I installed nvidia-driver package from the above, then rebooted, and my resolution was fine afterwards. On your VM, download and install the CUDA toolkit. Connect to the VM where you want to install the driver. To install the NVIDIA toolkit, complete the following steps: Select a CUDA toolkit that supports the minimum driver that you need. This is surprising and unexpected since the cuda-toolkit installation did that thing where it disabled Noveau, though did not even install an Nvidia graphics driver. For VMs that have Secure Boot enabled, see Installing GPU drivers on VMs that use Secure Boot. Note the drivers all under "Suggests", not "Depends". Tag: devel::TODO, devel::compiler, role::program Suggests: nvidia-driver (>= 460) | nvidia-tesla-510-driver (>= 460) | nvidia-tesla-470-driver (>= 460) | nvidia-tesla-460-driver (>= 460) CUDA 11.4 Release Notes NVIDIA CUDA Toolkit 11.4 RN-06722-001 v11.4 2 Component Name Version Information Supported Architectures CUDA NVTX 11.4. If one wants to deinstall using the package manager there is always the danger to deinstall too much or too less, if one works with wildcards in ‘apt remove’.It's because the cuda-toolkit in the debian repo does not depend on an actual Nvidia driver, UNLIKE the cuda-toolkit straight from Nvidia. It seems there is clean way to uninstall everything if necessary very simple by executing an uninstall script. If one follows the official path of install via deb local (or net) and concludes with ‘apt install cuda’ one does, what I did not want, namely replace the drivers too (so one has, what I wanted to avoid “a complete CUDA installation”). If one installs via the package manager, one gets update recommendations automatically, which one has to decline explicitly, if one does not want the local CUDA installation to be a “moving target”. I think using the runfile is for me the better option, because So even if you were to stop conda from performing the dependency installation, there is a version mismatch so it wouldnt work. (By the way, they had a smaller version number than my currently installed ones). As you are now fully aware, versioning is critical to Tensorflow and a Tensorflow build requiring CUDA 10.2 wont work with CUDA 11.2. This is only necessary if one intends to install also the drivers contained in the runfile. So I installed everything but the drivers and therefore could do all selection in my X terminal, no need to reboot and switch to runlevel 3. The runfile, upon executed, displayed a simple ncurses (text based pseudo GUI) interface where it was possible to unselect the option to install the drivers. Also my application asxp (Algebraic Surface eXPlorer) (available on GitHub), which was the ultimate purpose of installing CUDA, compiled and ran perfectly. NVIDIA® CUDA Toolkit 11.2 GA no longer supports development or running applications on macOS. It seems to work flawlessly, I built ‘deviceQuery’ and ‘fluidsGL’ from the samples and they ran ok. I installed 11.2.0 via the runfile installer, avoiding the package manager. Just for the record and for the use of coming-by readers of this thread: Or should I stay with an older version of CUDA Toolkit? Or will the CUDA install also install a newer driver? Is it safe to do so, as far as compatibility between driver 460.80 and CUDA Toolkit 11.4 is concerned? | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. I currently have installed drivers 460.80, see output of nvidia-smi I would like to install the CUDA Toolkit on my Ubuntu 18.04 HP Z240 machine.
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