Enable GPU access to WSL2 debian for your AI workloads

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.

Marti Sec
Sep 3, 2022
2 min read
Photo by Dimitris Chapsoulas / Unsplash

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.

- WSL2
- debian 11
- Windows 10 version > 21H2 (check out in: About -> Version)
- Nvidia GPU
- GPU drivers installed on Windows (find your own)

Update WSL2

Source: Run Linux GUI apps on the Windows Subsystem for Linux (Microsoft docs)
  1. Open PowerShell as admin
  2. wsl --update
  3. Restart wsl --shutdown

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 
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo add-apt-repository contrib
sudo apt update
sudo apt -y install cuda

Test it

git clone
cd ~/Dev/cuda-samples/Samples/1_Utilities/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!

Extra: tensorflow

If you have tensorflow installed you can also check it running:

import tensorflow as tf

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.