How to install SLE-15-SP6 on NVIDIA's Jetson AGX Orin, Jetson Orin Nano/NX and IGX Orin

This covers the installation of updated Kernel, out-of-tree nvidia kernel modules package, how to get GNOME desktop running and installation/run of glmark2 benchmark. Also it describes how to get some CUDA and TensorRT samples running.

SP6

Download SLE-15-SP6 (Arm) installation image. This you can put on a regular USB stick or on an SD card using dd command.

Boot from the USB stick/SD card, that you wrote above and install SP6. You can install via serial console or connect a monitor to the display port.

When using a connected monitor for installation

This needs for the installation a special setting in the Firmware of the machine.

--> UEFI Firmware Settings
 --> Device Manager
  --> NVIDIA Configuration
   --> Boot Configuration
    --> SOC Display Hand-Off Mode <Always>

This setting for SOC Display Hand-Off Mode will change automatically to Never later with the installation of the graphics driver.

Installation

Once grub starts you need to edit the grub entry Installation. Press e for doing this and add console=tty0 exec="date -s 2025-01-27" (when using a connected monitor for intallation) or exec="date -s 2025-01-27" (when installing on a serial console and add also console=ttyTCU0,115200 on Jetson Orin Nano) to the linux [...] line. Replace 2025-01-27 with the current date.

### When using a connected monitor for intallation
[...]
linux /boot/aarch64/linux splash=silent console=tty0 exec="date -s 2025-01-27"
[...]

### When installing on a serial console
[...]
linux /boot/aarch64/linux splash=silent exec="date -s 2025-01-27"
# On Jetson Orin Nano
linux /boot/aarch64/linux splash=silent console=ttyTCU0,115200 exec="date -s 2025-01-27"
[...]

The reason for this is that during installation the driver nvvrs-pseq-rtc for the battery-backed RTC0 (Real Time Clock) is not yet available and therefore the non-battery-backed RTC1 is used, which doesn’t have the correct time set during installation. So this is a workaround to avoid a product registration failure later due to a certificate, which is not valid yet.

Then press F10 to continue to boot.

Make sure you select the following modules during installation:

• Basesystem (enough for just installing the kernel driver)
• Containers (needed for podman for CUDA libraries)
• Desktop Applications (needed for running a desktop)
• Development Tools (needed for git for CUDA samples)

Select SLES with GNOME for installation.

In Clock and Time Zone dialogue chose Other Settings to open Change Date and Time dialogue. There enable Synchronize with NTP Server.

--> Clock and Time Zone dialogue
 --> Other Settings
  --> Change Date and Time dialogue
   --> (x) Synchronize with NTP Server

Kernel + KMP drivers

After installation update kernel and install our KMP (kernel module package) for all nvidia kernel modules.

Installation on NVIDIA’s Jetson AGX Orin and Jetson Orin Nano/NX

The KMP is available as a driver kit via the SolidDriver Program. For installation please use the following commands:

# flavor either default or 64kb (check with `uname -r` command)
sudo zypper up kernel-<flavor>
sudo zypper ar https://drivers.suse.com/nvidia/Jetson/Jetson_Linux_36.4/sle-15-sp6-aarch64/1.0/install jetson-kmp
sudo zypper ar https://drivers.suse.com/nvidia/Jetson/Jetson_Linux_36.4/sle-15-sp6-aarch64/1.0/update  jetson-kmp-update
sudo zypper ref
sudo zypper in -r jetson-kmp nvidia-jetson-36_4-kmp-<flavor>

Installation on NVIDIA IGX Orin

We plan to make the KMP available as a driver kit via the SolidDriver Program. For now please install an updated kernel and the KMP after checking the build status (type ‘igx’ in Search… field; rebuilding can take a few hours!) from our open buildservice:

# flavor either default or 64kb (check with `uname -r` command)
sudo zypper up kernel-<flavor>
sudo zypper ar https://download.opensuse.org/repositories/X11:/XOrg/SLE_15_SP6/ igx-kmp
sudo zypper ref
sudo zypper in -r jetson-kmp nvidia-igx-36_4-kmp-<flavor>

Userspace/Desktop

Installation on NVIDIA’s Jetson AGX Orin and Jetson Orin Nano/NX

Please install userspace on these devices by using the following commands:

sudo zypper ar https://repo.download.nvidia.com/jetson/sle15-sp6/jp6.1/ jetson-userspace 
sudo zypper ref 
sudo zypper in nvidia-jetpack-all

Installation on NVIDIA IGX Orin

Unfortunately installing the userspace on this device is still a non-trivial task.

