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The syslog-ng Insider 2024-05: documentation; grouping-by(); PAM Essentials; health

The May syslog-ng newsletter is now on-line:

  • The official syslog-ng OSE documentation got a new look

The syslog-ng Administration Guide received a new look and easier navigation. Not only that, but it is also up-to-date now. Besides, there are now contributor guides available both for the documentation and for syslog-ng developers.

The admin guide is available at: https://syslog-ng.github.io/admin-guide/README

You can reach all syslog-ng OSE-related documentation at: https://syslog-ng.github.io/

If you find any issues, pull requests and problem reports are welcome. The contributor guide describes how you can fix / extend the documentation. You can report issues at: https://github.com/syslog-ng/syslog-ng.github.io/issues

  • Aggregating messages in syslog-ng using grouping-by()
  • Alerting on One Identity Cloud PAM Essentials logs using syslog-ng
  • The syslog-ng health check

It is available at https://www.syslog-ng.com/community/b/blog/posts/the-syslog-ng-insider-2024-05-documentation-grouping-by-pam-essentials-health

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Planned outage of Weblate on May 14th

The openSUSE will undergo a critical update with the migration of Weblate to a hosted solution.

Shifting to a hosted solution for the web-based localization tool in order to keep up with the increasing demands of projects’ development.

The migration is slated for May 14 and it is anticipated that the service will be down for approximately one day.

This is a planned short-term inconvenience for a long-term benefit and will allow for our translation contributors to pick up right where they left off.

People wanting to contribute to the openSUSE Project by helping to translate using Weblate can register on https://l10n.opensuse.org and connect with other translators through translation@lists.opensuse.org and project@lists.opensuse.org mailing lists.

Any attempt to connect to Weblate during the migration will trigger a notification informing the user of the ongoing maintenance. Others will be informed of the outage through https://status.opensuse.org.

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the avatar of Stefan Dirsch
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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. In addition it describes the firmware update on Jetson AGX Orin and Jetson Orin Nano and how to connect a serial console to Jetson Orin Nano.

Firmware Update on Jetson AGX Orin

On Jetson AGX Orin first update the firmware to Jetpack 6.1/36.4.0.

Download Driver Package (BSP) from this location. Extract Jetson_Linux_R36.4.0_aarch64.tbz2.

tar xf Jetson_Linux_R36.4.0_aarch64.tbz2

Then connect with two cables your computer to the Micro-USB port and Type-C port (next to the 40pin connector) of Jetson AGX Orin. Now switch Jetson AGX Orin to recovery mode (using your Micro-USB cable).

cd Linux_for_Tegra
sudo ./tools/board_automation/boardctl -t topo recovery

Check that Jetson AGX Orin is now in recovery mode.

lsusb
[...]
Bus 003 Device 099: ID 0955:7023 NVIDIA Corp. APX
[...]

Now flash your firmware (using the Type-C cable). Make sure you have package dtc installed, because the tool fdtoverlay is needed.

sudo ./flash.sh p3737-0000-p3701-0000-qspi external

Reboot Jetson AGX Orin.

sudo ./tools/board_automation/boardctl -t topo power_on

After reboot you should see in the Firmware setup - shown on your monitor or on your serial console - the firmware version 36.4.0-gcid-XXXXXXXX.

Firmware Update on Jetson Orin Nano

Updating the firmware on Jetson Orin Nano is similar to the process above for Jetson AGX Orin.

Unfortunately the board automation tools do not support Jetson Orin Nano. Therefore for switching this device in recovery mode instead of running boardctl you need to connect two pins or put a jumper on both respectively. These are the pins 9/10 (GND/FC REC) of the 12-pin J14 “button” header of carrier board located under the Jetson module (right below the fan next to the SD card slot).

So disconnect Jetson Orin Nano from power, then connect these pins and then reconnect power. With that the device should be in Recovery mode. Connect an USB cable to the Type-C port of Jetson Orin Nano and check if it is now in Recovery mode.

lsusb
[...]
Bus 003 Device 105: ID 0955:7523 NVIDIA Corp. APX
[...]

Now flash your firmware. Make sure you have package dtc installed, because the tool fdtoverlay is needed.

sudo ./flash.sh p3768-0000-p3767-0000-a0-qspi external

Disconnect Jetson Orin Nano from power and reconnect it to power. After reboot you should see in the Firmware setup - shown on your monitor or on your serial console - the firmware version 36.4.0-gcid-XXXXXXXX.

