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Asked: 2024-09-26T03:36:47+05:30 In:

What are the steps to install NVIDIA drivers and CUDA for a GeForce GTX 1650 mobile GPU on Ubuntu 22.04?

I’ve recently upgraded to a laptop with a GeForce GTX 1650 mobile GPU and decided to hop onto the Linux train with Ubuntu 22.04. However, I’m feeling a bit lost when it comes to installing the NVIDIA drivers and CUDA. I know I need these to unleash the full potential of my GPU, particularly for some deep learning projects I’m eager to get started on.

So, here’s the thing: I’ve done some preliminary digging and found a bunch of tutorials online, but they all seem a bit scattered and overwhelming. I’ve seen warnings about conflicting packages and even how I should uninstall the Nouveau drivers first, but the details are fuzzy. Do I need to do that manually? Or is there an easier way?

Also, I’ve come across different installation methods like using the command line and the graphical installer, but I have to admit, the terminal can be intimidating for me. Do most people just go with the command line, or is the graphical option valid too? And what about the CUDA toolkit? I need that for my TensorFlow setup, right? Is it best to install it from the NVIDIA website or can I grab it from Ubuntu’s repositories?

I could really use a simplified step-by-step guide that covers everything: from verifying if the GPU is detected, removing old drivers, installing the right NVIDIA drivers, and finally getting CUDA up and running. It would also be super helpful to know what to do if something doesn’t go right during the process. Like, what’s the best way to troubleshoot if my GPU isn’t recognized after installation?

Has anyone here successfully navigated this maze before? If you could share your experiences or any tips, I’d be incredibly grateful! A little clarity on this would make my transition to working with this GPU on Ubuntu so much smoother. Thanks in advance!

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    1. Installing NVIDIA Drivers and CUDA on Ubuntu 22.04


      NVIDIA Drivers and CUDA Installation Guide for Ubuntu 22.04

      If you’re feeling lost, don’t worry! Here’s a simplified step-by-step guide to help you through the process of installing NVIDIA drivers and CUDA on your new laptop.

      Step 1: Check if your GPU is detected

      Open a terminal (you can search for "Terminal" in the applications):
$ lspci | grep -i nvidia

      If you see your GPU listed, you’re good to go!

      Step 2: Remove existing drivers (like Nouveau)

      Yes, it’s generally a good idea to remove Nouveau drivers to prevent conflicts.

      $ sudo apt-get purge xserver-xorg-video-nouveau

      Then you’ll need to update your initramfs:

      $ sudo update-initramfs -u

      Reboot your machine:

      $ sudo reboot

      Step 3: Install NVIDIA drivers

      You can go with the command line for this or the “Additional Drivers” GUI tool:

      • Command Line: 
        $ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt update
$ sudo apt install nvidia-driver-510
      • Graphical Method:
        Go to “Software & Updates” > “Additional Drivers” tab, and select the NVIDIA driver from the list there. Click “Apply Changes.”

      Reboot your computer again:

      $ sudo reboot

      Step 4: Install CUDA Toolkit

      For TensorFlow, you definitely need CUDA. You can download CUDA from the NVIDIA website, which usually offers the latest version.

      $ wget https://developer.download.nvidia.com/compute/cuda/12.0.0/local_installers/cuda_12.0.0_520.61.05_linux.run
$ sudo sh cuda_12.0.0_520.61.05_linux.run

      Follow the prompts during installation. Make sure to add CUDA to your PATH as instructed in the installer.

      Step 5: Verify the installation

      $ nvcc -V
$ nvidia-smi

      If your GPU shows up in nvidia-smi, congrats! You’ve done it!

      Troubleshooting

      If something goes wrong and your GPU isn’t recognized:

      • Make sure secure boot is disabled in the BIOS. It can prevent the NVIDIA drivers from loading.
      • Check for any error messages in the terminal when you run nvidia-smi.
      • Look into reinstalling the drivers or checking for conflicting packages.

      In the end, most folks use the command line because it’s often more straightforward and powerful, but if you’re more comfortable with the GUI, go for it! Good luck with your deep learning projects!



    2. To set up your GeForce GTX 1650 on Ubuntu 22.04, it’s essential to start by checking whether the system has detected your GPU. You can do this by opening a terminal and running the command lspci | grep -i nvidia. If you see output related to NVIDIA, your GPU is recognized. Before proceeding to install the drivers, it’s crucial to remove any existing Nouveau drivers, which can conflict with NVIDIA drivers. To do this, you can safely uninstall them by executing the command sudo apt-get remove --purge xserver-xorg-video-nouveau in the terminal. After that, you should add the NVIDIA PPA repository to ensure you have access to the latest drivers: sudo add-apt-repository ppa:graphics-drivers/ppa. Update your package list with sudo apt update and then install the recommended driver package, typically nvidia-driver-XXX (where “XXX” is the version number that might be suggested by the previous command).

      After installing the NVIDIA drivers, you can download the CUDA Toolkit, which is crucial for TensorFlow. While you can grab it from the NVIDIA website for the latest version, you might find that installing from Ubuntu’s repositories is more straightforward for dependency management. After installation, verify that CUDA is correctly installed by running nvcc --version in the terminal. If you encounter issues, such as your GPU not being recognized, check the NVIDIA settings GUI or run nvidia-smi to see if the driver is active. If problems persist, common troubleshooting steps include re-checking the driver installation and ensuring secure boot is disabled, as it can sometimes prevent proprietary drivers from loading. Additionally, consider looking through the kernel logs using dmesg | grep nvidia for any error messages that can provide clues for diagnosing the issue. With these steps, you should be set to fully utilize your GPU for deep learning projects.


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