docker常用指令

一、目录

1. docker 指令
   1. 创建镜像
   2. 保存镜像
   3. 加载镜像
   4. 删除容器
   5. 删除镜像
   6. 查看容器日志
   7. 查看docker 占用内存情况
   8. 查看docker 根目录(安装路径)
   9. 创建一个容器

二、实现

1. docker指令
   1 创建镜像

   docker commit  fd69960ed65f  jyf：0.0.1

2. 保存镜像

docker save -o my.tar hello-word:latest

3. 加载镜像

docker load -i my.tar

4. 删除容器

docker rm CONTAINER-ID   #删除容器id, 或者>>docker rm name        #容器名字
批量删除
docker rm $(docker ps -a -q)        

5. 删除镜像

docker rmi image-id     #删除镜像id

6. 查看容器日志

docker logs CONTAINER-ID   #容器id

7. 查看docker 占用内存情况

[root@localhost ~]# docker system df
TYPE            TOTAL     ACTIVE    SIZE      RECLAIMABLE
Images          81        41        37.12GB   18.87GB (50%)
Containers      58        18        33.98GB   32.39GB (95%)
Local Volumes   42        25        1.598GB   1.492GB (93%)
Build Cache     130       0         562.7MB   562.7MB

8. 查看docker 根目录(安装路径)

[root@dockermain ~]# docker info Client: Docker Engine - Community
Version: 24.0.1 Context: default Debug Mode: false Plugins:
buildx: Docker Buildx (Docker Inc.)
Version: v0.10.4
Path: /usr/libexec/docker/cli-plugins/docker-buildx compose: Docker Compose (Docker Inc.)
Version: v2.18.1
Path: /usr/libexec/docker/cli-plugins/docker-compose scan: Docker Scan (Docker Inc.)
Version: v0.23.0
Path: /usr/libexec/docker/cli-plugins/docker-scan

Server: Containers: 58 Running: 21 Paused: 0 Stopped: 37
Images: 141 Server Version: 24.0.1 Storage Driver: overlay2
Backing Filesystem: xfs Supports d_type: true Using metacopy:
false Native Overlay Diff: true userxattr: false Logging Driver:
json-file Cgroup Driver: systemd Cgroup Version: 2 Plugins:
Volume: local Network: bridge host ipvlan macvlan null overlay
Log: awslogs fluentd gcplogs gelf journald json-file local logentries
splunk syslog Swarm: inactive Runtimes: io.containerd.runc.v2 runc
Default Runtime: runc Init Binary: docker-init containerd version:
3dce8eb055cbb6872793272b4f20ed16117344f8 runc version:
v1.1.7-0-g860f061 init version: de40ad0 Security Options: seccomp
Profile: builtin cgroupns Kernel Version:
5.14.0-284.11.1.el9_2.x86_64 Operating System: AlmaLinux 9.2 (Turquoise Kodkod) OSType: linux Architecture: x86_64 CPUs: 16
Total Memory: 31.09GiB Name: dockermain ID:
b1b354b2-ccd6-4204-9c1d-78ca30da0712
Docker Root Dir: /home/dockerdata #根目录
Debug Mode: false
Experimental: false Insecure Registries:
10.120.130.49:5000
127.0.0.0/8 Registry Mirrors: http://hub-mirror.c.163.com/ Live Restore Enabled: false

9. 创建一个容器

镜像选取：docker pull pytorch/pytorch:1.9.1-cuda11.1-cudnn8-devel
docker run -it --gpus  '"device=1,2"' --name leinao -v /tmp:/tmp  pytorch/pytorch:pytorch:1.9.1-cuda11.1-cudnn8-devel /bin/bash 
docker run -it --gpus '"device=2,3"' -v /jiayafei_linux/:/home/ --name llm_g pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel /bin/bash

docker run -it --gpus '"device=0"' -v /jiayafei_linux/:/home/ --name nodes_0 -p8201:8010 pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel /bin/bash
docker run -it --gpus '"device=1"' -v /jiayafei_linux/:/home/ --name nodes_1 -p8202:8010 pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel /bin/bash

docker run -it --gpus all --name llm-01 -v /:/home  -p8011:8011 -p8012:8012 -p8013:8013 -p8014:8014  pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel /bin/bash