docker inspect输出内容详解，推测容器运行命令

原始输出

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false,"ReadonlyRootfs": false,"SecurityOpt": ["label=disable"],"UTSMode": "","UsernsMode": "","ShmSize": 67108864,"Runtime": "nvidia","Isolation": "","CpuShares": 0,"Memory": 0,"NanoCpus": 0,"CgroupParent": "","BlkioWeight": 0,"BlkioWeightDevice": [],"BlkioDeviceReadBps": [],"BlkioDeviceWriteBps": [],"BlkioDeviceReadIOps": [],"BlkioDeviceWriteIOps": [],"CpuPeriod": 0,"CpuQuota": 0,"CpuRealtimePeriod": 0,"CpuRealtimeRuntime": 0,"CpusetCpus": "","CpusetMems": "","Devices": [],"DeviceCgroupRules": null,"DeviceRequests": [{"Driver": "","Count": -1,"DeviceIDs": null,"Capabilities": [["gpu"]],"Options": {}}],"MemoryReservation": 0,"MemorySwap": 0,"MemorySwappiness": null,"OomKillDisable": null,"PidsLimit": null,"Ulimits": [],"CpuCount": 0,"CpuPercent": 0,"IOMaximumIOps": 0,"IOMaximumBandwidth": 0,"MaskedPaths": ["/proc/asound","/proc/acpi","/proc/kcore","/proc/keys","/proc/latency_stats","/proc/timer_list","/proc/timer_stats","/proc/sched_debug","/proc/scsi","/sys/firmware","/sys/devices/virtual/powercap"],"ReadonlyPaths": ["/proc/bus","/proc/fs","/proc/irq","/proc/sys","/proc/sysrq-trigger"]},"GraphDriver": {"Data": {"LowerDir": 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["HUGGING_FACE_HUB_TOKEN=<secret>","PATH=/opt/py38/bin:/opt/tritonserver/bin:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin","CUDA_VERSION=11.8.0.065","CUDA_DRIVER_VERSION=520.61.05","CUDA_CACHE_DISABLE=1","NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=","_CUDA_COMPAT_PATH=/usr/local/cuda/compat","ENV=/etc/shinit_v2","BASH_ENV=/etc/bash.bashrc","SHELL=/bin/bash","NVIDIA_REQUIRE_CUDA=cuda>=9.0","NCCL_VERSION=2.15.5","CUBLAS_VERSION=11.11.3.6","CUFFT_VERSION=10.9.0.58","CURAND_VERSION=10.3.0.86","CUSPARSE_VERSION=11.7.5.86","CUSOLVER_VERSION=11.4.1.48","CUTENSOR_VERSION=1.6.1.5","NPP_VERSION=11.8.0.86","NVJPEG_VERSION=11.9.0.86","CUDNN_VERSION=8.7.0.84","TRT_VERSION=8.5.1.7","TRTOSS_VERSION=22.12","NSIGHT_SYSTEMS_VERSION=2022.4.2.1","NSIGHT_COMPUTE_VERSION=2022.3.0.22","DALI_VERSION=1.20.0","DALI_BUILD=6562491","POLYGRAPHY_VERSION=0.43.1","TRANSFORMER_ENGINE_VERSION=0.3","LD_LIBRARY_PATH=/opt/tritonserver/lib:/opt/tritonserver/backends/onnxruntime:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64","NVIDIA_VISIBLE_DEVICES=all","NVIDIA_DRIVER_CAPABILITIES=compute,utility,video","NVIDIA_PRODUCT_NAME=Triton 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"0.0.0.0","HostPort": "23333"},{"HostIp": "::","HostPort": "23333"}]},"HairpinMode": false,"LinkLocalIPv6Address": "","LinkLocalIPv6PrefixLen": 0,"SecondaryIPAddresses": null,"SecondaryIPv6Addresses": null,"EndpointID": "8e1b1258d03f72237ebaecca9ff78d8951a4b281947bef16b8bc774feca42937","Gateway": "172.16.0.1","GlobalIPv6Address": "","GlobalIPv6PrefixLen": 0,"IPAddress": "172.16.0.5","IPPrefixLen": 16,"IPv6Gateway": "","MacAddress": "02:42:ac:10:00:05","Networks": {"bridge": {"IPAMConfig": null,"Links": null,"Aliases": null,"MacAddress": "02:42:ac:10:00:05","DriverOpts": null,"NetworkID": "b914ed43880d4258355ef85e24f301f098f06a0a4c1d17d826cbb7b4e3881fdb","EndpointID": "8e1b1258d03f72237ebaecca9ff78d8951a4b281947bef16b8bc774feca42937","Gateway": "172.16.0.1","IPAddress": "172.16.0.5","IPPrefixLen": 16,"IPv6Gateway": "","GlobalIPv6Address": "","GlobalIPv6PrefixLen": 0,"DNSNames": null}}}}
]

