https://github.com/rbonghi/jetson_stats jetson_stats] - jtop, service and other tools
 +
:* [https://sites.google.com/site/jetsontricks/ v4l2loopback,rtsp,screencapture,misc]
 +
:* [https://devtalk.nvidia.com/default/topic/1057158/jetson-tx2/guide-to-enabling-mcp251x-mcp2515-on-the-tx2-spi-can-/ Enabling MCP2515 SPI-CAN Device]
 +
</div>
 +
 +
=== Robotics ===
 +
 +
:* [https://github.com/NVIDIA-Jetson NVIDIA Jetson GitHub] {{spaces|10}} (open-source robotics projects with deep learning)
 +
:* [https://github.com/NVIDIA-Jetson/redtail NVIDIA Redtail GitHub] {{spaces|9}} (end-to-end deep learning drone for ROS)
 +
:* [https://developer.nvidia.com/embedded/community/reference-platforms Jetson Reference Platforms] {{spaces|1}} (off-the-shelf robots with TX1/TX2)
 +
:* [[Jetson/FRC_Setup|FIRST FRC Configuration]] {{spaces|8}} (setup guide for FIRST Robotics)
 +
:* [https://www.chiefdelphi.com/media/papers/download/4758 FIRST FRC Neural Networks] (Zebracorns team #900 [https://www.chiefdelphi.com/media/papers/3274 object tracking])
 +
:* [https://www.chiefdelphi.com/media/papers/download/5169 ROS for FRC Whitepaper] {{spaces|5}} (Zebracorns team #900 Vision [https://github.com/FRC900/2017VisionCode GitHub])
 +
:* [http://www.jetsonhacks.com/2017/03/27/robot-operating-system-ros-nvidia-jetson-tx2/ Installing ROS Kinetic (TX2)] {{spaces|1}} (JetsonHacks guide)
 +
:* [https://github.com/mit-racecar/particle_filter Fast SLAM Particle Filter] {{spaces|6}} (Accelerated Localization using Raycasting)
 +
:* [http://ardupilot.org/dev/docs/companion-computer-nvidia-tx2.html Connecting Pixhawk and TX2] (Autopilot with MAVLink Interface)
 +
:* [https://github.com/DiegoHerrera1890/Pixhawk-connected-to-Jetson-Tx2-devkit Running MAVROS with TX2 and PixHawk 4] (TX2/ROS setup with MAVLink)
  
 
=== Computer Vision ===
 
=== Computer Vision ===
  
 
:* NVIDIA [https://developer.nvidia.com/embedded/learn/tutorials#collapseOne OpenCV 101] - screencast tutorials
 
:* NVIDIA [https://developer.nvidia.com/embedded/learn/tutorials#collapseOne OpenCV 101] - screencast tutorials
 +
:* [https://github.com/AastaNV/JEP/blob/master/script/install_opencv3.4.0.sh OpenCV-3.4.0 for TX2] building script
 
:* [http://www.jetsonhacks.com/2017/04/05/build-opencv-nvidia-jetson-tx2/ Build OpenCV for TX2] (JetsonHacks)
 
:* [http://www.jetsonhacks.com/2017/04/05/build-opencv-nvidia-jetson-tx2/ Build OpenCV for TX2] (JetsonHacks)
 
:* [http://docs.opencv.org/3.2.0/d6/d15/tutorial_building_tegra_cuda.html Building OpenCV 3.2 with CUDA for Tegra]
 
:* [http://docs.opencv.org/3.2.0/d6/d15/tutorial_building_tegra_cuda.html Building OpenCV 3.2 with CUDA for Tegra]
 
:* [https://developer.nvidia.com/embedded/learn/tutorials#collapseVisionWorks VisionWorks training]
 
:* [https://developer.nvidia.com/embedded/learn/tutorials#collapseVisionWorks VisionWorks training]
−
:* [https://developer.ridgerun.com/wiki/index.php?title=Gstreamer_pipelines_for_Tegra_X2 gstreamer Pipelines for TX2]
 
