GPU support in Docker Desktop


Currently GPU support in Docker Desktop is only available on Windows with the WSL2 backend.

Using NVIDIA GPUs with WSL2

Docker Desktop for Windows supports WSL 2 GPU Paravirtualization (GPU-PV) on NVIDIA GPUs. To enable WSL 2 GPU Paravirtualization, you need:

  • A machine with an NVIDIA GPU
  • Up to date Windows 10 or Windows 11 installation
  • Up to date drivers from NVIDIA supporting WSL 2 GPU Paravirtualization
  • The latest version of the WSL 2 Linux kernel. Use wsl --update on the command line
  • To make sure the WSL 2 backend is turned on in Docker Desktop

To validate that everything works as expected, execute a docker run command with the --gpus=all flag. For example, the following will run a short benchmark on your GPU:

$ docker run --rm -it --gpus=all nbody -gpu -benchmark

The output will be similar to:

Run "nbody -benchmark [-numbodies=<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=<N>    (number of bodies (>= 1) to run in simulation)
        -device=<d>       (where d=0,1,2.... for the CUDA device to use)
        -numdevices=<i>   (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.

> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
MapSMtoCores for SM 7.5 is undefined.  Default to use 64 Cores/SM
GPU Device 0: "GeForce RTX 2060 with Max-Q Design" with compute capability 7.5

> Compute 7.5 CUDA device: [GeForce RTX 2060 with Max-Q Design]
30720 bodies, total time for 10 iterations: 69.280 ms
= 136.219 billion interactions per second
= 2724.379 single-precision GFLOP/s at 20 flops per interaction

Or if you wanted to try something more useful you could use the official Ollama image to run the Llama2 large language model.

$ docker run --gpus=all -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
$ docker exec -it ollama ollama run llama2