1、Jetson Nano 官方资料下载
Jetson Nano Developer Kit官方介绍
Get-Started-With-Jetson-Nano-Devkit
Jetson-Nano-Dev-Kit-Sd-Card-Image
NVIDIA Jetson-Nano-Resources
Jetson Nano Wiki
Jetson Nano Upstream
图为科技Jetson Nano 运行通用案例分享(持续更新）
Jetson Nano 对应的DeepStream 2019Q2 才会发布，目前采用Nano 做的项目还是比较受限，期待DS 有更高的性能表现！
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2、Jetson Nano刷机
Nano MicroSD卡刷机工具：SDCardFormatter 和 balenaEtcher
Nano MicroSD卡系统镜像：jetson-nano-sd-r32.1-2019-03-18.zip (5.6GB)，但解压缩后大约12GB多，运行在Micro SD卡，因此SD卡务必16GB及以上且高速UHS（参考NV 说明)，否则刷TF卡镜像和系统软件运行时会慢如牛，你想砸了Nano或TF卡!
MicroSD卡选型可参考文章: 一文看懂各种储存卡:TF卡、SD/SDHC/SDXC卡、CF卡和Class等级
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TF卡（Nano运行）测试方法 （无其他软件运行)：
SanDisk --- UHS-I接口兼容(Speed: Class10 + A1)
写入速度测试：
$ dd count=1k bs=1M if=/dev/zero of=/home/nvidia/tst.img
1024+0 records in
1024+0 records out
1073741824 bytes (1.1 GB, 1.0 GiB) copied, 47.5029 s, 22.6 MB/s
读取速度测试：
$ sudo apt-get install hdparm
$ sudo hdparm -t /dev/mmcblk0p1
/dev/mmcblk0p1:
Timing buffered disk reads: 244 MB in 3.01 seconds = 81.19 MB/sec
SanDisk --- UHS-I接口兼容(Speed: U3 + V30 + A1)
读取速度测试：
$ sudo apt-get install hdparm
$ sudo hdparm -t /dev/mmcblk0p1
/dev/mmcblk0p1:
Timing buffered disk reads: 258 MB in 3.01 seconds = 85.61 MB/sec
写入速度测试：
nvidia@tw-Nano:~$ dd count=1k bs=1M if=/dev/zero of=/home/nvidia/tst.img
1024+0 records in
1024+0 records out
1073741824 bytes (1.1 GB, 1.0 GiB) copied, 8.68056 s, 124 MB/s
刷机过程如图：
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3、供电USB 5V 2A 供电自动开机（外加了一个风扇）
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AC Adapter 必须5V 开机
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4. Jetson Nano套件特性
硬件接口
- Ports & Interfaces
- 4x USB 3.0 A (Host)
- USB 2.0 Micro B (Device)
- MIPI CSI-2 x2 (15-position Camera Flex Connector)
- HDMI 2.0
- DisplayPort
- Gigabit Ethernet (RJ45)
- M.2 Key-E with PCIe x1
- MicroSD card slot
- (3x) I2C, (2x) SPI, UART, I2S, GPIOs
软件列表
- JetPack 4.2
- Linux4Tegra R32.1 (L4T)
- Linux kernel 4.9
- Ubuntu 18.04 LTS aarch64
- CUDA Toolkit 10.0
- cuDNN 7.3.1
- TensorRT 5.0.6
- TensorFlow 1.31.1
- VisionWorks 1.6
- OpenCV 3.3.1
- OpenGL 4.6
- OpenGL ES 3.2
- EGL 1.5
- Vulkan 1.1
- GStreamer 1.14.1
- V4L2 media controller support
5. 指导资料和教程文档
System Tools系统工具
Deep Learning深度学习资料
- Hello AI World (jetson-inference)
- TensorFlow 1.13.1 Installer (pip wheel)
-
PyTorch 1.1 Installer (pip wheel)
See the NVIDIA AI-IoT GitHub for other coding resources on deploying AI and deep learning.
Robotics 机器人应用
- NVIDIA JetBot (AI-powered robotics kit)
- jetbot_ros (ROS nodes for JetBot)
- ROS Melodic (ROS install guide)
- ros_deep_learning (jetson-inference nodes)
Camera 模组
- Leopard Imaging LI-IMX219-MIPI-FF-NANO
- Raspberry Pi Camera v2 (IMX219)
- Stereolabs ZED (stereo camera)
CUDA 10（路径：/usr/local/cuda/，但未加入环境变量）
运行如下命令添加环境变量：
export PATH=${PATH}:/usr/local/cuda/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
sudo apt-get update
sudo apt-get install samba
sudo apt-get install python3-pip
Tensorflow 1.13.1
pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v42 tensorflow-gpu==1.13.1+nv19.3 --user
安装过程若报错hdf5，请安装：
sudo apt-get install libhdf5-serial-dev
PS: Nano features list:
Nano 不支持SATA 硬盘，但可以通过如下方式：
1.USB3 SATA dongle
2.M.2 Key-E to Mini-PCIe adapter (未验证）
"$sudo nvpmodel -m 1" ---> Nano run in 5W (Power：5V1A)
"$sudo nvpmodel -m 0" ---> Nano run in 10W (Power：5V2A)
Supported video codecs: H.265, H.264, VP8, VP9 (VP9 decode only)
HEVC encoder supports 10-bit color, but B-frames are not supported
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