Serial Console on Jetson Orin Nano

In order to have a serial console on Jetson Orin Nano you need a 3.3.V USB-UART adapter/cable. Connect it to the pins 3/4/7 (RXD/TXD/GND) of the 12-pin J14 “button” header of carrier board located under the Jetson module (right below the fan next to the SD card slot).

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/NVIDIA_JetPack_6.1/sle-15-sp6-aarch64/1.0/install jetson-kmp
sudo zypper ar https://drivers.suse.com/nvidia/Jetson/NVIDIA_JetPack_6.1/sle-15-sp6-aarch64/1.0/update  jetson-kmp-update
sudo zypper ref
sudo zypper in -r jetson-kmp nvidia-jetson-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-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 1.1.1 Production Release). Extract Jetson_Linux_R36.4.5_aarch64.tbz2.

tar xf Jetson_Linux_R36.4.5_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_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-camera_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-core_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-cuda_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-firmware_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-gbm_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-multimedia-utils_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-multimedia_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-nvfancontrol_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-nvml_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-nvpmodel_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-nvsci_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-pva_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-tools_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-vulkan-sc-sdk_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-wayland_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-x11_36.4.5-20250205154014_arm64.tbz2
nvidia-l4t-nvml_36.4.5-20250205154014_arm64.tbz2

And from this tarball nvidia-l4t-init_36.4.5-20250205154014_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_36.4.5-20250205154014_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_36.4.5-20250205154014_arm64.tbz2
rm nvidia-l4t-init_36.4.5-20250205154014_arm64.tbz2
tar cjf nvidia-l4t-init_36.4.5-20250205154014_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_36.4.5-20250205154014_arm64.tbz2 | grep -v /usr/bin/nvidia-xconfig \
  > nvidia-l4t-x11_36.4.5-20250205154014.txt
tar xf  nvidia-l4t-x11_36.4.5-20250205154014_arm64.tbz2
rm      nvidia-l4t-x11_36.4.5-20250205154014_arm64.tbz2
tar cjf nvidia-l4t-x11_36.4.5-20250205154014_arm64.tbz2 $(cat nvidia-l4t-x11_36.4.5-20250205154014.txt)

# repackage nvidia-l4t-3d-core_ package
tar tf nvidia-l4t-3d-core_36.4.5-20250205154014_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_36.4.5-20250205154014.txt
tar xf  nvidia-l4t-3d-core_36.4.5-20250205154014_arm64.tbz2
rm      nvidia-l4t-3d-core_36.4.5-20250205154014_arm64.tbz2
tar cjf nvidia-l4t-3d-core_36.4.5-20250205154014_arm64.tbz2 $(cat nvidia-l4t-3d-core_36.4.5-20250205154014.txt)

Then extract the generated tarballs to your system.

pushd Linux_for_Tegra/nv_tegra/l4t_tar_packages
for i in \
nvidia-l4t-core_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-3d-core_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-cuda_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-firmware_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-gbm_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-multimedia-utils_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-multimedia_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-nvfancontrol_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-nvpmodel_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-tools_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-x11_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-nvsci_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-pva_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-wayland_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-camera_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-vulkan-sc-sdk_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-nvml_36.4.5-20250205154014_arm64.tbz2 \
nvidia-l4t-init_36.4.5-20250205154014_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 v12.5
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:r36.4.0 /bin/bash
# needed in container for nbody
apt-get install libglu1-mesa freeglut3
apt-get install --fix-missing libglu1-mesa-dev freeglut3-dev

CUDA

5. Build and run the samples in the container
cd /cuda-samples
make -j$(nproc)
./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 lack of Deep Learning Accelerator(s) (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 ln -snf nvpmodel/nvpmodel_p3767_0003_super.conf /etc/nvpmodel.conf
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

Known Issues

Jetson Orin Nano: Super Mode

Unfortunately Super mode of Jetson Orin Nano needs Jetpack 6.2.1/36.4.4 for Firmware, KMP drivers and userspace. We’re currently working on providing these as easily installable packages in addition to our packages for Jetpack 6.1/36.4.0. This document will be updated accordingly once these are available. Therefore currently when trying to switch Jetson Orin Nano into Super mode with

sudo nvpmodel -m 2

you’ll get an error message. Of course the other non-Super modes on Jetson Orin Nano are still available and working.

