docker 中使用gpu

• 普通docker的使用

docker gpu

• 尝试运行docker run --gpus=all -it --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:22.05-py3 bash

• 如果没有正确配置会报错：could not select device driver “” with capabilities: [[gpu]].

• 以下是配置的过程

• apt-get install nvidia-container-runtime

• distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

• curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -

• Warning: apt-key is deprecated. Manage keyring files in trusted.gpg.d instead (see apt-key(8)).
  OK

• curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

• deb https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/KaTeX parse error: Expected 'EOF', got '#' at position 10: (ARCH) / #̲deb https://nvi…(ARCH) /
  deb https://nvidia.github.io/nvidia-container-runtime/stable/ubuntu18.04/KaTeX parse error: Expected 'EOF', got '#' at position 10: (ARCH) / #̲deb https://nvi…(ARCH) /
  deb https://nvidia.github.io/nvidia-docker/ubuntu18.04/$(ARCH) /

• sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

正在设置 libnvidia-container1:amd64 (1.12.0-1) ...
正在设置 libnvidia-container-tools (1.12.0-1) ...
正在设置 nvidia-container-toolkit (1.12.0-1) ...
正在处理用于 libc-bin (2.35-0ubuntu3.1) 的触发器 ...
/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 is not a symbolic link

/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 is not a symbolic link

/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 is not a symbolic link

/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 is not a symbolic link

/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8 is not a symbolic link

/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 is not a symbolic link

/sbin/ldconfig.real: /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 is not a symbolic link

• 可能原因：/usr/local/cuda-11.4/lib64/ 和 /usr/local/cuda-11.4/targets/x86_64-linux/lib是链接关系，再使用copy方法安装cudnn时，部分软连接文件没有拷贝到cuda-11.4/lib64/文件目录

# 解决方案
$ sudo ln -sf  /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.8.0  /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
$ sudo ln -sf /usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.8.0  /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
$ sudo ln -sf /usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.8.0  /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
$ sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.8.0  /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
$ sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8.8.0 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8
$ sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.8.0 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
$ sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.8.0  /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8

• sudo systemctl restart docker

(base) pdd@pdd-Dell-G15-5511:~$ sudo docker run --gpus=all --rm -it --net=host --ipc=host  --ulimit memlock=-1 --ulimit stack=67108864  nvcr.io/nvidia/pytorch:22.05-py3 bash

=============
== PyTorch ==
=============

NVIDIA Release 22.05 (build 37432893)
PyTorch Version 1.12.0a0+8a1a93a

Container image Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Copyright (c) 2014-2022 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

WARNING: CUDA Minor Version Compatibility mode ENABLED.
  Using driver version 470.161.03 which has support for CUDA 11.4.  This container
  was built with CUDA 11.7 and will be run in Minor Version Compatibility mode.
  CUDA Forward Compatibility is preferred over Minor Version Compatibility for use
  with this container but was unavailable:
  [[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]]
  See https://docs.nvidia.com/deploy/cuda-compatibility/ for details.

root@pdd-Dell-G15-5511:/workspace# nvidia-smi
Sat Mar 11 05:45:06 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.161.03   Driver Version: 470.161.03   CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| 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. |

CG

• https://stackoverflow.com/questions/25185405/using-gpu-from-a-docker-container
•