−
:* [http://www.jetsonhacks.com/2017/03/27/robot-operating-system-ros-nvidia-jetson-tx2/ Installing ROS Kinetic] (JetsonHacks)
 
  
 
=== Deep Learning ===
 
=== Deep Learning ===
Line 161: Line 201:
 
:* [https://developer.nvidia.com/embedded/twodaystoademo NVIDIA Two Days to a Demo] {{spaces|1}} (DIGITS/TensorRT)
 
:* [https://developer.nvidia.com/embedded/twodaystoademo NVIDIA Two Days to a Demo] {{spaces|1}} (DIGITS/TensorRT)
 
:* Caffe {{spaces|1}} (BVLC [https://github.com/BVLC/caffe/wiki/Model-Zoo Model Zoo])
 
:* Caffe {{spaces|1}} (BVLC [https://github.com/BVLC/caffe/wiki/Model-Zoo Model Zoo])
−
:** [https://github.com/NVIDIA/caffe/tree/experimental/fp16 NVcaffe fp16 branch] {{spaces|1}} ([https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-nvcaffe.md Install Guide])
+
:** [https://github.com/nvidia/caffe NVcaffe FP16] {{spaces|1}} ([https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-nvcaffe.md Install Guide])
 
:** [http://www.jetsonhacks.com/2017/03/24/caffe-deep-learning-framework-nvidia-jetson-tx2/ Caffe Installation] {{spaces|1}} (JetsonHacks)
 
:** [http://www.jetsonhacks.com/2017/03/24/caffe-deep-learning-framework-nvidia-jetson-tx2/ Caffe Installation] {{spaces|1}} (JetsonHacks)
 
:* Caffe2 {{spaces|1}} ([https://github.com/caffe2/caffe2 github.com/caffe2])
 
:* Caffe2 {{spaces|1}} ([https://github.com/caffe2/caffe2 github.com/caffe2])
 
:* [https://github.com/chitoku/installDeepvizJetson Deep Visualization Toolbox] install script
 
:* [https://github.com/chitoku/installDeepvizJetson Deep Visualization Toolbox] install script
 +
:* [https://github.com/peterlee0127/tensorflow-tx2 TensorFlow] install for JetPack 3.1
 +
:* [http://www.jetsonhacks.com/2017/04/02/tensorflow-on-nvidia-jetson-tx2-development-kit/ TensorFlow] post for JetPack 3.0 {{spaces|1}} (JetsonHacks)
 
:* [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])
 
:* [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])
−
:* [http://www.jetsonhacks.com/2017/04/02/tensorflow-on-nvidia-jetson-tx2-development-kit/ TensorFlow] post {{spaces|1}} (JetsonHacks)
+
:* [https://developer.ridgerun.com/wiki/index.php?title=GstInference RidgeRun's GstInference]
 +
:* [https://developer.ridgerun.com/wiki/index.php?title=R2Inference RidgeRun's R2Inference]
 +
:* [https://github.com/AastaNV/JEP/tree/master/script/TensorFlow_1.6 TensorFlow] script and pip wheel for JetPack 3.2 DP
 
:* [https://github.com/dusty-nv/jetson-reinforcement/blob/master/CMakePreBuild.sh Torch7]  {{spaces|1}} install script
 
:* [https://github.com/dusty-nv/jetson-reinforcement/blob/master/CMakePreBuild.sh Torch7]  {{spaces|1}} install script
 
:* [https://gist.github.com/dusty-nv/ef2b372301c00c0a9d3203e42fd83426 pyTorch] {{spaces|0}} install script
 
:* [https://gist.github.com/dusty-nv/ef2b372301c00c0a9d3203e42fd83426 pyTorch] {{spaces|0}} install script
 
:* [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]
 
:* [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]
 
:* [https://github.com/Abaco-Systems/jetson-inference-gv jetson-inference-gv] GigEVision / RTP streaming video (Ross Newman)
 