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the avatar of Stefan's openSUSE Blog

How to install SLE-15-SP6 on NVIDIA Jetson platform (Jetson AGX Orin/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) 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"
[...]

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.

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 ‘jetson’ 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/  jetson-kmp
sudo zypper ref
sudo zypper in -r jetson-kmp nvidia-jetson-36_4-kmp-<flavor> kernel-firmware-nvidia-jetson-36_4

Userspace/Desktop

Unfortunately installing the userspace is a non-trivial task.

Installation

Download Driver Package (BSP) from this location. Extract Jetson_Linux_R36.4.0_aarch64.tbz2.

tar xf Jetson_Linux_R36.4.0_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_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-camera_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-core_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-cuda_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-gbm_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-multimedia-utils_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-multimedia_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-nvfancontrol_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-nvml_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-nvpmodel_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-nvsci_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-pva_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-tools_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-vulkan-sc-sdk_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-wayland_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-x11_36.4.0-20240912212859_arm64.tbz2
nvidia-l4t-nvml_36.4.0-20240912212859_arm64.tbz2

And from this tarball nvidia-l4t-init_36.4.0-20240912212859_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_36.4.0-20240912212859_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_36.4.0-20240912212859_arm64.tbz2
rm nvidia-l4t-init_36.4.0-20240912212859_arm64.tbz2
tar cjf nvidia-l4t-init_36.4.0-20240912212859_arm64.tbz2 $(cat nvidia-l4t-init.txt)
popd

On IGX Orin platform 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_36.4.0-20240912212859_arm64.tbz2 | grep -v /usr/bin/nvidia-xconfig \
  > nvidia-l4t-x11_36.4.0-20240912212859.txt
tar xf  nvidia-l4t-x11_36.4.0-20240912212859_arm64.tbz2
rm      nvidia-l4t-x11_36.4.0-20240912212859_arm64.tbz2
tar cjf nvidia-l4t-x11_36.4.0-20240912212859_arm64.tbz2 $(cat nvidia-l4t-x11_36.4.0-20240912212859.txt)

# repackage nvidia-l4t-3d-core_ package
tar tf nvidia-l4t-3d-core_36.4.0-20240912212859_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_36.4.0-20240912212859.txt
tar xf  nvidia-l4t-3d-core_36.4.0-20240912212859_arm64.tbz2
rm      nvidia-l4t-3d-core_36.4.0-20240912212859_arm64.tbz2
tar cjf nvidia-l4t-3d-core_36.4.0-20240912212859_arm64.tbz2 $(cat nvidia-l4t-3d-core_36.4.0-20240912212859.txt)

Then extract the generated tarballs to your system.

pushd Linux_for_Tegra/nv_tegra/l4t_tar_packages
for i in \
nvidia-l4t-core_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-3d-core_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-cuda_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-gbm_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-multimedia-utils_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-multimedia_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-nvfancontrol_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-nvpmodel_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-tools_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-x11_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-nvsci_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-pva_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-wayland_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-camera_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-vulkan-sc-sdk_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-nvml_36.4.0-20240912212859_arm64.tbz2 \
nvidia-l4t-init_36.4.0-20240912212859_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

In Mokmanager (Perform MOK management) select Continue boot. Although Secureboot is enabled by default in BIOS it seems it hasn’t been implemented yet (BIOS from 04/04/2024). 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. Unfortunately Linux console is not available. Use either a serial console or a ssh connection if you don’t want to use the graphical desktop 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++
phoronix-test-suite benchmark glmark2

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
./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 Power

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

sudo nvpmodel -m 0

Afterwards you need to reboot the system though.

sudo reboot

In order to check for the current value run

sudo nvpmodel -q

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the avatar of openSUSE News

openSUSE Asia Summit Set for Tokyo

openSUSE.Asia Summit will come back to Tokyo, Japan

The openSUSE Project is exciting to announce that openSUSE.Asia Summit 2024 is going to be held in Tokyo, Japan. The openSUSE.Asia Summit is an annual conference for users and contributors of openSUSE and FLOSS enthusiasts. During this summit, they will gather in person to share knowledge and experiences about openSUSE including applications running on it.

The venue of the summit will be located in Tokyo, the capital of Japan, blending tradition and cutting-edge technology. Its infrastructure and global connectivity make it a primal location for promoting collaboration among openSUSE users and developers. Moreover, Tokyo is a center of information technology; Many technology companies have their offices in Tokyo, with numerous engineers residing in the surrounding areas.

