Memory at f0000000 (64-bit, prefetchable) Įxpansion ROM at 000c0000 įor optimus (laptops and notebooks), you will not see NVIDIA for the primary card. Failed to identify your Debian suite.Ġ1:00.0 3D controller: NVIDIA Corporation GM108M (rev a2)įlags: bus master, fast devsel, latency 0, IRQ 132, IOMMU group 10 To detect the NVIDIA card, we need to install nvidia-detect: :~$ sudo apt install -y nvidia-detectĠ1:00.0 3D controller : NVIDIA Corporation GM108M (rev a2)Ĭhecking card: NVIDIA Corporation GM108M (rev a2) You can see what the primary card is by doing: :~$ lspci | grep -i vgaĠ0:02.0 VGA compatible controller: Intel Corporation HD Graphics 620 (rev 02) You may also not even see NVIDIA listed at all. They nvidia-detect package may fail in places due to Kali being a rolling distribution as it requires a stable release.

Notice how Kernel driver in use & Kernel modules from lspci are using nouveau, signalling the open-source driver for NVIDIA cards. After installing podman, we can proceed to install the NVIDIA Container Toolkit. For podman, we need to use the nvidia-container-toolkit package. CUDA Toolkit 11.5.1 (November 2021), Versioned Online Documentation CUDA Toolkit 11.5.0 (October 2021), Versioned Online Documentation CUDA Toolkit 11.4.4 (February 2022), Versioned Online Documentation CUDA Toolkit 11.4.3 (November 2021), Versioned Online Documentation CUDA Toolkit 11.4. See the architecture overview for more details on the package hierarchy. It's better to use docker version, it gives you more details. To use these features, you can download and install Windows 11 or Windows 10, version 21H2. This command works for nvidia-docker too, we add a single line on top of the output. nvidia-docker version NVIDIA Docker: 1.0.0 Client: Version: 1.13.0 API version: 1.25 Go version: go1.7.3 Git commit: 49bf474 Built: Tue Jan 17 09:58:26 2017 OS/Arch: linux/amd64 Server. 11.7.1 / Aug20 days ago () Operating system Windows, Linux Platform, Supported GPUs. I have multiple CUDA versions installed on the server, e.g., /opt/NVIDIA/cuda-9.1 and /opt/NVIDIA/cuda-10, and /usr/local/cuda is linked to the latter one.

Install the GPU driverĭownload and install the NVIDIA CUDA enabled driver for WSL to use with your existing CUDA ML workflows. For more info about which driver to install, see: I believe I installed my pytorch with cuda 10.2 based on what I get from running.

Geforce 6200 turbocache cuda cores