nvidia

Fixing Display Settings Issue in Ubuntu 24.04: “Changes Cannot Be Applied” Error

 in Bash / GNU/Linux  tagged bash / display / Gnome Display Manager / GNU/Linux / GPU / nvidia / ubuntu by

Ubuntu 24.04 is a powerful and user-friendly Linux distribution, but like any operating system, it sometimes presents users with challenges that need troubleshooting. One such issue you may encounter when adjusting display settings is the following error message:

“Changes cannot be applied. This could be due to hardware limitations.”

This error typically appears when attempting to modify the screen resolution or switch display configurations, such as external monitors. Despite having the necessary hardware (e.g., an NVIDIA GPU), users find that changes cannot be applied, leaving them limited control over display settings.

In this blog post, we’ll explain a simple solution to resolve this issue and ensure your display works as expected.

The Problem

In some instances, this error is not necessarily due to hardware limitations but rather a misconfiguration or a missing driver that prevents Ubuntu from properly interfacing with your NVIDIA graphics card. While Ubuntu’s open-source Nouveau drivers are functional for basic tasks, they often lack the features needed to fully utilize the power of NVIDIA GPUs, particularly for advanced display settings or multi-monitor setups.

The Solution: Installing Proprietary NVIDIA Drivers

To fix this issue, you must ensure the proprietary NVIDIA drivers are installed and configured correctly. The following steps will guide you through the process.

Step 1: Install NVIDIA Drivers

The first step is to install the appropriate NVIDIA drivers for your GPU. Ubuntu provides a convenient tool for this:

  1. Open a terminal by pressing Ctrl + Alt + T.
  2. Enter the following command to detect and install the best drivers for your system automatically:
   sudo ubuntu-drivers autoinstall

This command will scan your system’s hardware and install the proprietary drivers NVIDIA provides.

Step 2: Restart Your System

Once the drivers are installed, it’s important to reboot your system to ensure that the changes take effect:

sudo reboot

After restarting, Ubuntu should use the new drivers, but we still need to configure the display settings for optimal performance.

Step 3: Configure NVIDIA PRIME Profiles

NVIDIA’s PRIME technology allows systems with integrated graphics (like Intel) and discrete graphics (NVIDIA) to switch between different power modes. In many cases, Ubuntu will default to using the integrated GPU to conserve power, which can limit display capabilities. We need to switch to the NVIDIA GPU for better performance.

  1. Open the NVIDIA Settings application by searching for it in the application menu or running the following command in the terminal:
   nvidia-settings
  1. In the NVIDIA Settings window, navigate to the PRIME Profiles section.
  2. Select the NVIDIA (Performance Mode) option. This setting ensures that the system uses the discrete NVIDIA GPU, which provides better performance and full control over display settings.
  3. Apply the changes and close the application.
https://youtu.be/TSAZPFgFDZM

Step 4: Restart Again

For the changes to fully take effect, you’ll need to reboot your system one more time:

sudo reboot

After this final reboot, your system should now be using the NVIDIA GPU for display rendering, and you should be able to modify the display settings without encountering the “Changes cannot be applied” error.

Conclusion

If you could not change display settings in Ubuntu 24.04 and received the “Changes cannot be applied” error, the issue likely stemmed from a misconfigured or missing NVIDIA driver. You can restore full control over your display settings by installing the proprietary drivers and configuring PRIME profiles to use the NVIDIA GPU.

Following the steps in this guide should resolve the problem, enabling you to adjust your display settings as needed. If you encounter further issues, check the NVIDIA Settings application or verify that your drivers are up to date.

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Compiling ffmpeg with NVIDIA GPU Hardware Acceleration on Ubuntu 20.04LTS

 in Applications  tagged cuda / ffmpeg / GPU / nvidia / ubuntu by

Please note that the following commands were executed on a system that already had CUDA support so we might be missing a step or two to enable NVIDIA CUDA support.

Install necessary packages

sudo apt-get install build-essential yasm cmake libtool libc6 libc6-dev unzip wget libnuma1 libnuma-dev nvidia-cuda-toolkit;

Clone and install ffnvcodec

git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers.git;
cd nv-codec-headers;
sudo make install;
cd -;

Clone and compile FFmpeg’s public GIT repository with NVIDIA GPU hardware acceleration

git clone https://git.ffmpeg.org/ffmpeg.git ffmpeg/;
cd ffmpeg;
./configure --enable-nonfree --enable-cuda-nvcc --enable-libnpp --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 --disable-static --enable-shared;
make -j 8;
sudo make install;

SUCCESS!

After performing the above steps, we were able to process media using ffmpeg without stressing our CPU! The workload was transferred to the GPU!

Rough notes on how to install CUDA on an Ubuntu 20.04LTS 1

 in GNU/Linux  tagged cuda / GeForce / GeForce RTX 2080 / graphics / linux / nvidia / RTX / RTX 2080 / TensorFlow / ubuntu by

To anyone coming across this post, please note that Canonical does not officially support CUDA. Not being supported formally means that you could face problems that we did not while setting up our machine.

Recently we wanted to use our GPU to execute various TensorFlow projects. We had to use version 1, specifically version 1.15 of TensorFlow, on one of our attempts. That setup was causing many problems even after version 2 was working perfectly. There must be something different with version 1, and it is not supported correctly anymore. We recommend avoiding using it unless needed.

Finding out what graphics card we have

Before getting started, we executed the following command that gave us the model of our graphics card:

lspci | grep -i nvidia;

$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation TU104 [GeForce RTX 2080 Rev. A] (rev a1)
01:00.1 Audio device: NVIDIA Corporation TU104 HD Audio Controller (rev a1)
01:00.2 USB controller: NVIDIA Corporation TU104 USB 3.1 Host Controller (rev a1)
01:00.3 Serial bus controller [0c80]: NVIDIA Corporation TU104 USB Type-C UCSI Controller (rev a1)

Installing dependencies and NVidia repositories

Then we proceeded to install the headers of our Linux kernel, the repositories of NVidia, and finally install CUDA using apt-get.

sudo apt-get install linux-headers-$(uname -r);
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin;
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600;
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub;
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /";
sudo apt-get update;
sudo apt-get -y install cuda;
sudo apt-get install nvidia-gds;

After this step, we rebooted the computer to load the NVidia graphics driver.

Quick note on setting up our programming environment for Coursera.org “DeepLearning.AI TensorFlow Developer Professional Certificate” on Ubuntu 18.04LTS

 in Programming  tagged DeepLearning.AI / GPU / jupyter / LD_LIBRARY_PATH / libcublas / nvidia / TensorFlow / ubuntu by

Recently we were working on some local Jupyter notebooks for the “DeepLearning.AI TensorFlow Developer Professional Certificate“.

To prepare for the setup with GPU, we followed the instructions at https://www.tensorflow.org/install/gpu. But as we were going through the notebooks we noticed the following error at the terminal:

W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcublas.so.10'; dlerror: libcublas.so.10: cannot open shared object file: No such file or directory

Even though libcublas.so.10 was installed it was not loading. Using find was saw that it was installed and available at the folder /usr/local/cuda-10.2/targets/x86_64-linux/lib/ but it was ignored.

To fix this, we added a new entry in ~/.profile as follows:

if [ -d "/usr/local/cuda-10.2/targets/x86_64-linux/lib/" ]; then
    export LD_LIBRARY_PATH=/usr/local/cuda-10.2/targets/x86_64-linux/lib/:${LD_LIBRARY_PATH}
fi

And then executed source ~/.profile from the terminal which we would start the jupyter-notebook command.

After this, library was loaded as expected.