Jetson AGX Thor Setup
This guide covers the required steps to set up anNVIDIA Jetson AGX Thor. The Jetson runs NVIDIA Jetson Linux, an Ubuntu-based distribution with drivers and utilities optimized for the Jetson hardware.
The installation guide aims to be self-contained. However, theJetson Linux Developer Guide is a good reference for further details.
Post-Installation Setup
Note
The following steps can also be executed via:
./scripts/configure-system.agx-thor.sh
Update packages and install dependencies:
sudoaptupdatesudoaptdist-upgrade-ysudoaptinstall-yapt-utilscoreutilsgit-coregitcmakebuild-essential\bclibssl-devpython3python3-pipninja-buildca-certificatescurl\pandocnvidia-jetpack
Configure CUDA paths:
echo'export PATH=/usr/local/cuda/bin:$PATH'>>~/.bashrcecho'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH'>>~/.bashrcsource~/.bashrc
Download the Sionna Research Kit:
cd~# We assume sionna-rk is cloned in the home directorygitclone--recurse-submoduleshttps://github.com/NVlabs/sionna-rk.git
Docker Installation
Install Docker from the official Docker repository:
# Add Docker's official GPG keysudoinstall-m0755-d/etc/apt/keyringssudocurl-fsSLhttps://download.docker.com/linux/ubuntu/gpg-o/etc/apt/keyrings/docker.ascsudochmoda+r/etc/apt/keyrings/docker.asc# Add the repository to Apt sourcesecho\"deb [arch=$(dpkg--print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \$(./etc/os-release&&echo"${UBUNTU_CODENAME:-$VERSION_CODENAME}") stable"|\sudotee/etc/apt/sources.list.d/docker.list>/dev/null# Install Docker and pluginssudoaptupdatesudoaptinstall-ydocker-cedocker-ce-clicontainerd.iodocker-buildx-plugindocker-compose-plugin# Add user to docker groupsudousermod-aGdocker$USER# Log out and log in again for changes to take effect
NVIDIA Container Toolkit
Install the NVIDIA Container Toolkit for GPU support in Docker:
# Add NVIDIA Container Toolkit repositorycurl-fsSLhttps://nvidia.github.io/libnvidia-container/gpgkey|\sudogpg--dearmor-o/usr/share/keyrings/nvidia-container-toolkit-keyring.gpgcurl-s-Lhttps://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list|\sed's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g'|\sudotee/etc/apt/sources.list.d/nvidia-container-toolkit.list# Install the toolkitsudoaptupdatesudoaptinstall-ylibnvidia-container-toolslibnvidia-container1\nvidia-container-toolkitnvidia-container-toolkit-base# Configure Docker runtimesudonvidia-ctkruntimeconfigure--runtime=docker
Configure Docker service for Thor:
# Create Docker service overridesudomkdir-p/etc/systemd/system/docker.service.dsudotee/etc/systemd/system/docker.service.d/override.conf<<EOF[Service]Environment="DOCKER_INSECURE_NO_IPTABLES_RAW=1"EOF# Restart Dockersudosystemctldaemon-reloadsudosystemctlrestartdocker
Set the following environment variables:
exportSRK_PLATFORM="AGX Thor"exportSRK_THREAD_POOL="9,10,11,12,13"exportSRK_UE_THREAD_POOL="4,5"
TensorRT Installation
Install TensorRT and monitoring tools:
sudoaptinstall-ycuda-toolkittensorrt# Add trtexec alias for convenienceecho'alias trtexec=/usr/src/tensorrt/bin/trtexec'>>~/.bash_aliases
Quectel Modem Compatibility
If you want to connect a Quectel modem via USB to the Jetson AGX Thor, you need to build a custom kernel with theqmi_wwan kernel module. Note that this is only needed if the Thor acts as user equipment (UE).
This can be automatically done by running the following command:
./scripts/build-custom-kernel.sh./scripts/install-custom-kernel.sh
This will build and install the custom kernel (seeCustom Jetson Linux Kernel for details). Reboot the system for the changes to take effect.
Version Information
Check OS version:
cat/etc/lsb-releaseDISTRIB_ID=UbuntuDISTRIB_RELEASE=24.04DISTRIB_CODENAME=nobleDISTRIB_DESCRIPTION="Ubuntu 24.04.3 LTS"
Check Jetson Linux & JetPack version:
cat/etc/nv_tegra_release# R38 (release), REVISION: 2.1, GCID: 42061081, BOARD: generic, EABI: aarch64, DATE: Wed Sep 10 19:49:31 UTC 2025TARGET_USERSPACE_LIB_DIR=nvidiaTARGET_USERSPACE_LIB_DIR_PATH=usr/lib/aarch64-linux-gnu/nvidia