:* [https://github.com/Abaco-Systems/jetson-inference-gv jetson-inference-gv] GigEVision / RTP streaming video (Ross Newman)
−
:* [https://github.com/S4WRXTTCS/jetson-inference jetson-inference-cards] playing card recognition by S4WRXTTCS
+
:* [https://github.com/S4WRXTTCS/jetson-inference jetson-inference-cards] {{spaces|1}} (playing card recognition by S4WRXTTCS)
−
</div><br />
+
:* [https://github.com/AastaNV/Face-Recognition face-recognition] {{spaces|0}} (face detection with TensorRT plugin API by AastaNV)
 +
:* [https://github.com/NVIDIA-Jetson/JEP_ChatBot ChatBot] {{spaces|2}} (TensorFlow→TensorRT inferencing workflow by AastaNV)
 +
:* [https://github.com/NVIDIA-Jetson NVIDIA GitHub] {{spaces|2}} (open-source robotics/DL projects)
 +
:* [https://github.com/NVIDIA-Jetson/redtail NVIDIA Redtail] {{spaces|2}} (end-to-end deep learning drone for ROS)
 +
:* 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)
 +
:* [https://heyjetson.com/ Hey, Jetson!] {{spaces|2}} (Automatic Speech Recognition using CNN/RNN)
 +
</div>
 +
 
 +
=== Multimedia ===
 +
* [https://developer.ridgerun.com/wiki/index.php?title=Xavier/GStreamer_Pipelines Gstreamer Pipelines for AGX Xavier]
 +
* [https://developer.ridgerun.com/wiki/index.php?title=GstInterpipe RidgeRun's GstInterpipe] (GStreamer plug-in for communication between two or more independent pipelines)
 +
* [https://developer.ridgerun.com/wiki/index.php?title=GstWebRTC RidgeRun's GstRRWebRTC] (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)
 +
* [https://developer.ridgerun.com/wiki/index.php?title=GstRtspSink RidgeRun's GstRTSPSink] (GStreamer element for high performance streaming to multiple computers using the RTSP/RTP protocols)
 +
* [https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Daemon RidgeRun's Gstreamer Daemon - GstD] (GStreamer framework for controlling audio and video streaming using TCP connection messages)
 +
* [http://developer.ridgerun.com/wiki/index.php?title=GstCUDA RidgeRun's GstCUDA] (RidgeRun CUDA ZeroCopy for GStreamer)
 +
* [https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Pan_Tilt_Zoom_and_Rotate_Element RidgerRun's GstPTZR] (GStreamer Pan Tilt Zoom and Rotate Element)
 +
* [https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Color_Transfer RidgeRun's GstColorTransfer] (GStreamer plug-in that transfers the color scheme from a reference to a target image)
 +
 
 +
=== Camera Info ===
 +
*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].
 +
*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
 +
*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
 +
 
 +
=== V4L2 drivers for cameras ===
 +
 
 +
*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]
 +
 
 +
=== Design FAQs ===
 +
 
 +
There are some useful FAQs for Jetson TX2 design, link is [[Jetson_TX2/FAQ|here]].
 +
<br />
 +
<br />
  
 
= Ecosystem Products =
 
= Ecosystem Products =
  
−
The following are 3rd-party carriers, enclosures, drones, and accessories available for Jetson TX2.  
+
The following are 3rd-party carriers, enclosures, expansion boards, and accessories available for Jetson TX2.
 +
 
 +
For the latest list of TX2 compatible products, please visit the Jetson Ecosystem [https://developer.nvidia.com/EMBEDDED/jetson-partner-supported-cameras?t1_supported-jetson-products=TX2 Supported Cameras] and [https://developer.nvidia.com/embedded/community/jetson-partner-products?t1_supported-jetson=TX2 Carrier Boards and Production Systems] pages.
  
 
Please see additional backwards-compatible [[Jetson_TX1#Ecosystem_Products|Ecosystem Products for TX1]].
 
Please see additional backwards-compatible [[Jetson_TX1#Ecosystem_Products|Ecosystem Products for TX1]].
 
