JETSON NANO TX2 NX

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JETSON-TX2-NX-DEV-KIT
JETSON NANO TX2 NX.jpg
Jetson TX2 NX
Jetson-TX2-NX-1.jpg
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Overview

Note

If you purchased the kit with a module provided by Waveshare, the system has been pre-burned on the matching EMMC when it leaves the factory.
The pre-burned image does not have an SDK installed, and users need to download and install SDK plug-ins such as Cuda by themselves.

Introduction

NVIDIA Jetson TX2 NX delivers next-generation AI performance for entry-level embedded and edge products. It is s small and low-power, which makes it ideal for your next AI solution for manufacturing, transfer learning, the retail industry, agriculture, and life sciences. Pre-trained AI models, transfer learning toolkits, and the NVIDIA JetPack SDK help you bring powerful products to market quickly.

Jetson TX2 NX Specification

GPU NVIDIA Pascal architecture with 256 NVIDIA CUDA® cores
CPU Combination of dual-core NVIDIA Denver 2 64-bit CPU and quad-core Arm Cortex-A57 MPCore processor
Memory 4 GB 128-bit LPDDR4 51.2 GB/sec
Storage 16 GB eMMC 5.1 Flash / M.2 M KEY NVME SSD Interface
Video Encoding 3 x 4K30 | 4 x 1080p60 | 8 x 1080p30 (H.265)

1 x 4K60 | 3 x 4K30 | 7 x 1080p60 | 14 x 1080p30 (H.264)

Video Decoding 4 x 4p30 | 7 x 1080p60 | 14 x 1080p30

(H.265 and H.264)

Camera

Up to 5 cameras (support up to 12 via virtual channels)
12-Ch MIPI CSI-2 (3 x 4 or 5 x 2)
D-PHY 1.2 (up to 30 Gbps)

Networking

Wi-Fi requires an external chip
10/100/1000 BASE-T Ethernet

Display

2 x Multimode DP 1.2/eDP 1.4/HDMI 2.0
1 x 2 DSI (1.5Gbps/channel)

UPHY 1 x 1 (PCIe Gen3) + 1 x 4 (PCIe Gen4), 1 x USB 3.0, 2 x USB 2.0
IO 3 x UART, 2 x SPI, 2 x I2S, 4 x I2C, multiple GPIO headers

JETSON TX2 NX DEV KIT Onboard Resources

JETSON-TX2-NX-DEV-KIT01.png

  1. Jetson TX2 NX Module
  2. 40PIN GPIO Headers
  3. Micro USB Interface: For USB data transmission
  4. Gigabit Ethernet Port: 10/100/1000Base-T self-adaptive Ethernet port
  5. 4-Ch USB 3.0 port
  6. HDMI Interface
  7. DisplayPort Interface
  8. DC Power Port: For 12~21V power input
  9. 2-Ch MIPI CSI Camera Port
  10. Fan Header

Dimensions

JETSON-TX2-NX-DEV-KIT02.jpg

System Programming

Note that JETSON Xavier NX DEV KIT and JETSON TX2 NX DEV KIT are paired with the official 16eMMC version of Jetson Xavier NX 16GB/8GB core board or Jetson TX2 NX core board, without SD card slot. Therefore, the programming system needs to use the ubuntu 18.04 host, and use the SDK Manager tool to program.

Host Environment Configuration

  • Programming environment: Ubuntu18.04 host (or virtual device).

In order to download resources, the ubuntu18.04 host used for programming needs to reserve about 100G of memory.

In order to download resources and program the system normally in the future, please click JOIN in the upper right corner of the NVIDIA DEVELOPER website to register an account first.
  • Download the deb file to the ubuntu computer, then copy the deb file to the user's home directory.
  • Open a terminal and run the following program to install sdk manager.
sudo apt install ./sdkmanager_[version]-[build#]_amd64.deb

Note: the [version]-[build#] in the instruction is changed to the actual downloaded file name.

Hardware Configuration (enter recovery mode)

JETSON-XAVIER-NX-DEV-KIT05.jpg

  • Use jumper caps or Dupont wires to short-circuit the FC REC and GND pins, as shown in the figure above, at the bottom of the module.
  • Connect the DC power supply to the circular power port and wait for a while.
  • Use a USB cable (note that it is a data cable) to connect the Micro USB port of the Jetson board to the Ubuntu host.

System Programming

  • Open the terminal of the Ubuntu computer and run SDK manager to open the software.
  • Log in.
  • If Jetson Nano is identified normally, the SDK manager will detect and prompt for options.

JETSON-XAVIER-NX-DEV-KIT006.png

  • Select the JetsonTX2 NX option for the development board type, and in the JetPack option, select the latest supported system, uncheck Host PC, and then click Continue).

JETSON-XAVIER-NX-DEV-KIT116.png

  • Select Jetson OS, and remove the option of Jetson SDK Components. Check the first agreement at the bottom.