Download Bootloader(QSPI) Package from this location (select IGX-SW 1.1 Production Release). Extract Jetson_Linux_R36.4.1_aarch64.tbz2.

tar xf Jetson_Linux_R36.4.1_aarch64.tbz2

Then you need to convert debian packages from this content into tarballs.

pushd Linux_for_Tegra
sed -i -e 's/lbzip2/bzip2/g' -e 's/-I zstd //g' nv_tools/scripts/nv_repackager.sh
./nv_tools/scripts/nv_repackager.sh -o ./nv_tegra/l4t_tar_packages --convert-all
popd

From the generated tarballs you only need these:

nvidia-l4t-3d-core_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-camera_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-core_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-cuda_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-firmware_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-gbm_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-multimedia-utils_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-multimedia_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-nvfancontrol_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-nvml_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-nvpmodel_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-nvsci_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-pva_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-tools_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-vulkan-sc-sdk_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-wayland_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-x11_36.4.1-20241119120551_arm64.tbz2
nvidia-l4t-nvml_36.4.1-20241119120551_arm64.tbz2

And from this tarball nvidia-l4t-init_36.4.1-20241119120551_arm64.tbz2 you only need these files:

etc/asound.conf.tegra-ape
etc/asound.conf.tegra-hda-jetson-agx
etc/asound.conf.tegra-hda-jetson-xnx
etc/nvidia-container-runtime/host-files-for-container.d/devices.csv
etc/nvidia-container-runtime/host-files-for-container.d/drivers.csv
etc/nvsciipc.cfg
etc/sysctl.d/60-nvsciipc.conf
etc/systemd/nv_nvsciipc_init.sh
etc/systemd/nvpower.sh
etc/systemd/nv.sh
etc/systemd/system.conf.d/watchdog.conf
etc/systemd/system/multi-user.target.wants/nv_nvsciipc_init.service
etc/systemd/system/multi-user.target.wants/nvpower.service
etc/systemd/system/multi-user.target.wants/nv.service
etc/systemd/system/nv_nvsciipc_init.service
etc/systemd/system/nvpower.service
etc/systemd/system/nv.service
etc/udev/rules.d/99-tegra-devices.rules
usr/share/alsa/cards/tegra-ape.conf
usr/share/alsa/cards/tegra-hda.conf
usr/share/alsa/init/postinit/00-tegra.conf
usr/share/alsa/init/postinit/01-tegra-rt565x.conf
usr/share/alsa/init/postinit/02-tegra-rt5640.conf

So first let’s repackage nvidia-l4t-init_36.4.1-20241119120551_arm64.tbz2:

pushd Linux_for_Tegra/nv_tegra/l4t_tar_packages/
cat > nvidia-l4t-init.txt << EOF
etc/asound.conf.tegra-ape
etc/asound.conf.tegra-hda-jetson-agx
etc/asound.conf.tegra-hda-jetson-xnx
etc/nvidia-container-runtime/host-files-for-container.d/devices.csv
etc/nvidia-container-runtime/host-files-for-container.d/drivers.csv
etc/nvsciipc.cfg
etc/sysctl.d/60-nvsciipc.conf
etc/systemd/nv_nvsciipc_init.sh
etc/systemd/nvpower.sh
etc/systemd/nv.sh
etc/systemd/system.conf.d/watchdog.conf
etc/systemd/system/multi-user.target.wants/nv_nvsciipc_init.service
etc/systemd/system/multi-user.target.wants/nvpower.service
etc/systemd/system/multi-user.target.wants/nv.service
etc/systemd/system/nv_nvsciipc_init.service
etc/systemd/system/nvpower.service
etc/systemd/system/nv.service
etc/udev/rules.d/99-tegra-devices.rules
usr/share/alsa/cards/tegra-ape.conf
usr/share/alsa/cards/tegra-hda.conf
usr/share/alsa/init/postinit/00-tegra.conf
usr/share/alsa/init/postinit/01-tegra-rt565x.conf
usr/share/alsa/init/postinit/02-tegra-rt5640.conf
EOF
tar xf nvidia-l4t-init_36.4.1-20241119120551_arm64.tbz2
rm nvidia-l4t-init_36.4.1-20241119120551_arm64.tbz2
tar cjf nvidia-l4t-init_36.4.1-20241119120551_arm64.tbz2 $(cat nvidia-l4t-init.txt)
popd

On NVIDIA IGX Orin with dedicated graphics card (dGPU systems) you need to get rid of some files due to conflicts with dGPU userspace drivers.