<br />
 
<br />
  
−
<div style="width:70%;column-count:3;-moz-column-count:3;-webkit-column-count:3">
+
=== Cameras ===
 +
* Stereolabs ZED Sensors
 +
** Stereolabs [https://www.stereolabs.com/zed-2i/ Zed 2i RGB Camera] ( 2.2K resolution, Up to a 120° Wide-angle field of view, IP66 certified, Up to 35m Depth Range, Full Compatibility with Nvidia Orin/Jetson Xavier NX/AGX/TX2/Nano)
 +
** Stereolabs [https://www.stereolabs.com/zed-2/ Zed 2 RGB Camera] ( 2.2K resolution, Up to a 120° Wide-angle field of view, Up to 20m Depth Range, Full Compatibility with Nvidia Orin/Jetson Xavier NX/AGX/TX2/Nano)
 +
** Stereolabs [https://www.stereolabs.com/zed-mini/ Zed Mini RGB Camera] ( 2.2K resolution, Up to a 90° Wide-angle field of view, Up to 15m Depth Range, Full Compatibility with Nvidia Orin/Jetson Xavier NX/AGX/TX2/Nano, Specially designed for AR/VR Applications)
 +
 
 +
:* e-con Systems™ [https://www.e-consystems.com/nvidia-jetson-camera.asp#jetson-tx2-tx1-cameras NVIDIA Jetson TX2 cameras]
 +
:** SmarteCAM [https://www.e-consystems.com/smart-camera.asp IP66 rated ready-to-deploy artificial intelligence smart camera]  with powerful AI processing capabilities with an onboard NVIDIA Jetson TX2 CPU and 256 core GPU which can perform all image processing and analytics indigenously without the connectivity or power of cloud
 +
:** 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]
 +
:** e-con Systems™ [https://www.e-consystems.com/13mp-autofocus-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 AF MIPI Jetson TX1/TX2 Camera]
 +
:** 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]
 +
:** e-con Systems™ [https://www.e-consystems.com/2MP-HDR-Jetson-TX2-TX1-Camera-Board.asp 2MP AR0230AT MIPI Jetson TX1/TX2 Camera]
 +
:** e-con Systems™ [https://www.e-consystems.com/jetson-tx2-ultra-low-light-camera-board.asp 2MP SONY IMX290 MIPI Jetson TX1/TX2 Camera]
 +
:** e-con Systems™ [https://www.e-consystems.com/13mp-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 MIPI Jetson TX1/TX2 Camera]
 +
:** 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]
 +
:** e-con Systems™ [https://www.e-consystems.com/13mp-nvidia-jetson-tx1-camera-board.asp 13MP AR1820 MIPI Jetson TX1/TX2 Camera]
 +
:** e-con Systems™ [https://www.e-consystems.com/3MP-Jetson-TX1-Camera-board.asp 3.4 MP AR0330 MIPI Jetson TX1/TX2 Camera]
 +
:** 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]
 +
 
 +
:* Allied Vision MIPI CSI-2 (one open-source CSI-2 driver for all cameras on [https://github.com/alliedvision/linux_nvidia_jetson Github.com])
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-050.html Alvium 1500 C-050] 0.5MP PYTHON 480
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-120.html Alvium 1500 C-120] 1.2MP AR0135CS
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-210.html Alvium 1500 C-210] 2.1MP AR0521
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1500%20C-500.html Alvium 1500 C-500] 5MP AR0521
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-040.html Alvium 1800 C-040] 0.4MP Sony IMX287
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20C-158.html Alvium 1800 C-158] 1.6MP Sony IMX273
 +
:* Allied Vision USB3 Vision
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-040.html Alvium 1800 U-040] 0.4MP Sony IMX287
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-050.html Alvium 1800 U-050] 0.5MP PYTHON 480
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-120.html Alvium 1800 U-120] 1.2MP AR0135CS
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-158.html Alvium 1800 U-158] 1.6MP Sony IMX273
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-500.html Alvium 1800 U-500] 5MP AR0521
 +
:** [https://www.alliedvision.com/en/products/embedded-vision-cameras/detail/Alvium/1800%20U-501m%20NIR.html Alvium 1800 U-501m NIR] 5MP AR0522
 +
:* APPROPHO [http://www.appropho.com/products_en.html?type=36 TX1/TX2 Camera Solutions]
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-OV5640 ]] OV5640 M12 lens camera
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-OV5693 ]] OV5693 camera
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-H2C ]]  HDMI  to CSI2 board
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-H2C4K ]]  HDMI  to CSI2 4K board
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-SDI ]]  SDI  to CSI2 board
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-8eyes ]]  4 or 8 cameras ADAS  expansion board
 +
 
