After applying Pipewire with AAC support in Ubuntu 24.04, I got problem where my Bose QuiteComfort is repeatingly disconnecting. The log keep saying input failed to connect
grep blue /var/log/syslog
I did several things like re-install packages and configure the bluetooth /etc/bluetooth/main.conf with AutoEnable=trueand ControllerMode = bredr still not working. Also edit /etc/bluetooth/input.conf with `UserspaceHID=true`.
This will remove your headphones from bluetooth (*reset). Then change your headphones mode into pairing mode.
Then do this to enter the bluetooth terminal
bluetoothctl
Then lets find and pair
power on
scan on
pair Device ID
devices
trust ID
If any notification asking for authorization, then choose “yes”. This is usually causing the problem where paired bluetooth is not authorized. next time its connected, its get problem!
I’m using Bose QuietComfort Ultra and its have capabilities to support multiple codecs. Connecting into Ubuntu, currently its only support for SBC-XQ and SBC. To unlock the lossless compression on AAC in Ubuntu 24.04 we need to do several things
Installing TensorRT is tricky and shall be matched with the version of tensorflow. This is how you do it. First check the required version
python3 -c "import tensorflow.compiler as tf_cc; \
print(tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version())"
And then the result
2025-01-05 16:30:38.552990: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-01-05 16:30:38.576738: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2025-01-05 16:30:38.576767: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2025-01-05 16:30:38.577423: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2025-01-05 16:30:38.581769: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2025-01-05 16:30:39.023446: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
(8, 6, 1)
Extract it and go to “tensorrt/python” folder. Since i’m using mamba, I will go to my environment and install the tensor wheel packages based on my python version
First time installed Ubuntu, I can see the are two option for CTRL+Print Screen which are screenshot and screen recording. However after installing and upgrading other softwares, the screen recording is missing.
Running FFMPEG with NVIDIA is blazing fast! Here is a quick way on how to install and make it run on NVIDIA and Ubuntu. First of all, if you have Ubuntu 24.10, you need to downgrade to 24.04. The reason because several libraries is not ready on the latest Ubuntu version.
I have ASUS motherboard that support both 2.5G and 10G LAN Network. Connected with CAT6 with 10Gbps capacity. However, the default Ubuntu limit the transfer speed into 100Mbps.
Its showed here that my second LAN being capped by 100Mbit/s
3.Here is what I’ve learned. Your LAN will follow the other-side LAN speed. In my case, my PC is 2,500 mbps while my another side is 10,000 mbps. So, instead of make changes on my PC, actually go to another side and adjust it to follow yours!
I got to my workstation
sudo ethtool -s enp36s0f0 speed 2500 duplex full autoneg on
Then, I disconnect and re-connect. Finally the speed is changed. The biggest learning here is to change another side configuration, not your own 2.5G
I found issue where stutter and nvidia-smi took long time to print, which is unusual. After digging more, apparently this is caused by service of persistence mode.
To solve this, you can disable by comment /lib/systemd/system/nvidia-persistenced.service
sudo vim /lib/systemd/system/nvidia-persistenced.service