JETSON-XAVIER-NX-DEV-KIT07.png

  • Finally, click "Continue" and wait for the programming to finish.
    • Starting from JetPack 4.6.1, the "preconfig" window will pop up when using SDK Manager to program the system.
    1. Here, the development board type is selected by default. Be careful not to make a mistake when selecting the type of development board earlier.
    2. Here select Manual Setup-Jetson... (Different motherboard suffix prompts are different.)
    3. Here you can choose runtime or preconfig. If you choose runtime, you need to manually configure the system (username, password, language, etc.) later. If you choose preconfig, you can fill in the username and password (you can define it yourself), and nano will be automatically configured during the startup process.

JETSON-XAVIER-NX-DEV-KIT118.png

  • After the programming is finished, remove the jumper cap of the carrier board, connect to the monitor, power on again, and follow the prompts to configure the boot (if it is pre-config set, it will directly enter the system after power on).

Set The System To Boot From SSD

  1. Connect SSD to Jetson nano TX2 NX, check the device number of SSD, open Jetson nano TX2 NX terminal and input 
    ls /dev/nvme*
    For example, you will see nvme0, nvme0n1.

    Identification.png

  2. Format the SSD 
    sudo mkfs.ext4 /dev/nvme0n1

    Formatting succeeded.
    Format.png

  3. Modify the startup path 
    sudo vi /boot/extlinux/extlinux.conf
    • Copy the above content and place it under the red box of the file and comment it out with # to prevent modification errors
    • Change the LABEL primary in the uncommented copied content to LABLE NX
    • Find the statement APPEND ${cbootargs} quiet root=/dev/mmcblk0p1 rw rootwait rootfstype=ext4 console=ttyS0,115200n8 console=tty0, change mmcblk0p1 to nvme0n1 and save
    • Then the primary on the second line is changed to NX

    NX-configuration.png

  4. Mount SSD 
    sudo mount /dev/nvme0n1 /mnt
  5. Copy the system to SSD (please wait patiently for no information to be printed during this process) 
    sudo cp -ax / /mnt
  6. Unmount the SSD after the copy is complete (do not remove the SSD) 
    sudo umount /mnt/
  7. Restart the system 
    sudo reboot
  8. Enter 
    df -h

    Show boot from SSD
    Enable90.png

SDK installation

Jetpack mainly includes system images, libraries, APIs, developer tools, examples, and some documentation. In the SDK Manager software, we first install the OS, which is the system image, and the uninstalled part is the SDK, as shown below:
SDK.jpg
The SDK includes TensorRT, cuDNN, CUDA, Multimedia API, Computer Vision, and Developer Tools.

  • TensorRT: High-performance deep learning inference runtime for image classification, segmentation, and object detection neural networks, which speeds up deep learning inference and reduces convolutional and deconvolutional neural network operations time memory usage.
  • cuDNN: The CUDA deep neural network library provides high-performance primitives for deep learning frameworks, including support for convolutions, activation functions, and tensor transformations.
  • CUDA: The CUDA toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. The toolkit includes compilers for NVIDIA GPUs, math libraries, and tools for debugging and optimizing application performance.
  • multimedia API : Jetson Multimedia API provides a low-level API for flexible application development.
  • Computer Vision: VPI (Vision Programming Interface) is a software library that provides computer vision/image processing algorithms implemented on PVA1 (Programmable Vision Accelerator), GPU and CPU, of which OpenCV is used for computer The leading open source library for vision, image processing, and machine learning, now featuring GPU acceleration for real-time operations, with VisionWorks2, a software development kit for computer vision (CV) and image processing.
  • Developer Tools: The Developer Tools CUDA toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. The toolkit includes compilers for NVIDIA GPUs, math libraries, and tools for debugging and optimizing application performance.

The above are some functions of the SDK. When the previous system is installed, only the basic system is installed. Other JetPack SDK components, such as CUDA, need to be further installed after the system starts normally. Here are the steps to install the SDK. If you want to install this part, please ensure that the TF card or USB flash drive is the main system, because the downloaded content may cause the EMMC disk capacity to run out.

Use SDK Manager to install

When using the SDK Manager to install the SDK, there is no need to set the nano to recovery mode, that is, there is no need to short-circuit the pins.

  • Power on and start Nano normally.
  • After Jetson Nano enters the system and starts normally, connect the Micro USB interface of Jetson Nano to the Ubuntu host with a USB data cable.
  • Ubuntu host computer runs sdkmanager command to open SDK Manager (you need to install SDK Manager first).
  • Similar to the previous operation of burning the system, the difference is that in the step, do not check the OS option, but check the SDK' option, and then continue to the installation.
  • After downloading resources, a pop-up window will prompt you to fill in the username and password, just fill in the username and password of the nano system.
  • Wait for the SDK to be installed successfully.

Use the command to install

Users who do not have ubuntu or a virtual machine can choose to install directly on Jetson Nano by the following instructions. 

sudo apt update
sudo apt install nvidia-jetpack

FAN

Not that fan speed adjustment requires 4 wires.

sudo sh -c 'echo 255 > /sys/devices/pwm-fan/target_pwm'
# Among them, 255 is the maximum speed, 0 is the stop, and the speed can be modified by the value
cat /sys/class/thermal/thermal_zone0/temp
# Get the CPU temperature, you can intelligently control the fan through the program
#The system has its own temperature control system, and manual control is not required in unnecessary situations

Resource

NVIDIA Official Resources

Software

Support

If you require technical support, please go to the Support page and open a ticket.

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