容器基本信息

{"Id": "c2496d852ee3affd295a177e9f26f163a53da8d11e3708d6a479f189f707ad0b","Created": "2024-08-04T02:43:24.909341103Z","Path": "/startup.sh","Args": [],"State": {"Status": "running","Running": true,"Paused": false,"Restarting": false,"OOMKilled": false,"Dead": false,"Pid": 1308978,"ExitCode": 0,"Error": "","StartedAt": "2024-08-14T11:31:49.644923079Z","FinishedAt": "2024-08-14T11:31:49.206669276Z"},"Image": "sha256:629ec4df10b1d0202f30c9f379dea586ead067722597cedb0f3ebb9eb3337065","ResolvConfPath": "/var/lib/docker/containers/c2496d852ee3affd295a177e9f26f163a53da8d11e3708d6a479f189f707ad0b/resolv.conf","HostnamePath": "/var/lib/docker/containers/c2496d852ee3affd295a177e9f26f163a53da8d11e3708d6a479f189f707ad0b/hostname","HostsPath": "/var/lib/docker/containers/c2496d852ee3affd295a177e9f26f163a53da8d11e3708d6a479f189f707ad0b/hosts","LogPath": "/var/lib/docker/containers/c2496d852ee3affd295a177e9f26f163a53da8d11e3708d6a479f189f707ad0b/c2496d852ee3affd295a177e9f26f163a53da8d11e3708d6a479f189f707ad0b-json.log","Name": "/lmdeploy","RestartCount": 0,"Driver": "overlay2","Platform": "linux","MountLabel": "","ProcessLabel": "","AppArmorProfile": "docker-default","ExecIDs": null
}
  • Id: 容器的唯一标识符。
  • Created: 容器创建的时间。
  • Path: 容器启动时执行的命令，这里是 /startup.sh。
  • Args: 启动命令的参数，这里是空数组，表示没有额外参数。
  • State: 容器的当前状态，包括是否正在运行、进程ID等。
    • Status: 容器的当前状态，这里是 running。
    • Running: 指示容器是否正在运行。
    • Paused: 指示容器是否处于暂停状态。
    • Restarting: 指示容器是否正在重启。
    • OOMKilled: 指示容器是否因内存不足被杀死。
    • Dead: 指示容器是否已死。
    • Pid: 容器内的主进程的 PID。
    • ExitCode: 容器的退出代码。
    • Error: 容器的错误信息。
    • StartedAt: 容器的启动时间。
    • FinishedAt: 容器的结束时间。
  • Image: 容器镜像的SHA256标识符。
  • ResolvConfPath: 容器内的 resolv.conf 文件路径。
  • HostnamePath: 容器内的 hostname 文件路径。
  • HostsPath: 容器内的 hosts 文件路径。
  • LogPath: 容器日志文件的路径。
  • Name: 容器的名称，这里是 /lmdeploy。
  • RestartCount: 容器的重启次数。
  • Driver: 使用的存储驱动，这里是 overlay2。
  • Platform: 容器运行的平台，这里是 linux。
  • MountLabel: 安全标签。
  • ProcessLabel: 安全进程标签。
  • AppArmorProfile: 使用的AppArmor配置，这里是 docker-default。
  • ExecIDs: 当前在容器中执行的命令ID，这里是 null。

主机配置

{"HostConfig": {"Binds": ["/home/hlj/.cache/huggingface:/root/.cache/huggingface"],"ContainerIDFile": "","LogConfig": {"Type": "json-file","Config": {"max-file": "3","max-size": "10m"}},"NetworkMode": "bridge","PortBindings": {"23333/tcp": [{"HostIp": "","HostPort": "23333"}]},"RestartPolicy": {"Name": "no","MaximumRetryCount": 0},"AutoRemove": false,"VolumeDriver": "","VolumesFrom": null,"ConsoleSize": [60,121],"CapAdd": null,"CapDrop": null,"CgroupnsMode": "private","Dns": [],"DnsOptions": [],"DnsSearch": [],"ExtraHosts": null,"GroupAdd": null,"IpcMode": "host","Cgroup": "","Links": null,"OomScoreAdj": 0,"PidMode": "","Privileged": false,"PublishAllPorts": false,"ReadonlyRootfs": false,"SecurityOpt": ["label=disable"],"UTSMode": "","UsernsMode": "","ShmSize": 67108864,"Runtime": "nvidia","Isolation": "","CpuShares": 0,"Memory": 0,"NanoCpus": 0,"CgroupParent": "","BlkioWeight": 0,"BlkioWeightDevice": [],"BlkioDeviceReadBps": [],"BlkioDeviceWriteBps": [],"BlkioDeviceReadIOps": [],"BlkioDeviceWriteIOps": [],"CpuPeriod": 0,"CpuQuota": 0,"CpuRealtimePeriod": 0,"CpuRealtimeRuntime": 0,"CpusetCpus": "","CpusetMems": "","Devices": [],"DeviceCgroupRules": null,"DeviceRequests": [{"Driver": "","Count": -1,"DeviceIDs": null,"Capabilities": [["gpu"]],"Options": {}}],"MemoryReservation": 0,"MemorySwap": 0,"MemorySwappiness": null,"OomKillDisable": null,"PidsLimit": null,"Ulimits": [],"CpuCount": 0,"CpuPercent": 0,"IOMaximumIOps": 0,"IOMaximumBandwidth": 0,"MaskedPaths": ["/proc/asound","/proc/acpi","/proc/kcore","/proc/keys","/proc/latency_stats","/proc/timer_list","/proc/timer_stats","/proc/sched_debug","/proc/scsi","/sys/firmware","/sys/devices/virtual/powercap"],"ReadonlyPaths": ["/proc/bus","/proc/fs","/proc/irq","/proc/sys","/proc/sysrq-trigger"]}
}
  • Binds: 挂载的卷，这里将主机的 /home/hlj/.cache/huggingface 挂载到容器的 /root/.cache/huggingface。
  • LogConfig: 日志配置，使用 json-file 日志驱动，并设置最大文件数和大小。
  • NetworkMode: 网络模式，这里是 bridge。
  • PortBindings: 端口绑定，将主机的 23333 端口映射到容器的 23333 端口。
  • RestartPolicy: 重启策略，这里是 no，表示不自动重启。
  • IpcMode: IPC 模式，这里是 host，表示共享主机的 IPC 命名空间。
  • Runtime: 容器运行时，这里是 nvidia，表示使用 NVIDIA 运行时。
  • DeviceRequests: 设备请求，这里请求 GPU 设备。

