# 主机能看到 GPU 占用进程号，但不知道运行在哪
$ nvidia-smi

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A   1794854      C   ./bin/llama_example             31534MiB |
|    1   N/A  N/A   1916178      C   ...onserver/bin/tritonserver    15454MiB |
|    2   N/A  N/A   1809897      C   /usr/bin/python3                16738MiB |
|    7   N/A  N/A   3306896      C   /bin/python                     39964MiB |
+-----------------------------------------------------------------------------+

# 核心指令
# 比如查看 PID 1809897
# 找到了目标容器的启动命令， 以及容器名称为 lmdeploy
$ ps -e -o pid,cmd,comm,cgroup | grep 1809897
1809897 /usr/bin/python3 /usr/local lmdeploy        12:perf_event:/system.slice/docker-d33d781cbad158192f3819f1118f46ccb1979298a563f6d19f627d8602e6edfb.scope,11:freezer:/system.slice
3758737 grep --color=auto 1809897   grep            8:devices:/system.slice/ssh.service,7:pids:/system.slice/ssh.service,6:blkio:/system.slice/ssh.service,4:cpu,cpuacct:/system.slice/ssh.service,3:memory:/system.slice/ssh.service,1:name=systemd:/system.slice/ssh.service,0::/system.slice/ssh.service

# 再次通过 docker container id 确认
$ docker ps | grep d33d781cbad1
d33d781cbad1        nvcr.io/nvidia/tritonserver:22.12-py3       ...    lmdeploy

# 此时可以 exec 进入容器，进行删除