The NVIDIA® Jetson Nano™ Developer Kit is a great way to get started with AI. It is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.
Last week, I wanted to run NVIDIA DeepStream software in a Docker container on top of Jetson Nano. To my surprise, I found that the only SD card available was 16GB in size. Then I realized that I hardly got 291 MB space left on my device.
df -h Filesystem Size Used Avail Use% Mounted on /dev/mmcblk0p1 15G 14G 291M 98% / none 947M 0 947M 0% /dev tmpfs 986M 40K 986M 1% /dev/shm tmpfs 986M 44M 943M 5% /run tmpfs 5.0M 4.0K 5.0M 1% /run/lock tmpfs 986M 0 986M 0% /sys/fs/cgroup tmpfs 198M 20K 198M 1% /run/user/1000
Luckily, I was able to solve this problem. Here’s what I followed to free up some disk space in NVIDIA Jetson Nano.
sudo apt update
sudo apt autoremove -y
sudo apt clean
sudo apt remove thunderbird libreoffice-* -y
sudo rm -rf /usr/local/cuda/samples \
/usr/share/visionworks* ~/VisionWorks-SFM*Samples \
sudo apt purge cuda-repo-l4t-local libvisionworks-repo -y
sudo rm /etc/apt/sources.list.d/cuda*local /etc/apt/sources.list.d/visionworks*repo*
sudo rm -rf /usr/src/linux-headers-*
sudo apt-get purge gnome-shell ubuntu-wallpapers-bionic light-themes chromium-browser* libvisionworks libvisionworks-sfm-dev -y
sudo apt-get autoremove -y
sudo apt clean -y
sudo rm -rf /usr/local/cuda/targets/aarch64-linux/lib/.a \
Woah! I was able to install NVIDIA DeepStream 6.0 on my 16GB SD card.