mirror of https://github.com/docker/docs.git
update wsl2 nvidia doc (#19365)
* Update WSL2 + Nvidia doc The Nvidia drivers have been generally available for a long while now. So change our doc to reflect that, also add a usage example which better illustrates what can be done with the feature. Signed-off-by: Piotr Stankiewicz <piotr.stankiewicz@docker.com> * Add a top-level GPU support doc Currently the WSL2 + nvidia doc is not easily discoverable for people browsing our docs. So add a top level GPU support page and move the WSL2 + nvidia doc there. Signed-off-by: Piotr Stankiewicz <piotr.stankiewicz@docker.com> --------- Signed-off-by: Piotr Stankiewicz <piotr.stankiewicz@docker.com>
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---
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title: GPU support in Docker Desktop
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description: How to use GPU in Docker Desktop
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keywords: gpu, gpu support, nvidia, wsl2, docker desktop, windows
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toc_max: 3
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---
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> **Note**
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>
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> Currently GPU support in Docker Desktop is only available on Windows with the WSL2 backend.
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## Using NVIDIA GPUs with WSL2
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Docker Desktop for Windows supports WSL 2 GPU Paravirtualization (GPU-PV) on NVIDIA GPUs. To enable WSL 2 GPU Paravirtualization, you need:
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- A machine with an NVIDIA GPU
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- Up to date Windows 10 or Windows 11 installation
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- [Up to date drivers](https://developer.nvidia.com/cuda/wsl) from NVIDIA supporting WSL 2 GPU Paravirtualization
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- The latest version of the WSL 2 Linux kernel. Use `wsl --update` on the command line
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- Make sure the [WSL 2 backend is turned on](wsl/index.md/#turn-on-docker-desktop-wsl-2) in Docker Desktop
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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:
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```console
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$ docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
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```
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The output will be similar to:
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```console
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Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
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-fullscreen (run n-body simulation in fullscreen mode)
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-fp64 (use double precision floating point values for simulation)
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-hostmem (stores simulation data in host memory)
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-benchmark (run benchmark to measure performance)
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-numbodies=<N> (number of bodies (>= 1) to run in simulation)
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-device=<d> (where d=0,1,2.... for the CUDA device to use)
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-numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
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-compare (compares simulation results running once on the default GPU and once on the CPU)
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-cpu (run n-body simulation on the CPU)
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-tipsy=<file.bin> (load a tipsy model file for simulation)
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> NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
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> Windowed mode
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> Simulation data stored in video memory
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> Single precision floating point simulation
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> 1 Devices used for simulation
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MapSMtoCores for SM 7.5 is undefined. Default to use 64 Cores/SM
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GPU Device 0: "GeForce RTX 2060 with Max-Q Design" with compute capability 7.5
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> Compute 7.5 CUDA device: [GeForce RTX 2060 with Max-Q Design]
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30720 bodies, total time for 10 iterations: 69.280 ms
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= 136.219 billion interactions per second
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= 2724.379 single-precision GFLOP/s at 20 flops per interaction
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```
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Or if you wanted to try something more useful you could use the official [Ollama image](https://hub.docker.com/r/ollama/ollama) to run the Llama2 large language model.
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```console
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$ docker run --gpus=all -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
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$ docker exec -it ollama ollama run llama2
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```
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@ -1,7 +1,7 @@
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---
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title: Use WSL
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description: How to develop with Docker and WSL 2 and understand GPU support for WSL
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keywords: wsl, wsl 2, gpu support, develop, docker desktop, windows
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keywords: wsl, wsl 2, develop, docker desktop, windows
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---
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## Develop with Docker and WSL 2
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@ -23,52 +23,3 @@ The following section describes how to start developing your applications using
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Alternatively, you can type the name of your default Linux distro in your Start menu, open it, and then run `code` .
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3. When you are in VS Code, you can use the terminal in VS Code to pull your code and start working natively from your Windows machine.
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## GPU support
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> **Note**
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>
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> GPU support is only available in Docker Desktop for Windows with the WSL2 backend.
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With Docker Desktop version 3.1.0 and later, WSL 2 GPU Paravirtualization (GPU-PV) on NVIDIA GPUs is supported. To enable WSL 2 GPU Paravirtualization, you need:
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- A machine with an NVIDIA GPU
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- The latest Windows Insider version from the Dev Preview ring
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- [Beta drivers](https://developer.nvidia.com/cuda/wsl) from NVIDIA supporting WSL 2 GPU Paravirtualization
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- Update WSL 2 Linux kernel to the latest version using `wsl --update` from an elevated command prompt
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- Make sure the WSL 2 backend is turned on in Docker Desktop
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To validate that everything works as expected, run the following command to run a short benchmark on your GPU:
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```console
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$ docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
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```
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The following displays:
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```console
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Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
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-fullscreen (run n-body simulation in fullscreen mode)
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-fp64 (use double precision floating point values for simulation)
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-hostmem (stores simulation data in host memory)
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-benchmark (run benchmark to measure performance)
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-numbodies=<N> (number of bodies (>= 1) to run in simulation)
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-device=<d> (where d=0,1,2.... for the CUDA device to use)
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-numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
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-compare (compares simulation results running once on the default GPU and once on the CPU)
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-cpu (run n-body simulation on the CPU)
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-tipsy=<file.bin> (load a tipsy model file for simulation)
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> NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
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> Windowed mode
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> Simulation data stored in video memory
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> Single precision floating point simulation
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> 1 Devices used for simulation
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MapSMtoCores for SM 7.5 is undefined. Default to use 64 Cores/SM
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GPU Device 0: "GeForce RTX 2060 with Max-Q Design" with compute capability 7.5
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> Compute 7.5 CUDA device: [GeForce RTX 2060 with Max-Q Design]
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30720 bodies, total time for 10 iterations: 69.280 ms
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= 136.219 billion interactions per second
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= 2724.379 single-precision GFLOP/s at 20 flops per interaction
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```
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@ -1160,6 +1160,8 @@ Manuals:
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title: Use WSL
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- path: /desktop/wsl/best-practices/
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title: Best practices
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- path: /desktop/gpu/
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title: GPU support
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- sectiontitle: Additional resources
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section:
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- path: /desktop/kubernetes/
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