 +
:* Leopard Imaging [https://leopardimaging.com/product-category/nvidia-jetson-cameras/nvidia-tx1tx2-mipi-camera-kits/csi-2-mipi-cameras/ TX1/TX2 camera kits]
 +
:* Stereolabs [https://www.stereolabs.com/ ZED] (stereo camera)
 +
 
 +
=== Carriers ===
 +
 
 
:* Aetina [http://www.aetina.com.tw/wp-content/uploads/2017/04/eDM_ACE-N620_carrier/index.html N620] nano-ITX carrier
 
:* Aetina [http://www.aetina.com.tw/wp-content/uploads/2017/04/eDM_ACE-N620_carrier/index.html N620] nano-ITX carrier
−
:* Auvidea [http://auvidea.eu/j20/ J20] 6-camera module
 
 
:* Auvidea [https://auvidea.com/j100/ J100] carrier
 
:* Auvidea [https://auvidea.com/j100/ J100] carrier
 
:* Auvidea [https://auvidea.com/j106/ J106] carrier (6 camera)
 
:* Auvidea [https://auvidea.com/j106/ J106] carrier (6 camera)
Line 191: Line 312:
 
:* Auvidea [https://auvidea.com/j150/ J150] OpenGear blade
 
:* Auvidea [https://auvidea.com/j150/ J150] OpenGear blade
 
:* 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
 
:* 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
 +
:* Avermedia [https://www.avermedia.com/professional/product/ex731_aa_n1/overview EX731-AA] carrier
 +
:* Avermedia [https://www.avermedia.com/professional/product/ex713_aa/overview EX713-AA] carrier
 +
:* Bluetechnix [https://www.bluetechnix.com/en/products/multi-tof-platform/product/multi-tof-platform/ Multi-ToF platform]
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG008.asp?l1=GPU&l2=ASG008 Sprocket] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG008.asp?l1=GPU&l2=ASG008 Sprocket] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG003.asp?l1=GPU&l2=ASG003 Orbitty] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG003.asp?l1=GPU&l2=ASG003 Orbitty] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG006.asp?l1=GPU&l2=ASG006 Spacely] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG006.asp?l1=GPU&l2=ASG006 Spacely] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG007.asp?l1=GPU&l2=ASG007 Cogswell] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG007.asp?l1=GPU&l2=ASG007 Cogswell] carrier
 +
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG002.asp?l1=GPU&l2=ASG002 Elroy] carrier
 +
:* ConnectTech [http://connecttech.com/sub/Products/VPG003_GraphiteVPXTX1.asp?l1=GPU&l2=VPXTX1 3U VPX] card
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-Lite carrier]] with eDP, CSI, DSI, MicroHDMI etc
 +
:* Gumstix [https://store.gumstix.com/aerocore2-for-nvidia-jetson.html Aerocore2] drone carrier
 +
:* Realtimes [http://www.realtimes.cn/en/product/9001.html RTSO-9001] carrier
 +
:* Realtimes [http://www.realtimes.cn/en/product/RTSO9002.html RTSO-9002] carrier
 +
:* Realtimes [http://www.realtimes.cn/en/product/rtso-9003.html RTSO-9003] carrier
 +
:* Realtimes [http://www.realtimes.cn/en/product/products-8-55.html RTSS-Z5O3U] enclosure
 +
 +
=== Enclosures ===
 +
 +
:* Aaeon [http://www.aaeon.com/en/p/fanless-embedded-computers-boxer-8120ai BOXER-8120AI] enclosure
 +
:* Abaco [https://www.abaco.com/products/gvc1000 GVC1000] enclosure
 +
:* ADLINK [https://www.adlinktech.com/Products/Deep_Learning_Accelerator_Platform_and_Server/Inference_Platform/DLAP-201-JT2?lang=en DLAP-201-JT2] enclosure
 +
:* Advantech [https://www.advantech.com/products/9140b94e-bcfa-4aa4-8df2-1145026ad613/mic-7200/mod_19d7f198-a3f3-4975-ac87-e8facd1045b3 MIC-720AI] enclosure
 +
:* Axiomtek [http://www.axiomtek.com/Default.aspx?MenuId=Products&FunctionId=ProductView&ItemId=24544&upcat=144&C=eBOX560-900-FL#/ eBOX560-900-FL]
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ESG501.asp?l1=GPU&l2=ESG501 Rosie] enclosure
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ESG501.asp?l1=GPU&l2=ESG501 Rosie] enclosure
−
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG002.asp?l1=GPU&l2=ASG002 Elroy] carrier
 
