Docker Desktop における GPU サポート
もくじ
メモ
現時点において Docker Desktop における GPU サポートは、WSL2 バックエンドを使った Windows 上においてのみ利用可能です。
WSL2 のもとでの NVIDIA GPU の利用
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 nvcr.io/nvidia/k8s/cuda-sample:nbody 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