# repackage nvidia-l4t-x11_ package
tar tf nvidia-l4t-x11_36.4.1-20241119120551_arm64.tbz2 | grep -v /usr/bin/nvidia-xconfig \
  > nvidia-l4t-x11_36.4.1-20241119120551.txt
tar xf  nvidia-l4t-x11_36.4.1-20241119120551_arm64.tbz2
rm      nvidia-l4t-x11_36.4.1-20241119120551_arm64.tbz2
tar cjf nvidia-l4t-x11_36.4.1-20241119120551_arm64.tbz2 $(cat nvidia-l4t-x11_36.4.1-20241119120551.txt)

# repackage nvidia-l4t-3d-core_ package
tar tf nvidia-l4t-3d-core_36.4.1-20241119120551_arm64.tbz2 | \
  grep -v \
       -e /etc/vulkan/icd.d/nvidia_icd.json \
       -e /usr/lib/xorg/modules/drivers/nvidia_drv.so \
       -e /usr/lib/xorg/modules/extensions/libglxserver_nvidia.so \
       -e /usr/share/glvnd/egl_vendor.d/10_nvidia.json \
       > nvidia-l4t-3d-core_36.4.1-20241119120551.txt
tar xf  nvidia-l4t-3d-core_36.4.1-20241119120551_arm64.tbz2
rm      nvidia-l4t-3d-core_36.4.1-20241119120551_arm64.tbz2
tar cjf nvidia-l4t-3d-core_36.4.1-20241119120551_arm64.tbz2 $(cat nvidia-l4t-3d-core_36.4.1-20241119120551.txt)

Then extract the generated tarballs to your system.

pushd Linux_for_Tegra/nv_tegra/l4t_tar_packages
for i in \
nvidia-l4t-core_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-3d-core_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-cuda_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-firmware_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-gbm_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-multimedia-utils_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-multimedia_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-nvfancontrol_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-nvpmodel_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-tools_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-x11_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-nvsci_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-pva_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-wayland_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-camera_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-vulkan-sc-sdk_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-nvml_36.4.1-20241119120551_arm64.tbz2 \
nvidia-l4t-init_36.4.1-20241119120551_arm64.tbz2; do
  sudo tar xjf $i -C /
done
popd

On systems without dedicated graphics (internal GPU systems) card you still need to move

/usr/lib/xorg/modules/drivers/nvidia_drv.so
/usr/lib/xorg/modules/extensions/libglxserver_nvidia.so

to

/usr/lib64/xorg/modules/drivers/nvidia_drv.so
/usr/lib64/xorg/modules/extensions/libglxserver_nvidia.so

So let’s do this.

sudo mv /usr/lib/xorg/modules/drivers/nvidia_drv.so \
          /usr/lib64/xorg/modules/drivers/
sudo mv /usr/lib/xorg/modules/extensions/libglxserver_nvidia.so \
          /usr/lib64/xorg/modules/extensions/
sudo rm -rf /usr/lib/xorg

Then add /usr/lib/aarch64-linux-gnu and /usr/lib/aarch64-linux-gnu/tegra-egl to /etc/ld.so.conf.d/nvidia-tegra.conf.

echo /usr/lib/aarch64-linux-gnu | sudo tee -a /etc/ld.so.conf.d/nvidia-tegra.conf
echo /usr/lib/aarch64-linux-gnu/tegra-egl | sudo tee -a /etc/ld.so.conf.d/nvidia-tegra.conf

Run ldconfig 

sudo ldconfig

Video group for regular users

A regular user needs to be added to the group video to be able to log in to the GNOME desktop as regular user. This can be achieved by using YaST, usermod or editing /etc/group manually.