Tokyo is also a popular place for sightseeing with its unique culture, food, etc. Especially, characters from video games, anime, and comics, which are now common in the world, attract tourists to Japan. In Tokyo, you can easily find character shops and get items related to works you love.

The number of tourists from abroad has recovered last year to the same level as before COVID-19. Due to the currency exchange rate, it will be a great chance to enjoy your trip to Japan while saving your money. Even though you may have attended the last summit in Tokyo, you will discover new facets, developed before the TOKYO 2020 Summer Olympics.

Please see also:

The expected summit date is Nov. 2 and 3 soon after Open Source Summit Japan. Our call for speakers is going to end around the end of July. For more details including the venue, please stay tuned until the next announcement in a couple of weeks.

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the avatar of rickspencer3's Blog

openSUSE Tumbleweed is the Best Distro No One Knows About

openSUSE Tumbleweed is the Best Distro No One Knows About

I've been at SUSE for 4 months now. Of course the company keeps my primary focus on our Enterprise customers, but I have learned a lot more how openSUSE is built and used in the four months, and I have to say, I am impressed. I think Tumbleweed is the best developer distro that nobody knows about.

On my main laptop I opted to install the "stable" verion of openSUSE called "Leap." (you can read about that here). I followed suit on my $65 laptop, but ran into some issues based on the cheapness and newness of the laptop's components. For example, the wifi module was not recognized, and the built in speakers just didn't work. The wifi issue was obvious; the wifi module was too new for Leap 15.5, and I was too lazy to compile and install an up to date kernel driver for it.

As I learned more about openSUSE, I finally understood the difference between Tumbleweed and Leap, and I realized that Tumbleweed would probably work well on my oddball $65 laptop.

How is openSUSE built anyway?

openSUSE is unique, because it is both upstream of Suse Linux Enterprise, and downstream from it. Basically, what happens is:

  1. The openSUSE community is constantly packaging upstream software with the Open Build Server.
  2. Those packages are constantly being built into openSUSE Tumbleweed, which is, therefore, a rolling release. There is a quality assurance process that keeps Tumbleweed stable in the sense of "not crashy."
  3. Periodically, those packages from the Open Build Server, which become highly used and vetted by the community using Tumbleweed, then get moved into SUSE's Internal Build Server. From there, SUSE builds Certified and L3 Supported packages, that go into SUSE Linux Enterprie releases. This is a paid Enterprise product.
  4. Out of those packages, openSUSE Leap is built. Leap, therefore, is essentially the same as SUSE Linux Enterprise, but without the certifications and support.

diagram of SUSE packaging process

I assume I got some details wronng above, but I think that's the gist of it.

Choice happens. You can choose a high quality rolling release, a fully supported Enterprise release with a long lifecycle, or a free (as in speach and beer) release with the same lifecycle and bits as the Enterprise version.

For simplicity, I left out that there are even more options. For example, do you want an immutable OS with transactional updates? The openSUSE community has you covered with Microos.

So How did it Go?

Installing Tumbleweed was actually pretty boring. The main difference from installing Leap was that the wifi driver was recogized by the kernel (as I expected). I was pleasantly surprised to see that I also a built in LTE modem.

Tumbleweed detected and allowed me to configure the wifi

Up to Date

Looks like after install, every single package is up to date with the repositories. I uppose the installer installed all up to date packages from the repositories, which is sweet.

everything up to date

WIFI woes

However, while the built in wifi seemsto work, I noticed that when I am downloading files, they sometimes get "stuck." Either the server times out, or the data trickles in so slowly the files will never download. More on this bellow.

Next Steps

So now I seem to be a happy Tumbleweed user. I have installed my work software (Slack, etc...) so I am planning to take this device as my only laptop on an upcoming work trip to Europe in May. I should be in meetings most of the time, so it's a pretty low risk situation.

Follow up on Issues

So, this wifi issue ... this seems like a good opportunity for me to help out with the community however modestly. I will learn how to log an issue in the right place, and then see if I can help who ever turns out to be the right mainter address the bug.

Connect with the Community

I am motivated to started looking at this issue as openSUSE Conference is coming up at the end of June, and I am looking forward to connecting with community members and generally learning how the openSUSE community works, and seeing how I can collaborate and help.

