mirror of https://github.com/docker/docs.git
65 lines
2.9 KiB
Markdown
65 lines
2.9 KiB
Markdown
---
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title: GPU support in Docker Desktop
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linkTitle: GPU support
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weight: 90
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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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- To 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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