- The initialization on the Jetson devices will take a couple of minutes.
- Your Jetpack version should match the version used by our software.
Docker is an open source platform for creating, deploying, and running containers. Docker is included in JetPack, so running containers on Jetson is easy and does not require any installation.
Make sure the Jetpack libraries are installed. And then restart.
sudo apt updatesudo apt install nvidia-jetpack
Or if you are on a storage constrained device:
sudo apt-get install nvidia-docker2 nvidia-container-csv-tensorrt
If the packages are not available, the nvidia sources may not be enabled. Edit
vi /etc/apt/sources.list.d/nvidia-l4t-apt-source.list and uncomment the lines.
See those post installation steps.
NVIDIA JetPack SDK is the most comprehensive solution for building AI applications. It includes the latest OS images for Jetson products, along with libraries and APIs, samples, developer tools, and documentation.
If you see errors such as
createInferBuilder_INTERNAL symbol not found., it means your JetPack version does not match the one used by the Docker image. Your options are:
- Upgrade JetPack (see below).
- Use a version of the software that matches your JetPack version (see below).
Install and run jetson-stats.
sudo apt install python3-pippip3 install -U jetson-statsjetson_release -v
To free up RAM:
- Boot in text mode
sudo systemctl set-default multi-user.target. In text mode, just after logging in, the device will use around 300MB of RAM.
To free up around 1G of storage, use
sudo apt remove thunderbird libreoffice* imagemagick chromium-*sudo apt autoremove -y # If you do need a desktop environment, do the following:sudo apt remove ubuntu-desktop gdm3 untiysudo apt autoremove -y
If you use an older JetPack version, you can install an image that matches your version.
See all the Stream tags available. And pick the one that matches your Jetpack. For example, if you use Jetpack r32.5.0, use the image