NVIDIA runtime on Ubuntu Server 24.04¶
Grub¶
The UEFI boot loader will automatically launch GRUB, which then will launch Ubuntu.
Ubuntu, first boot¶
You will be required on first boot to change your password, as the pre-installed image comes with a predefined user ubuntu (password ubuntu).
WLAN¶
You should be able to check the WLAN interface (using ip link for instance):
$ ip link show
1: lo: <LOOPBACK,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN mode DEFAULT group default qlen 1000
link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00
2: wlP1p1s0: <BROADCAST,MULTICAST> mtu 1500 qdisc noop state DOWN mode DEFAULT group default qlen 1000
link/ether 90:e8:68:bc:88:a9 brd ff:ff:ff:ff:ff:ff
Ubuntu Server configuration¶
Ubuntu Server comes with netplan and systemd-networkd preinstalled. The initial netplan configuration in the image should already take care of the Ethernet interface. To setup a WLAN connection, you can perform the following steps to add the related configuration:
# Replace <SSID> with your SSID
SSID='<SSID>'
# Replace <PASSWD> with your password
PASSWD='<PASSWD>'
# Create a netplan configuration for the WLAN
cat <<EOF | sudo tee /etc/netplan/51-wireless.yaml
network:
version: 2
wifis:
wlP1p1s0:
dhcp4: yes
dhcp6: yes
access-points:
"$SSID":
password: "$PASSWD"
EOF
sudo netplan apply
Once applied, your network interface should get up and running after a few seconds, which you can confirm using the ip address command.
Install NVIDIA proprietary software¶
The Ubuntu image brings anything necessary to boot Linux on a Jetson development kit. However, to unlock the features of the Tegra SoC (wireless network, bluetooth, GPU, …) you can install additional NVIDIA proprietary drivers and libraries using a Launchpad PPA and NVIDIA packages repository :
sudo apt-key adv --fetch-keys "https://repo.download.nvidia.com/jetson/jetson-ota-public.asc"
sudo add-apt-repository -y "deb https://repo.download.nvidia.com/jetson/common r38.2 main"
sudo add-apt-repository -y "deb https://repo.download.nvidia.com/jetson/som r38.2 main"
# Install Tegra firmwares and necessary NVIDIA libraries
sudo apt install -y nvidia-l4t-firmware nvidia-l4t-firmware-openrm nvidia-l4t-core nvidia-l4t-nvml nvidia-l4t-init
# Adding user to group render allows running GPU related commands as non root
# video group is necessary to use the camera
sudo usermod -a -G render,video ubuntu
sudo reboot
Install CUDA and TensorRT¶
SDKs like CUDA Toolkit and TensorRT that allow building AI applications on Jetson devices are available directly from NVIDIA:
# CUDA
sudo wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/sbsa/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt update
sudo apt install -y nvidia-l4t-cuda cuda-toolkit-13-0
# Tensor RT
sudo apt install -y libnvinfer-bin libnvinfer-samples
# cuda-samples dependencies
sudo apt install -y cmake
echo "export PATH=/usr/local/cuda/bin\${PATH:+:\${PATH}}" >> ~/.profile
echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib64\${LD_LIBRARY_PATH:+:\${LD_LIBRARY_PATH}}" >> ~/.profile
# Logout or reboot to apply the profile change
sudo reboot
Test your system¶
Snap¶
It’s Ubuntu, you can install a snap!
NVIDIA system management interface¶
nvidia-smi can be used to display GPU related information.
Run GPU’s sample code application¶
CUDA samples¶
You can build and run CUDA sample applications. You can start with deviceQuery, but you can also build and try many others.
git clone https://github.com/NVIDIA/cuda-samples.git -b v13.0
cd cuda-samples
cd Samples/1_Utilities/deviceQuery && cmake . && make
Running this sample code should produce the following output
ubuntu@ubuntu:~/cuda-samples/Samples/1_Utilities/deviceQuery$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA Thor"
CUDA Driver Version / Runtime Version 13.0 / 13.0
CUDA Capability Major/Minor version number: 11.0
Total amount of global memory: 125517 MBytes (131614285824 bytes)
(020) Multiprocessors, (128) CUDA Cores/MP: 2560 CUDA Cores
GPU Max Clock rate: 1049 MHz (1.05 GHz)
Memory Clock rate: 0 Mhz
Memory Bus Width: 0-bit
L2 Cache Size: 33554432 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total shared memory per multiprocessor: 233472 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Managed Memory: Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 13.0, CUDA Runtime Version = 13.0, NumDevs = 1
Result = PASS
TensorRT¶
mkdir ${HOME}/tensorrt-samples
ln -s /usr/src/tensorrt/data ${HOME}/tensorrt-samples/data
cp -a /usr/src/tensorrt/samples ${HOME}/tensorrt-samples/
cd ${HOME}/tensorrt-samples/samples/sampleProgressMonitor && make -j 8
cd ${HOME}/tensorrt-samples/bin
./sample_progress_monitor
GStreamer¶
Pre-requisites¶
Make sure to install the necessary GStreamer packages
# Install gstreamer plugins and nvidia codecs
sudo apt install -y gstreamer1.0-tools gstreamer1.0-alsa \
gstreamer1.0-plugins-base gstreamer1.0-plugins-good \
gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly \
gstreamer1.0-libav nvidia-l4t-gstreamer \
nvidia-l4t-3d-core nvidia-l4t-gbm nvidia-l4t-multimedia-openrm nvidia-l4t-video-codec-openrm
sudo apt install -y libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \
libgstreamer-plugins-good1.0-dev \
libgstreamer-plugins-bad1.0-dev
Transcode using GStreamer¶
Using a stream from the Big Buck Bunny project, you can easily test the transcoding pipelines:
sudo apt install unzip
wget -nv https://download.blender.org/demo/movies/BBB/bbb_sunflower_1080p_30fps_normal.mp4.zip
unzip -qu bbb_sunflower_1080p_30fps_normal.mp4.zip
echo "H.264 Decode (NVIDIA Accelerated Decode) to H265 encode"
gst-launch-1.0 filesrc location=bbb_sunflower_1080p_30fps_normal.mp4 ! qtdemux ! queue ! \
h264parse ! nvv4l2decoder ! nvv4l2h265enc bitrate=8000000 ! h265parse ! \
qtmux ! filesink location=h265-reenc.mp4 -e
echo "H.265 Decode (NVIDIA Accelerated Decode) to H.264 encode"
gst-launch-1.0 filesrc location=h265-reenc.mp4 ! qtdemux ! queue ! h265parse ! nvv4l2decoder ! \
nvv4l2h264enc bitrate=20000000 ! h264parse ! queue ! qtmux name=mux ! \
filesink location=h264-reenc.mp4 -e
cuDNN¶
Prerequisite¶
sudo apt install cudnn libcudnn9-samples
Run cuDNN Samples¶
Build and run the Converted sample.
cd /usr/src/cudnn_samples_v9
cd conv_sample
sudo make -j8
sudo chmod +x run_conv_sample.sh
sudo ./run_conv_sample.sh
You can also try other sample applications.
VPI¶
Prerequisites for VPI¶
Install VPI and its sample applications
sudo apt install nvidia-vpi vpi4-samples libopencv cmake libpython3-dev python3-numpy libopencv-python
Test¶
Execute steps 1 to 6 from the NVIDIA VPI test plan, for each VPI sample application.