Reboot the machine with the previously updated kernel

sudo reboot

Select first entry SLES 15-SP6 for booting.

Basic testing

First basic testing will be running nvidia-smi. 

sudo nvidia-smi

Graphical desktop (GNOME) should work as well. Linux console will also be available. Use either a serial console or a ssh connection if you don’t want to use the graphical desktop/Linux console or need remote access to the system.

glmark2

Install phoronix-test-suite

sudo zypper ar https://cdn.opensuse.org/distribution/leap/15.6/repo/oss/ repo-oss
sudo zypper ref
sudo zypper in phoronix-test-suite

Run phoronix-test-suite

sudo zypper in gcc gcc-c++
# Prepare for realistic numbers
# 1. Logout from your GNOME session
# 2. Login again, but select IceWM Session as desktop instead of GNOME
# 3. Start xterm and run the following command
phoronix-test-suite benchmark glmark2

This should give you an average score of about 4500 running in 1920x1080 resolution with MaxN Power and best performance settings (see Misc/Performance and Misc/MaxN/MaxN_Super Power below) on Jetson AGX Orin and about 2500 on Jetson Orin Nano (also with best performance settings).

Wayland based Desktop

In order to enable our GNOME on Wayland desktop you need to install two additional packages: xwayland and gnome-session-wayland.

sudo zypper in xwayland gnome-session-wayland

Afterwards restart GDM

sudo systemctl restart display-manager.service

or reboot your machine.

CUDA/Tensorflow

Containers

NVIDIA provides containers available for Jetson that include SDKs such as CUDA. More details here. These containers are Ubuntu based, but can be used from SLE as well. You need to install the NVIDIA container runtime for this. Detailed information here.

1. Install podman and nvidia-container-runtime

sudo zypper install podman
sudo zypper ar https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
sudo zypper modifyrepo --enable nvidia-container-toolkit-experimental
sudo zypper --gpg-auto-import-keys install -y nvidia-container-toolkit
sudo nvidia-ctk cdi generate --mode=csv --output=/var/run/cdi/nvidia.yaml
sudo nvidia-ctk cdi list

2. Download the CUDA samples

sudo zypper install git
cd
git clone https://github.com/NVIDIA/cuda-samples.git
cd cuda-samples
git checkout v12.4

3. Start X

sudo rcxdm stop
sudo Xorg -retro &> /tmp/log &
export DISPLAY=:0
xterm &

Monitor should now show a Moiree pattern with an unframed xterm on it. Otherwise check /tmp/log.

4. Download and run the JetPack6 container

sudo podman run --rm -it -e DISPLAY --net=host --device nvidia.com/gpu=all --group-add keep-groups --security-opt label=disable -v $HOME/cuda-samples:/cuda-samples nvcr.io/nvidia/l4t-jetpack:r36.2.0 /bin/bash

CUDA

5. Build and run the samples in the container

cd /cuda-samples
make -j$(nproc)
pushd ./Samples/5_Domain_Specific/nbody
make
popd
./bin/aarch64/linux/release/deviceQuery
./bin/aarch64/linux/release/nbody

Tensorrt

6. Build and run Tensorrt in the container

This is both with the GPU and DLA (deep-learning accelerator).

cd /usr/src/tensorrt/samples/
make -j$(nproc)
cd ..
./bin/sample_algorithm_selector
./bin/sample_onnx_mnist
# Fails on Jetson Orin Nano due to lacking Deep Learning Accelerator (DLA)
./bin/sample_onnx_mnist --useDLACore=0
./bin/sample_onnx_mnist --useDLACore=1

Misc

Performance

You can improve the performance by giving the clock a boost. For best performance you can run jetson_clocks to set the device to max clock settings

sudo jetson_clocks --show
sudo jetson_clocks
sudo jetson_clocks --show

The 1st and 3rd command just prints the clock settings.

MaxN/MaxN_Super Power

For maximum performance you also need to set MaxN/MaxN_Super Power. This can be done by running

# Jetson AGX Orin
sudo nvpmodel -m 0
# Jetson Orin Nano
sudo nvpmodel -m 2

Afterwards on Jetson AGX Orin you need to reboot the system though.

sudo reboot

In order to check for the current value run

sudo nvpmodel -q