側欄
這是本文件的舊版！
目錄表
Jetson AGX Orin Development
Download SDKmanager in Host(X86 PC)
Orin AGX Platform Adaptation and Bring-Up Guide
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
Flash AGX ORIN Development Kit
sudo ./flash.sh jetson-agx-orin-devkit internal
Install Vscode on Target Device
git clone https://github.com/JetsonHacksNano/installVSCode.git cd installVSCode ./installVSCode.sh ./installVSCodeWithPython.sh code --> run vscode from console mode
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
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
Fan Control
/etc/nvfancontrol.conf
FAN_DEFAULT_PROFILE cool
Restart the service
sudo systemctl restart nvfancontrol
Power Mode Setting
Nvidia Power Model Tool to config ID=0 “MAXN”
sudo nvpmodel -m 0(MAXN)/1(15W)/2(30W)/3(50W)
Set static max frequency to CPU, GPU and EMC clocks
sudo jetson_clocks
AGX Stable Diffusion
https://github.com/chitoku/stable-diffusion cd stable-diffusion git checkout jetson ./docker/run.sh python3 scripts/txt2img.py --prompt "Robots marching down a street in Japanese city" --plms
JETSON AGX Orin performance test
https://github.com/NVIDIA-AI-IOT/jetson_benchmarks
git clone https://github.com/NVIDIA-AI-IOT/jetson_benchmarks.git\\ cd jetson_benchmarks\\ mkdir models\\ sudo sh install_requirements.sh\\ python3 utils/download_models.py --all --csv_file_path ./benchmark_csv/orin-benchmarks.csv --save_dir /home/aopen/jetson_benchmarks/models\\ sudo python3 benchmark.py --all --csv_file_path benchmark_csv/orin-benchmarks.csv --model_dir /home/aopen/jetson_benchmarks/models\\
Where is DTSI file
/home/ubuntu/workspace/dev2725/Linux_for_Tegra/source/public/hardware/nvidia/platform/t23x
AGX ORIN Development Kit Flash flow
sudo ./flash.sh jetson-agx-orin-devkit internal flash.sh # Num 1688 code start & entry jetson-agx-orin-devkit.conf --> Define the DTB & SOM p3701.conf.common --> SOM Define
Disable Board ID EEPROM
File Location : Linux_for_Tegra/bootloader/tegra234-mb2-bct-common.dtsi (the MB2 BCT file) Modification: - cvb_eeprom_read_size = <0x100> + cvb_eeprom_read_size = <0x0>
Modified
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/bootloader/t186ref/BCT/tegra234-mb2-bct-scr-p3701-0000.dts
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/bootloader/tegra234-mb2-bct-common.dtsi
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/source/public/hardware/nvidia/platform/t23x/common/kernel-dts/t234-common-cvm/tegra234-cvm-p3701.dtsi --> Serial port
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/source/public/hardware/nvidia/platform/t23x/concord/kernel-dts/tegra234-p3701-0000-p3737-0000.dts --> hdmi / hpd pin
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/bootloader/tegra234-mb2-bct-common.dtsi --> eeprom size 0
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/bootloader/tegra234-mb1-bct-gpio-p3701-0000-a04.dtsi --> GPIO setting
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/bootloader/t186ref/BCT/tegra234-mb1-bct-pinmux-p3701-0000-a04.dtsi --> pinmux
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/bootloader/t186ref/BCT/tegra234-mb1-bct-padvoltage-p3701-0000-a04.dtsi --> padvoltage
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/p3701.conf.common --> process_chip_sku_version() / ODMDATA
LAN
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/p3701.conf.common
ODMDATA="gbe-uphy-config-22,hsstp-lane-map-3,nvhs-uphy-config-0,hsio-uphy-config-16,gbe0-enable-10g";
/home/ubuntu/workspace/dev2725o/Linux_for_Tegra/source/public/hardware/nvidia/platform/t23x/concord/kernel-dts/cvb/tegra234-p3737-pcie.dtsi
/DonRtest
pcie@14180000 {
status = "okay";
phys = <&p2u_hsio_0>;
phy-names = "p2u-0";
};
Check HDMI Status
xrandr --output HDMI-0 --mode <res> --rate <refresh_rate> ex: xrandr --output HDMI-0 --mode 1280x720 --rate 60