 
:* ConnectTech [http://connecttech.com/sub/Products/ESG503.asp?l1=GPU&l2=ESG503 Rudi] enclosure
 
:* ConnectTech [http://connecttech.com/sub/Products/ESG503.asp?l1=GPU&l2=ESG503 Rudi] enclosure
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG001.asp?l1=GPU&l2=ASG001 Astro] carrier
 
:* ConnectTech [http://www.connecttech.com/sub/Products/ASG001.asp?l1=GPU&l2=ASG001 Astro] carrier
−
:* ConnectTech [http://connecttech.com/sub/Products/VPG003_GraphiteVPXTX1.asp?l1=GPU&l2=VPXTX1 3U VPX] card
+
:* Curtiss-Wright [https://www.curtisswrightds.com/products/electronic-systems/rugged-mission-computing/duracor-mission-computers/duracor-312.html Parvus DuraCor-312] rugged enclosure
−
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-Lite carrier]] with eDP, CSI, DSI, MicroHDMI Etc
+
:* MiiVii [https://en.miivii.com/index.php?s=index/category/index&id=107 S2] enclosure
−
:* RidgeRun [https://developer.ridgerun.com/wiki/index.php?title=Tegra_X2_or_TX2 GStreamer and Multimedia Solutions]  
+
:* MiiVii [https://en.miivii.com/index.php?s=index/category/index&id=106 EVO TX2] enclosure
−
:* e-con Systems [https://www.e-consystems.com/13mp-nvidia-jetson-tx2-camera-board.asp 13MP AR1335 MIPI Jetson TX2 Camera]
+
:* MiiVii [https://en.miivii.com/index.php?s=index/category/index&id=143 EVO TX2 GMSL2] enclosure
−
:* e-con Systems [https://www.e-consystems.com/autofocus-liquid-lens-nvidia-jetson-tx2-camera.asp 3.4 MP AF AR0330 MIPI Jetson TX2 Camera]
+
:* RapidProto [https://www.hazcam.io/collections/hazcam-kits/products/jetson-tx1-and-tx2-aluminium-enclosure Aluminum enclosure]
−
:* e-con Systems [https://www.e-consystems.com/13mp-nvidia-jetson-tx1-camera-board.asp 13MP AR1820 MIPI Jetson TX2 Camera]
+
:* Rebotnix [https://rebotnix.com/product/gustav/ GUSTAV] enclosure
−
:* e-con Systems [https://www.e-consystems.com/3MP-Jetson-TX1-Camera-board.asp 3.4 MP AR0330 MIPI Jetson TX2 Camera]
+
:* Silverstone [http://www.phoronix.com/scan.php?page=article&item=silverstone-pt13-mini&num=2 PT13] mini-ITX system
−
:* [http://www.phoronix.com/scan.php?page=article&item=silverstone-pt13-mini&num=2 Silverstone PT13] (mini-ITX enclosure)
+
:* SMP Robotics [https://smprobotics.com/technology_autonomous_mobile_robot/video_analytics_security_system/ T9 System] enclosure
−
</div>
+
:* Syslogic [https://www.syslogic.de/eng/ki-embedded-system-94630.shtml?parentPageId=94706 IPC/COMPACTA-2] TX2i enclosure
 +
:* Syslogic [https://www.syslogic.de/eng/deep-learning-rail-computer-92161.shtml IPC/COMPACTA-2] TX2i enclosure (railway system)
 +
:* Syslogic [https://www.syslogic.de/eng/ai-rugged-computer-jetson-tx2-99518.shtml?parentPageId=100092 RPC/COMPACTA-2] TX2i enclosure (IP67)
 +
:* [https://www.vision4ce.com/wp-content/uploads/2017/09/CHARM-100-170801.pdf Vision4CE CHARM-100] enclosure
 +
 