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a silhouette of a person's head and shoulders, used as a default avatar

openSUSE Tumbleweed – Review of the weeks 2024/17 & 18

Dear Tumbleweed users and hackers,

Last week, I was attending the SUSE Labs Conference last week and had to skip writing the weekly review. As many SUSE devs were there too, the expectation was to get fewer changes anyway during week 17. Consequently, I am spanning two weeks again today and will be covering the nine snapshots (0419, 0421, 0423, 0425…0430) released during this period.

The most relevant changes delivered were:

  • Linux kernel 6.8.7 & 6.8.8
  • SETools 4.5.0
  • libxml 2.12.6
  • LLVM 18.1.4
  • Python 3.11.9 & 3.12.3
  • Mesa 24.0.5
  • Mozilla Firefox 125.0.2
  • SQLite 3.45.3

Having some engineers together at the Labs Conference also allowed them to directly exchange ideas and work on some of the things in staging. Simon and I have worked on dbus-broker and made some good progress, but we have not yet reached the end goal. Similarly for other things in the staging areas. The most interesting changes being prepared are:

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the avatar of Nathan Wolf

Google Groupware Calendar with KOrganizer Fix

I have been having issues with the Google Groupware Calendar module in KOganizer not remembering how to communicate with Google. I was very frustratingly having to connect it with KOrganzier but there is an easy solution. Kontact with its Kmail and Korganizer components. To me, this has been the ultimate in calendar management. It is […]
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the avatar of Santiago Zarate

RuPerl - Rust with embedded Perl

Thanks to a colleague who introduced me to Nim during last week’s SUSE Labs conference, I became a man with a dream, and after fiddling with compiler flags and obviously not reading documentation, I finally made it.

This is something that shouldn’t exist; from the list of ideas that should never have happened.

But it does. It’s a Perl interpreter embedded in Rust. Get over it.

Once cloned, you can run the following commands to see it in action:

  • cargo run --verbose -- hello.pm showtime
  • cargo run --verbose -- hello.pm get_quick_headers

How it works

There is a lot of autogenerated code, mainly for two things:

  • bindings.rs and wrapper.h; I made a lot of assumptions and perlxsi.c may or may not be necessary in the future (see main::xs_init_rust), depends on how bad or terrible my C knowledge is by the time you’re reading this.
  • xs_init_rust function is the one that does the magic, as far as my understanding goes, by hooking up boot_DynaLoader to DynaLoader in Perl via ffi.

With those two bits in place, and thanks to the magic of the bindgen crate, and after some initialization, I decided to use Perl_call_argv, do note that Perl_ in this case comes from bindgen, I might change later the convention to ruperl or something to avoid confusion between that a and perl_parse or perl_alloc which (if I understand correctly) are exposed directly by the ffi interface.

What I ended up doing, is passing the same list of arguments (for now, or at least for this PoC), directly to Perl_call_argv, which will in turn, take the third argument and pass it verbatim as the call_argv

        Perl_call_argv(myperl, perl_sub, flags_ptr, perl_parse_args.as_mut_ptr());

Right now hello.pm defines two sub routines, one to open a file, write something and print the time to stdout, and a second one that will query my blog, and show the headers. This is only example code, but enough to demostrate that the DynaLoader works, and that the embedding also works :)

itsalive

I got most of this working by following the perlembed guide.

Why?

Why not?.

I want to see if I can embed also python in the same binary, so I can call native perl, from native python and see how I can fiddle all that into os-autoinst

Where to find the code?

On github: https://github.com/foursixnine/ruperl or under https://crates.io/crates/ruperl

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the avatar of openSUSE News

openSUSE Tumbleweed Monthly Update - April

Welcome to the monthly update for openSUSE Tumbleweed for April 2024. This month began after addressing last month’s supply chain attack against xz compression library for the rolling release. An explanation of that XZ Backdoor, how it was address and what was learned can be found on news.opensuse.org.

A flurry of updates, enhancements, and crucial security fixes arrived in openSUSE’s rolling release this month as the busy season for conferences begins. Should readers desire a more frequent amount of information about snapshot updates, readers are encouraged to subscribe to the openSUSE Factory mailing list.

