Resolving the no external monitor signal error after updating Ubuntu 24.04

After installing system updates on my Ubuntu 24.04 Thinkpad P53 with a NVIDIA T1000 GPU, sometimes my external HDMI monitor will no longer get any display signal from my laptop after rebooting. This happened to me many times over the years; and every time I had to search for the solution to the problem. This could take a while and wasted my time. So for posterity's sake, I am writing this post so that I can refer to it in the future.

If the Ubuntu system updates borks up your external monitor display, you can do the following:

 Check if the NVIDIA drivers are installed correctly

1. Open up a terminal and type in the following command:
   
   $ sudo nvidia-smi
   
   
2.  If the above command shows a table as shown below, then your drivers are installed.
   
   
   
3. If not, then try to find and install the correct NVIDIA driver. 
   
   

Finding the correct NVIDIA driver for your system

1.  In a terminal, type in the following command:
   
   $ sudo ubuntu-drivers devices
   
   
2. In the following list that appears, take note of the recommended driver, e.g. nvidia-driver-595-open.
   
   
   
   

 Configure Ubuntu to use the recommended NVIDIA driver 

1.  Click the Ubuntu icon on the desktop and then choose the Additional drivers icon. 
   
   The Software & Updates dialog box appears.
   
   
   
2. In the list of drivers, toggle on the recommended driver, e.g. nvidia-driver-595-open. Then click Apply Changes. 
   
   
   
3. Reboot the machine.

 After restarting, the external monitor should be displaying the signal from the laptop.

Fixing the Tensorflow error: could not load dynamic library 'libcudart.so.11.0'

I tried to install and run Tensorflow on a Ubuntu 20.04 laptop with a Nvidia GPU but I encountered the "could not load dynamic library 'libcudart.so.11.0'" error message, as shown in the screenshot below.

To resolve the issue, I had to install the Nvidia kernel and Cuda 11 libraries from the Nvidia repository. The steps are outlined below.

1. On the Ubuntu machine, open a Terminal. Type in the following commands to add the Nvidia ppa repository:
   
   $ 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/ /"
   
   
2. In the Terminal, type in the commands to install the Nvidia kernel.
   
   $ sudo apt-get update && sudo apt-get install -y nvidia-kernel-source-460
   
   
3. Finally, install Cuda with the following command.
   
   $ sudo apt-get -y install cuda
   
   

Subsequently when importing the Tensorflow library, the error message no longer appears.

Note: Download and install any additional missing libraries from https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ if necessary.

Creating a simple CUDA with CMake C++ project

Assuming you have downloaded and installed the right version of the NVIDIA CUDA Toolkit for your NVIDIA GPU card, the next step is to create your awesome C++ CUDA project. In this post, I will illustrate how to create a simple Hello World project using CMake on Ubuntu. 

1. In a folder, create a CUDA C++ file, e.g. hello.cu. Type in the following code as shown in the snippet below.
   
   Note: the main function simply calls an empty CUDA mykernel consisting of 1 block and 1 thread per block function. Then it prints out a hello message.
   
   
2. Next, create a CMakeLists.txt file. Type in the following code as shown.
   
   Note: The CMakeLists.txt simply tells CMake to use C++ and CUDA languages and then defines the executable and its source.
   
   
   
3. Create a build directory. Then change directory into the build directory and run the cmake command.
   
   $ mkdir build
   $ cd build
   $ cmake ..
   
   The processing messages appear.
   
   
   
4. Now compile the project with the make command.
   
   $ make
   
   
   
   
   
5. Finally, run the hello executable.
   
   $ ./hello
   
   The message "Hello CUDA!" is printed to the screen.
   

The sample project can be downloaded from the Github repository at https://gitlab.com/dominoc925/hello-cuda-cmake

CUDA11.2.props not found in Visual Studio Community 2019

While trying out the NVIDIA CUDA development sample solutions in Visual Studio Community 2019, I encountered error messages about some CUDA11.2.props files not found. An example is shown in the screen shot below.

The solution to this is simple: simply do the following:

1. Using the Windows Explorer, browse to the location of the NVIDIA CUDA Toolkit e.g. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\visual_studio_integration\MSBuildExtensions\.
   
   
   
   
2. Select and copy all the files inside the folder: CUDA 11.2.props, CUDA 11.2.targets, CUDA 11.2.xml, and Nvda.Build.CudaTasks.v11.2.dll.
   
   
3. Browse to the folder C:\Program Files(x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\BuildCustomizations\.
   
   
   
4. Paste the copied CUDA files inside.
   
   
   
   
   

Now, when compiling the CUDA sample solutions, the error messages no longer appear, as shown below.