 +
=== Expansion Boards ===
 +
 
 +
:* Auvidea [http://auvidea.eu/j20/ J20] 6-camera module
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-AUDIO ]]  Sound card expansion board
 +
:* DCDZ(冬虫电子) [[Jetson/xcb-lite| XCB-DSPK ]]  Digital speaker and MIC expansion board
 +
:* [[BeadaPanel#BeadaPanel_for_NVIDIA_Jetson_Dev._Board|BeadaPanel USB Display+WiFi+Storage 3-In-1 Companion Kit]]
 +
 
 +
=== Other ===
 +
 
 +
:* Aeryon [https://www.aeryon.com/skyranger/r80/ SkyRanger R80] drone
 +
:* [http://black.ai black.ai] perception platform
 +
:* RidgeRun [https://developer.ridgerun.com/wiki/index.php?title=Tegra_X2_or_TX2 GStreamer and Multimedia Solutions]
 +
:* [https://www.skydio.com/ Skydio 2] drone
 +
 
 
<br />
 
<br />
  

Latest revision as of 05:36, 6 April 2022

NVIDIA 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.

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.

Jetson TX2 is available as the module, developer kit, and in compatible ecosystem products. See the wiki of other Jetson's here, including the latest Jetson AGX Xavier.

  Parallel ForAll — NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge


NVIDIA Jetson TX2 Module Devkit.png

Jetson TX2 Module

The Jetson TX2 module contains all the active processing components. The ports are broken out through a carrier board.

Below is a partial list of the module's features. Please see the Jetson TX2 Module Datasheet for the complete specifications.

Tegra Parker Block Diagram.png

Processing Components

  • dual-core NVIDIA Denver2 + quad-core ARM Cortex-A57
  • 256-core Pascal GPU
  • 8GB LPDDR4, 128-bit interface
  • 32GB eMMC
  • 4kp60 H.264/H.265 encoder & decoder
  • Dual ISPs (Image Signal Processors)
  • 1.4 gigapixel/sec MIPI CSI camera ingest
NVIDIA Jetson TX2 Module TTP.png

Ports & Peripherals

  • HDMI 2.0
  • 802.11a/b/g/n/ac 2×2 867Mbps WiFi
  • Bluetooth 4.1
  • USB3, USB2
  • 10/100/1000 BASE-T Ethernet
  • 12 lanes MIPI CSI 2.0, 2.5 Gb/sec per lane
  • PCIe gen 2.0, 1×4 + 1×1 or 2×1 + 1×2
  • SATA, SDcard
  • dual CAN bus
  • UART, SPI, I2C, I2S, GPIOs

Form-Factor

  • 400-pin Samtec board-to-board connector
  • dimensions: 50x87mm   (1.96" x 3.42")
  • Thermal Transfer Plate (TTP), -25C to 80C operating temperature
  • mass: 85 grams, including TTP
  • 5.5-19.6VDC input power (consuming 7.5W under typical load)

Software Support

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

  Parallel ForAll — JetPack 3.1 Doubles Jetson's Low-Latency Inference Performance

Jetson TX2i Module

Jetson TX2i Module and TTP 800px.png

There's an extended-temperature variant of the TX2 module available called Jetson TX2i that's intended for industrial environments. It has the same processing capabilities as TX2, with a rugged design.

