Month: January 2025

Categories
Ubuntu

Enable AAC A2DP codec Ubuntu 24.04

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

  1. Install pipewire with AAC patch
  2. Switch from PulseAudio to Pipewire
  3. Enable it from the sound configuration

Lets do it

First, enable this PPA https://launchpad.net/%7Eaglasgall/+archive/ubuntu/pipewire-extra-bt-codecs

sudo add-apt-repository ppa:aglasgall/pipewire-extra-bt-codecs
sudo apt update

Then, re-install pipewire

sudo apt remove pulseaudio-module-bluetooth
sudo apt install --reinstall pipewire-audio-client-libraries libspa-0.2-bluetooth libspa-0.2-jack

Next, we configure it

sudo apt install pipewire-audio-client-libraries libspa-0.2-bluetooth libspa-0.2-jack

sudo apt install wireplumber pipewire-media-session-

systemctl --user --now enable wireplumber.service

sudo cp /usr/share/doc/pipewire/examples/alsa.conf.d/99-pipewire-default.conf /etc/alsa/conf.d/

sudo apt remove pulseaudio-module-bluetooth

sudo cp /usr/share/doc/pipewire/examples/ld.so.conf.d/pipewire-jack-*.conf /etc/ld.so.conf.d/

sudo ldconfig

Credit to https://ubuntuhandbook.org/index.php/2022/04/pipewire-replace-pulseaudio-ubuntu-2204/

Now enable and verify

systemctl --user --now enable wireplumber.service

pactl info
Server String: /run/user/1000/pulse/native
Library Protocol Version: 35
Server Protocol Version: 35
....
Server Name: PulseAudio (on PipeWire 1.0.5) <------ HERE
Server Version: 15.0.0
Default Sample Specification: float32le 2ch 48000Hz
Default Channel Map: front-left,front-right
Default Sink: bluez_output.AC_BF_71_CF_15_96.1
Default Source: alsa_input.usb-

Voila Done!

Categories
Tensorflow

Solve TF-TRT Warning: Could not find TensorRT

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)

This showing result for my tensorflow version, the tensor rt version is “(8.6.1)”. Next, go download tensorrt version eg: https://developer.nvidia.com/nvidia-tensorrt-8x-download

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

(tf) ➜  python ls
tensorrt-8.6.1-cp310-none-linux_x86_64.whl           tensorrt_dispatch-8.6.1-cp37-none-linux_x86_64.whl
tensorrt-8.6.1-cp311-none-linux_x86_64.whl           tensorrt_dispatch-8.6.1-cp38-none-linux_x86_64.whl
tensorrt-8.6.1-cp36-none-linux_x86_64.whl            tensorrt_dispatch-8.6.1-cp39-none-linux_x86_64.whl
tensorrt-8.6.1-cp37-none-linux_x86_64.whl            tensorrt_lean-8.6.1-cp310-none-linux_x86_64.whl
tensorrt-8.6.1-cp38-none-linux_x86_64.whl            tensorrt_lean-8.6.1-cp311-none-linux_x86_64.whl
tensorrt-8.6.1-cp39-none-linux_x86_64.whl            tensorrt_lean-8.6.1-cp36-none-linux_x86_64.whl
tensorrt_dispatch-8.6.1-cp310-none-linux_x86_64.whl  tensorrt_lean-8.6.1-cp37-none-linux_x86_64.whl
tensorrt_dispatch-8.6.1-cp311-none-linux_x86_64.whl  tensorrt_lean-8.6.1-cp38-none-linux_x86_64.whl
tensorrt_dispatch-8.6.1-cp36-none-linux_x86_64.whl   tensorrt_lean-8.6.1-cp39-none-linux_x86_64.whl

And I installed

pip install tensorrt-8.6.1-cp311-none-linux_x86_64.whl

Done, now your tensor rt warning is gone!

If you are looking for Ubuntu 24.04 Nvidia deb packages

https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/

For CUDNN, NUMA and others, you can follow this

https://stackoverflow.com/questions/78894063/how-can-i-resolve-tensorflow-warnings-cudnn-cufft-cublas-and-numa

Categories
ML

Solve no matches found: tensorflow[and-cuda]

I found that Tensorflow 2.15 under python 3.9 not able to find my GPU. With nvcc already installed and using mamba, the solution is quite easy

python3 -m pip install 'tensorflow[and-cuda]'

Or 

pip install 'tensorflow[and-cuda]'

# with version
pip install 'tensorflow[and-cuda]==2.15.1'