【windows11】GPU+tensorflow環境をwsl2+dockerで実現する。
目的
GPU+tensorflow環境をwsl2+dockerで実現する。なるべくクリーンな環境で試したい。
環境
windows11
wsl2(ubuntu)
NVIDIA GeForce GTX 1050Ti
手順
1. windows11にNVIDIA Driverをインストールする。
僕の場合、勝手にインストールされていた気がします。インストールされていない場合、https://www.nvidia.co.jp/Download/index.aspx?lang=jp でダウンロードできます。
ubuntu上でnvidia-smiを実行し、GPUを認識できていれば成功です。
$ nvidia-smi
Sun Mar 20 11:00:03 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03 Driver Version: 511.65 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| 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 | 00000000:1C:00.0 On | N/A |
| 44% 31C P0 N/A / 75W | 1744MiB / 4096MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
wsl2はwsl1と違い、本物のlinux kernelを使用しているのでこの時点でlinuxがGPUを認識できていると考えて大丈夫です。
2. dockerをwsl2上にインストールする。
ここからはnvidia公式サイト(https://docs.nvidia.com/cuda/wsl-user-guide/index.html#getting-started-with-cuda-on-wsl) の4.2節を流用しているだけです。
wsl2のターミナル上で以下のコマンドを実行します。docker公式でlinuxへのインストール手順があるのでこれを参考にします。(https://docs.docker.jp/linux/step_one.html)
curl -fsSL https://get.docker.com/ | sh
成功すると以下のようなログが出力されます。
curl -fsSL https://get.docker.com/ | sh
# Executing docker install script, commit: 93d2499759296ac1f9c510605fef85052a2c32be
WSL DETECTED: We recommend using Docker Desktop for Windows.
Please get Docker Desktop from https://www.docker.com/products/docker-desktop
You may press Ctrl+C now to abort this script.
+ sleep 20
+ sudo -E sh -c apt-get update -qq >/dev/null
[sudo] password for USERNAME:
+ sudo -E sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -qq apt-transport-https ca-certificates curl >/dev/null
+ sudo -E sh -c curl -fsSL "https://download.docker.com/linux/ubuntu/gpg" | gpg --dearmor --yes -o /usr/share/keyrings/docker-archive-keyring.gpg
+ sudo -E sh -c echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu focal stable" > /etc/apt/sources.list.d/docker.list
+ sudo -E sh -c apt-get update -qq >/dev/null
+ sudo -E sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -qq --no-install-recommends docker-ce-cli docker-scan-plugin docker-ce >/dev/null
+ version_gte 20.10
+ [ -z ]
+ return 0
+ sudo -E sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -qq docker-ce-rootless-extras >/dev/null
================================================================================
To run Docker as a non-privileged user, consider setting up the
Docker daemon in rootless mode for your user:
dockerd-rootless-setuptool.sh install
Visit https://docs.docker.com/go/rootless/ to learn about rootless mode.
To run the Docker daemon as a fully privileged service, but granting non-root
users access, refer to https://docs.docker.com/go/daemon-access/
WARNING: Access to the remote API on a privileged Docker daemon is equivalent
to root access on the host. Refer to the 'Docker daemon attack surface'
documentation for details: https://docs.docker.com/go/attack-surface/
================================================================================
wslなのでdocker desktopをお勧めすると書いてありますが、docker desktopは有償化されてしまったので、無視します。(個人の利用では無料)
dockerとターミナル上に打ち込み、コマンド認識できていればOKです。
3. NVIDIA Container Toolkitをwsl2上にインストールする。
以下のコマンドでtoolkitをインストールします。何をやっているか正直理解できていないですが、公式なので大丈夫でしょう。
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
インストールをします。
sudo apt-get update
sudo apt-get install -y nvidia-docker2
4. 動作確認
dockerがGPUを認識できているか動作確認を行います。
まずdockerを再起動しておきましょう。
sudo service docker stop
sudo service docker start
docker imageをプルしてきて、テストします。
$ sudo docker run --rm --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
Unable to find image 'nvcr.io/nvidia/k8s/cuda-sample:nbody' locally
nbody: Pulling from nvidia/k8s/cuda-sample
2f94e549220a: Pull complete
8a196a8ba405: Pull complete
d84024e05c66: Pull complete
c6e912b1cab0: Pull complete
2c392aca5e2c: Pull complete
e12411a32402: Pull complete
246511fe8354: Pull complete
8eec16b7bc5c: Pull complete
9917b9983432: Pull complete
Digest: sha256:2a17caedda57b64f02f85f6ffae682ebbfdeadbf401e1c6a4c561f1b12b2257a
Status: Downloaded newer image for nvcr.io/nvidia/k8s/cuda-sample:nbody
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
GPU Device 0: "Pascal" with compute capability 6.1
> Compute 6.1 CUDA device: [NVIDIA GeForce GTX 1050 Ti]
6144 bodies, total time for 10 iterations: 5.805 ms
= 65.027 billion interactions per second
= 1300.547 single-precision GFLOP/s at 20 flops per interaction
認識できていますね。
5. tensorflowのdocker imageをプルしてテスト
sudo docker run --gpus all -it tensorflow/tensorflow:latest-gpu bash
これでbashに入れます。bash上でpythonを実行して、確認します。
Python 3.8.10 (default, Nov 26 2021, 20:14:08)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>>
>>> from tensorflow.python.client import device_lib
lib.list_local_devices()
>>> device_lib.list_local_devices()
2022-03-20 02:33:15.693566: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-20 02:33:15.827200: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:1c:00.0/numa_node
Your kernel may have been built without NUMA support.
2022-03-20 02:33:15.834763: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:1c:00.0/numa_node
Your kernel may have been built without NUMA support.
2022-03-20 02:33:15.835051: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:1c:00.0/numa_node
Your kernel may have been built without NUMA support.
2022-03-20 02:33:16.524473: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:1c:00.0/numa_node
Your kernel may have been built without NUMA support.
2022-03-20 02:33:16.524820: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:1c:00.0/numa_node
Your kernel may have been built without NUMA support.
2022-03-20 02:33:16.524865: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1609] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-03-20 02:33:16.525211: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:1c:00.0/numa_node
Your kernel may have been built without NUMA support.
2022-03-20 02:33:16.525316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 2752 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:1c:00.0, compute capability: 6.1
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 15340632351876512816
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 2886126798
locality {
bus_id: 1
links {
}
}
incarnation: 10473275661079893773
physical_device_desc: "device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:1c:00.0, compute capability: 6.1"
xla_global_id: 416903419
]
GPUを認識できていますね。
まとめ
wsl2を使うと、docker desktopを使わなくてもdockerを使用できます。かなりクリーンな環境でGPUを利用できるので良いと思います。
Author And Source
この問題について(【windows11】GPU+tensorflow環境をwsl2+dockerで実現する。), 我々は、より多くの情報をここで見つけました https://qiita.com/daikon/items/72b8e0215250b31676c8著者帰属：元の著者の情報は、元のURLに含まれています。著作権は原作者に属する。
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