目錄表
Jetson AGX Orin Development
Download SDKmanager in Host(X86 PC)
Console mode flash command
Before JetPack 5.1.1
Jetson Orin module to be emulated Flashing command
Jetson AGX Orin 64GB sudo ./flash.sh jetson-agx-orin-devkit mmcblk0p1
Jetson AGX Orin 32GB sudo ./flash.sh jetson-agx-orin-devkit-as-jao-32gb mmcblk0p
Jetson Orin NX 16GB sudo ./flash.sh jetson-agx-orin-devkit-as-nx16gb mmcblk0p1
Jetson Orin NX 8GB sudo ./flash.sh jetson-agx-orin-devkit-as-nx8gb mmcblk0p1
Jetson Orin Nano 8GB* sudo ./flash.sh jetson-agx-orin-devkit-as-nano8gb mmcblk0p1
Jetson Orin Nano 4GB sudo ./flash.sh jetson-agx-orin-devkit-as-nano4gb mmcblk0p1
After JetPack 5.1.2
sudo ./flash.sh jetson-agx-orin-devkit internal
Install Vscode
$ git clone https://github.com/JetsonHacksNano/installVSCode.git
$ cd installVSCode
$ ./installVSCode.sh
./installVSCodeWithPython.sh
code
Jetpack 5.x會有無法啟動的問題 安裝後執行以下程式碼即可啟用(參考來源)
code –no-sandbox
Install Chromium
search ubuntu SW “chromium”
Install JetPACK
cat /etc/nv_tegra_release
sudo apt update
sudo apt dist-upgrade
sudo reboot
sudo apt install nvidia-jetpack
Install JTOP
sudo apt install python3-pip
sudo pip3 install jetson-stats
sudo systemctl restart jtop.service
sudo jtop
jetson_release 命令显示NVIDIA Jetson的状态和所有信息
tegrastats 命令行查看各资源信息
minicom -D /dev/ttyACM0
Setting CPU clock to maximum
Setting AGX ORIN development kit Power mode at the upper right corner MAXN 50W 30W 15W
If you want to run CPU maximum frequency
$sudo jetson_clocks
Using Jetson Power GUI
https://developer.nvidia.com/deepstream-getting-started
https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#jetson-setup
Deepstream
/opt/nvidia/deepstream/deepstream-6.3/samples
$ sudo apt install \ libssl1.1 \ libgstreamer1.0-0 \ gstreamer1.0-tools \ gstreamer1.0-plugins-good \ gstreamer1.0-plugins-bad \ gstreamer1.0-plugins-ugly \ gstreamer1.0-libav \ libgstreamer-plugins-base1.0-dev \ libgstrtspserver-1.0-0 \ libjansson4 \ libyaml-cpp-dev
1. Clone the librdkafka repository from GitHub:
$ git clone https://github.com/edenhill/librdkafka.git
2. Configure and build the library:
$ cd librdkafka
$ git reset –hard 7101c2310341ab3f4675fc565f64f0967e135a6a
$ ./configure
$ make
$ sudo make install
3. https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream
Get deepstream-6.3_6.3.0-1_arm64.deb for Jetson not to download for x86
$ sudo apt-get install ./deepstream-6.3_6.3.0-1_arm64.deb
4. Copy the generated libraries to the deepstream directory:
$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.3/lib
$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.3/lib
Running deepstreaming demo
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source30_1080p_dec_infer-resnet_tiled_display_int8.txt
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source30_1080p_dec_preprocess_infer-resnet_tiled_display_int8.txt
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt
XXXX sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8_gpu1.txt
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source1_usb_dec_infer_resnet_int8.txt
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source2_1080p_dec_infer-resnet_demux_int8.txt
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.yml
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source30_1080p_dec_infer-resnet_tiled_display_int8.yml
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source4_1080p_dec_preprocess_infer-resnet_preprocess_sgie_tiled_display_int8.txt
sudo deepstream-app -c /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app/source2_dewarper_test.txt
CPU Stress Test
sudo apt update
sudo apt install cuda-toolkit-11-4
sudo apt install stress
sudo apt -y install pip
sudo apt -y install python3-pip
sudo -H pip install -U jetson-stats
compiled jetson-gpu-burn source code
git clone https://github.com/anseeto/jetson-gpu-burn.git
cd jetson-gpu-burn
make
Using stress to run CPU loading
Using gpu_burn to run GPU loading
running CPU：8 cores
$stress -c 8 &
running CPU：12 cores
$stress -c 12 &
running GPU maximum loading：
$./gpu_burn 1000