New Features and Enhancements

  • Linux Kernel: The month of April had a few kernel updates. Notable changes with the 6.8.5 version included mitigation for Branch History Injection (BHI) vulnerabilities, improvements to Spectre mitigation, updates for Intel graphics drivers, fixes for SMB client vulnerabilities and fixes for RISC-V architecture. Version 6.8.7 included updates and fixes for AMD display drivers, Intel i915 driver, x86 speculative execution vulnerabilities, arm 64 device tree files, DRM drivers, filesystem handling, and more.
  • KDE Frameworks 6.1.0: The numpy package introduces enhanced support for structured arrays and flexible indexing, while pandas incorporates improved handling of missing data and new methods for data manipulation. Additionally, the matplotlib package offers enhanced customization options for plot aesthetics. New algorithms for machine learning tasks in scikit-learn were included in the update.
  • KDE Gear 24.02.2: The KDE Gear 24.02.2 update encompasses a wide range of fixes and enhancements, including resolving issues with tag addition functionality in Akonadi, addressing translated shortcut and icon appearance problems in Akregator, various improvements and fixes in ark such as disabling RAR4 compression method, multiple fixes in Elisa including volume slider and track playback issues and numerous enhancements in Konsole. There were fixes for calendar selection and the todo view updates in Korganizer.
  • PHP8 8.3.6: There were significant bug fixes, security patches and improvements across different components including in the update. Besides fixes with Core, DOM, GD, Opcache and Session other fixes include:
    • FPM: Fixes have been applied to address issues with the configuration test running twice in daemonized mode and incorrect checks in fpm_shm_free().
    • Gettext: Fixes have been made to address issues with dcgettext and dcngettext calls with specific configurations.
    • MySQLnd: Various fixes have been applied, including correcting handshake response and charset length checks.
    • Random: Compatibility improvements have been introduced for PHP versions prior to 8.2, and issues with global Mt19937 reset have been resolved.
    • Standard: Validation has been added for specific characters in the mail() function, and various bug fixes have been implemented, including addressing command injection and cookie bypass vulnerabilities. (Noted in CVE-2024-1874, CVE-2024-2756 and fixing issues with mb_encode_mimeheader and password_verify with CVE-2024-3096 and CVE-2024-2757.
  • Mozilla Firefox 125.0.2. The browser brought new features such as:
    • Support for AV1 codec in Encrypted Media Extensions (EME) for improved video playback quality.
    • Enhanced PDF viewer capabilities with text highlighting.
    • Introduction of the URL Paste Suggestion feature, improving usability by allowing quick navigation to URLs copied to the clipboard.
    • Multiple critical security fixes addressing vulnerabilities like out-of-bounds reads and use-after-free errors that enhance browser security.
  • dracut: There were improvements such as the addition of tpm2.target and systemd-tpm2-generator and several memory leak fixes.
  • ffmpeg: Versions 4 and 6 took care of some video handling issues and made fixes for memory leaks with improved EOF handling. The updates addresses:
  • sqlite3: An update from version 3.45.2 to 3.45.3 addresses a long-standing bug affecting the accuracy of trigger responses in certain UPSERT operations to ensure for more reliable database operations.
  • Flatpak: The 1.15.8 update had some security fixes to prevent sandbox escape and various other usability improvements.
  • Python3.11: The 3.11.9 version had various security patches and bug fixes, such as addressing CVE-2023-52425, updating bundled libexpat to version 2.6.0, fixing possible crashes in collections.deque.index() and improves SSLContext behavior.
  • Cppcheck: New checks in version 2.14.0 include:
    • eraseIteratorOutOfBounds: Warns about calling erase() on an iterator that is out of bounds, enhancing the robustness of code.
    • returnByReference: Warns when a large class member is returned by value from a getter function, which can impact performance and memory usage.

Other Package Updates

  • SDL2: Version 2.30.2 introduces support for various new controllers, including the 6-button SEGA Mega Drive Control Pad and the Hori Fighting Stick EX2.
  • Cryptsetup: Version 2.7.2 addressed several issues, including fixes for OPAL device formatting and activation.
  • SpamAssassin: A package with a great name, version 4.0.1 enhances URL shortener link redirection handling and improved TxRep locking management, which bolsters email security for users.

Bug Fixes

Conclusion

The month of April 2024 had a blend of feature enhancements and crucial security fixes. From improved gaming support with SDL2 to strengthened encryption practices with Cryptsetup, users benefited from a host of updates aimed at enhancing functionality, stability and security. Other packages to update in Tumbleweed during the month were Mesa, GTK4, transactional-update and more .

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Contributing to openSUSE Tumbleweed

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