WSL2 backed docker containers can't see GPU's

Hello everyone,

I am trying to use WSL2 backed docker containers to run my machine learning experiments. I am struggling to get this working. I have re-installed Windows and followed step 2 and step 3(option 1) from CUDA on WSL :: CUDA Toolkit Documentation

Machine:
Processor: AMD Ryzen Threadripper
(Qty 4) - NVIDIA GeForce RTX 2080Ti

Windows:
Edition: Windows 11 Pro
Version: 21H2
OS Build: 22000.1098

Checks:
“wsl -l -v” shows that it is WSL2 running

The installed Nvidia Driver Version is 522.25
The CUDA version is 11.8

In the WSL2 Ubuntu prompt; running “nvidia-smi” yields this screen:

±----------------------------------------------------------------------------+
| NVIDIA-SMI 520.56.05 Driver Version: 522.25 CUDA Version: 11.8 |
|-------------------------------±---------------------±---------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce … On | 00000000:03:00.0 Off | N/A |
| 30% 43C P8 22W / 250W | 0MiB / 11264MiB | 0% Default |
| | | N/A |
±------------------------------±---------------------±---------------------+
| 1 NVIDIA GeForce … On | 00000000:21:00.0 Off | N/A |
| 30% 43C P8 21W / 250W | 615MiB / 11264MiB | 0% Default |
| | | N/A |
±------------------------------±---------------------±---------------------+
| 2 NVIDIA GeForce … On | 00000000:4A:00.0 Off | N/A |
| 30% 35C P8 7W / 250W | 8MiB / 11264MiB | 0% Default |
| | | N/A |
±------------------------------±---------------------±---------------------+
| 3 NVIDIA GeForce … On | 00000000:4B:00.0 Off | N/A |
| 30% 40C P8 18W / 250W | 0MiB / 11264MiB | 0% Default |
| | | N/A |
±------------------------------±---------------------±---------------------+

±----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
±----------------------------------------------------------------------------+

Docker Desktop 4.12.0 (85629) is installed.

To test it with a docker container I tried both:
docker run --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody
and
docker run --env NVIDIA_DISABLE_REQUIRE=1 --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody
both yield this result, unable to find the GPU’s:

Run “nbody -benchmark [-numbodies=]” to measure performance.
-fullscreen (run n-body simulation in fullscreen mode)
-fp64 (use double precision floating point values for simulation)
-hostmem (stores simulation data in host memory)
-benchmark (run benchmark to measure performance)
-numbodies= (number of bodies (>= 1) to run in simulation)
-device= (where d=0,1,2… for the CUDA device to use)
-numdevices= (where i=(number of CUDA devices > 0) to use for simulation)
-compare (compares simulation results running once on the default GPU and once on the CPU)
-cpu (run n-body simulation on the CPU)
-tipsy=<file.bin> (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

Error: only 0 Devices available, 1 requested. Exiting.

I have also run a variety of tensorflow containers and these containers can just see the CPU.

I have tried with the BIOS secure boot both on and off.

It seems like this should be easy but I am struggling to get this to work, I would be grateful for any advice of what to check or try next.