This post aims to centralize the information on how to use your Nvidia GPUs on debian using WSL2 in order to train and run your AI/ML models. If you use ubuntu there is a nice guide here.
- debian 11
- Windows 10 version > 21H2 (check out in: About -> Version)
- Nvidia GPU
- GPU drivers installed on Windows (find your own)
Source: Run Linux GUI apps on the Windows Subsystem for Linux (Microsoft docs)
- Open PowerShell as admin
Install the custom CUDA drivers
Source: CUDA toolkit documentation (Nvidia docs), Installer for debian
We are going to install custom cuda drivers instead of the ones provided by the usual packages. This is needed for WSL2 compatibility.
# Remove old driver keys sudo apt-key del 7fa2af80 sudo apt install -y software-properties-common wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb sudo add-apt-repository contrib sudo apt update sudo apt -y install cuda
git clone https://github.com/nvidia/cuda-samples cd ~/Dev/cuda-samples/Samples/1_Utilities/deviceQuery make ./deviceQuery rm -r cuda-samples
You should see something like this
./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "NVIDIA GeForce RTX 3090" CUDA Driver Version / Runtime Version 11.7 / 11.7 CUDA Capability Major/Minor version number: 8.6 Total amount of global memory: 24576 MBytes (25769279488 bytes) (082) Multiprocessors, (128) CUDA Cores/MP: 10496 CUDA Cores GPU Max Clock rate: 1695 MHz (1.70 GHz) Memory Clock rate: 9751 Mhz Memory Bus Width: 384-bit L2 Cache Size: 6291456 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total shared memory per multiprocessor: 102400 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 1536 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 6 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device supports Managed Memory: Yes Device supports Compute Preemption: Yes Supports Cooperative Kernel Launch: Yes Supports MultiDevice Co-op Kernel Launch: No Device PCI Domain ID / Bus ID / location ID: 0 / 9 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.7, CUDA Runtime Version = 11.7, NumDevs = 1 Result = PASS
That should be it!
If you have tensorflow installed you can also check it running:
import tensorflow as tf tf.config.list_physical_devices('GPU')
If you see a list like
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')], then everything works correctly.
INFO: You might need to install tensorflow via conda as explained in the documentation.