For more info, see the FAQ "What changes for industrial environments does Jetson TX2i have compared to Jetson TX2?"


Jetson TX2 Developer Kit

The Jetson TX2 Developer Kit bundles together all the parts to get started, including:

NVIDIA Jetson TX2 Devkit Unbox.png

What's Included

  • mini-ITX Reference carrier board
  • Jetson TX2 Module
    • fan and heatsink (pre-assembled)
  • 5MP CSI camera module (with Omnivision OV5693)
  • WiFi/BT antennas
  • USB OTG adapter
  • 19VDC Power brick
  • AC Power cable

The design files for the reference carrier board and camera module are freely available for download.

Getting Started

Availability


Platform Documentation

NVIDIA has released comprehensive documentation and reference designs for the Jetson TX2 module and devkit.

  • Module Datasheet          the official module features, ports, signal pin-out, and package specifications.
  • Design Guide                  detailed technical design and layout information for creating OEM products.
  • DevKit Carrier Spec        design info about the reference carrier board from the devkit.
  • DevKit Design Files        schematics, layout, and design files for the devkit reference carrier board.
  • DevKit CAD Models        3D STEP file for reference carrier board, heatsink, camera board, and module.
  • Camera Design Files      schematics, layout, and design files for the devkit MIPI CSI-2 camera module.
  • Thermal Design Guide   mechanical specifications for designing active and passive cooling solutions.
  • TX1/TX2 Migration          guide to porting applications and hardware between Jetson TX1 and TX2
  • Battery Charger Guide   document for the design of battery charger
  • Tegra X2 (Parker) TRM   Technical Reference Manual for NVIDIA TX2 system-on-chip and register data.
  • L4T Kernel Docs             documentation for L4T kernel developers (including V4L2/camera drivers).
  • Multimedia API Reference          documentation to Argus camera API and V4L2 media codecs
  • Accelerated GStreamer Guide   example gstreamer pipelines for accessing H.264/H.265 hardware video codec.

Above is a partial list of documents. Please visit the Downloads Center at Embedded Developer Zone for the full list that's currently available.

Guides and Tutorials

This section contains recipes for following along on Jetson.

System Tools

Please see Jetson TX1 Wiki for similar entries that also apply to TX2.

Robotics

Computer Vision

Deep Learning

Multimedia

Camera Info

V4L2 drivers for cameras

Design FAQs

There are some useful FAQs for Jetson TX2 design, link is here.

Ecosystem Products

The following are 3rd-party carriers, enclosures, expansion boards, and accessories available for Jetson TX2.

For the latest list of TX2 compatible products, please visit the Jetson Ecosystem Supported Cameras and Carrier Boards and Production Systems pages.

Please see additional backwards-compatible Ecosystem Products for TX1.

Cameras

  • Stereolabs ZED Sensors
    • Stereolabs Zed 2i RGB Camera ( 2.2K resolution, Up to a 120° Wide-angle field of view, IP66 certified, Up to 35m Depth Range, Full Compatibility with Nvidia Orin/Jetson Xavier NX/AGX/TX2/Nano)
    • Stereolabs Zed 2 RGB Camera ( 2.2K resolution, Up to a 120° Wide-angle field of view, Up to 20m Depth Range, Full Compatibility with Nvidia Orin/Jetson Xavier NX/AGX/TX2/Nano)
    • Stereolabs Zed Mini RGB Camera ( 2.2K resolution, Up to a 90° Wide-angle field of view, Up to 15m Depth Range, Full Compatibility with Nvidia Orin/Jetson Xavier NX/AGX/TX2/Nano, Specially designed for AR/VR Applications)

Carriers

Enclosures

Expansion Boards

Other


Getting Help

If you have a technical question or bug report, please visit the DevTalk Developer Forums and search or start a topic.

We summarize some useful topics in http://elinux.org/Jetson_TX2/TX2_Issue page.

See the official Support page on Embedded Developer Zone for warranty and RMA information: https://developer.nvidia.com/embedded/support

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

Retrieved from "https://elinux.org/index.php?title=Jetson_TX2&oldid=564116"