Setting up Common Software on Jetson AGX Orin
TL;DR
In general, we need to install the aarch64
distro of the software, since this device is using the ARM64 ISA.
Install CUDA
CUDA should be included with the Jetpack 6.1 SDK.
To test installation, do
nvcc --version
Install VSCode
When downloading from the official website, select ".deb - Arm64" button.

After download, use the following command to install.
sudo apt install ~/Downloads/code_1.95.1-1730354713.deb
Install MiniForge
Download "Mambaforge-24.9.0-0-Linux-aarch64.sh" from the official release page.
chmod +x ~/Downloads/Mambaforge-24.9.0-0-Linux-aarch64.sh
~/Downloads/Mambaforge-24.9.0-0-Linux-aarch64.sh
Install PyTorch
PyTorch with CUDA support is not available from the normal pip installation method.
Method 1: install from whl file
Older version of PyTorch can be found here.
First, we need to install dependencies for sparse matrix operation support
Follow instructions here.
wget https://developer.download.nvidia.com/compute/cusparselt/0.6.3/local_installers/cusparselt-local-tegra-repo-ubuntu2204-0.6.3_1.0-1_arm64.deb
sudo dpkg -i cusparselt-local-tegra-repo-ubuntu2204-0.6.3_1.0-1_arm64.deb
sudo cp /var/cusparselt-local-tegra-repo-ubuntu2204-0.6.3/cusparselt-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install libcusparselt0 libcusparselt-dev
also numpy is recommended
pip install numpy
Download the PyTorch wheel for JetPack 6.1.
After download, do
pip install ~/Downloads/torch-2.5.0a0+872d972e41.nv24.08.17622132-cp310-cp310-linux_aarch64.whl
Method 2: using docker containers
NVIDIA also provided a couple of preconfigured containers that can be installed with the jetson-container command. Following this instruction.
cd ~/Desktop/
git clone https://github.com/dusty-nv/jetson-containers
bash jetson-containers/install.sh
Edit /etc/docker/daemon.json
{
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia"
}
sudo systemctl restart docker
If docker fails to start with error message "Failed to start Docker Application Container Engine", use this command to print the error message
sudo journalctl -u docker.service
Then, do
jetson-containers run dustynv/pytorch:2.1-r36.2.0
Note
For some reason, the auto versioning command is not working:
jetson-containers run $(autotag pytorch)
Testing Installation
Test if installation succeed
python
> import torch
> torch.zeros(64).cuda()
Install Jtop
This is a helpful system resource monitor tool, similar to htop and nvidia-smi.
sudo pip3 install -U jetson-stats
After install, a system restart is required.
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