Docker容器，针对不同集群环境的配置方法

集群环境

文章目录

重点关注cuda版本。
尝试运行 【Step1】

docker run --name yen_v3 --runtime=nvidia --gpus=all -v ~/workspace/NeRF:/zj -tid 172.20.208.7/zhaojing_repo/nerf:yen37_v2 bash

报错：
docker: Error response from daemon: Unknown runtime specified nvidia.

尝试解决方案1：
在 /etc/docker/daemon.json里的内容如下：

{
    "registry-mirrors": ["https://f1z25q5p.mirror.aliyuncs.com"],
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

然后命令
sudo systemctl daemon-reload
sudo systemctl restart docker

执行 sudo systemctl restart docker 的时候，报错：Job for docker.service failed because the control process exited with error code. See "systemctl status docker.service" and "journalctl -xe" for details.

错误原因： 上一步在编辑 daemon.json文档的时候，存在编辑错误。

重新尝试运行【Step1】，运行成功
【Step2】配置环境参数如下：

Package                 Version
----------------------- -----------
absl-py                 1.0.0
cachetools              5.0.0
certifi                 2021.10.8
charset-normalizer      2.0.12
ConfigArgParse          1.5.3
cycler                  0.11.0
dataclasses             0.6
fonttools               4.31.2
future                  0.18.2
google-auth             2.6.2
google-auth-oauthlib    0.4.6
grpcio                  1.44.0
idna                    3.3
imageio                 2.16.1
imageio-ffmpeg          0.4.5
importlib-metadata      4.11.3
kiwisolver              1.4.0
Markdown                3.3.6
matplotlib              3.5.1
numpy                   1.21.5
oauthlib                3.2.0
opencv-python           4.5.5.64
packaging               21.3
Pillow                  9.0.1
pip                     22.0.4
protobuf                3.19.4
pyasn1                  0.4.8
pyasn1-modules          0.2.8
pyparsing               3.0.7
python-dateutil         2.8.2
requests                2.27.1
requests-oauthlib       1.3.1
rsa                     4.8
setuptools              57.5.0
six                     1.16.0
tensorboard             2.8.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit  1.8.1
torch                   1.7.0+cu110
torchaudio              0.7.0
torchvision             0.8.1+cu110
tqdm                    4.63.1
typing_extensions       4.1.1
urllib3                 1.26.9
Werkzeug                2.0.3
wheel                   0.37.1
zipp                    3.7.0

查看nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.80       Driver Version: 460.80       CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| 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  GeForce RTX 208...  Off  | 00000000:01:00.0  On |                  N/A |
| 30%   42C    P8    12W / 250W |    240MiB / 11014MiB |      1%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

【Step3】尝试本地运行：python run_nerf.py --config configs/fern.txt.运行成功。

【Step4】将环境提交并push

docker commit -m "hh" yen_v3 172.20.208.7/zhaojing_repo/nerf:yen_cu110
docker push 172.20.208.7/zhaojing_repo/nerf:yen_cu110

【Step5】服务器运行测试

失败。

暂时没有找到解决的办法，后面有时间了再探索。