存储驱动信息

{"GraphDriver": {"Data": {"LowerDir": "/var/lib/docker/overlay2/ddf24cd9ee208b8219020834967b5e5cf84f570879a9ddd7cefaa5968db598b2-init/diff:/var/lib/docker/overlay2/kd1a3js9cf8p1x3dih4oqksay/diff:/var/lib/docker/overlay2/e83a275c474b137f32aeec2d8b0835ddf5ed8ecd3671d7d83034b9ce62bd42e1/diff:/var/lib/docker/overlay2/1e389356a5fc4a57d53fbb5f806fb7ae294d0fb9cf32797b541b4756b309d70e/diff:/var/lib/docker/overlay2/da8f6d57b66412e24313af526b34b030be59fbe69d58d978bfc8910fb0a02768/diff:/var/lib/docker/overlay2/22a01998c4071857ea37a948e3aea91ffd7839f0cee89f78167b2d16bf862dbe/diff:/var/lib/docker/overlay2/0edeaba0eacef4e0ddc86dda01e4675257701cb12546b4d0ece8509313193420/diff:/var/lib/docker/overlay2/1bdd123142274c8dd7439373506bc3a13b997803ae1f91e4ca1a4f7b42f747e6/diff:/var/lib/docker/overlay2/b8add21d81efb9c7ac49e96cea14143b5c1e44fcafbc4ade08040039a54cf794/diff:/var/lib/docker/overlay2/c550471aadac0dd9cf61d894cf0c7ba4f194e44b103617610048dd57243778af/diff:/var/lib/docker/overlay2/d1353c33022ef4af2f85de516e6656201cdf13e66b32630a9e9450883181bd41/diff:/var/lib/docker/overlay2/e41238625eb790dde7e2e83c4679f7b0e473eaf8e5d3adbfd098b273bdb210b9/diff:/var/lib/docker/overlay2/2144bef9db48678df41a01bd901a7b7ddd282ace8ceb902910d3d5152a00645f/diff:/var/lib/docker/overlay2/4c2f9582f9a15ca6626af7337211304f11f3e6d23e7f0758530245a50d220078/diff:/var/lib/docker/overlay2/abc505d36333267f450108bd96c00ac5ff1e128c260e61e7997da2be301a0d51/diff:/var/lib/docker/overlay2/62681593678b2b8fda5b477cccb9c7c65ab998c72b0206af834f36fb8cc756ef/diff:/var/lib/docker/overlay2/010d7cb67f6ca5714671338cca64799957ef1e4e955a0ec2062d623a0f21ac45/diff:/var/lib/docker/overlay2/a4c223bc609c7c2b6f480c31ade268fba8a1c186ab9401e3132a38ebf4bc271c/diff:/var/lib/docker/overlay2/b563a8c0ff49d555df389db0bb2495ba402d6847491f756c7fa03589bf4569fe/diff:/var/lib/docker/overlay2/a88230089aad72f7fb7afeb1a2ab108aa78e5a88cc1207e7c3f5fe377fa62c71/diff:/var/lib/docker/overlay2/809390ce9fd3b8ea936f6262068ceeee0b337a9e46c297b22850f516c4f6fc36/diff:/var/lib/docker/overlay2/49b671d21d41637d8c448b6d30ab8a26e45fcd89ef427bb015053c2a99d872bb/diff:/var/lib/docker/overlay2/fcee4f32a58f9552a4fefc701ff02e3d0afe71b2acd013e3d8340b322bbbe778/diff:/var/lib/docker/overlay2/3f38c8a89ef97cba50090290c7805db38f1656b11665a8b60c99d020d3a5af7e/diff:/var/lib/docker/overlay2/808d9a4cb41fd6b70a13bec963e8082cb02a01657881baedff0d0ee69f44869f/diff:/var/lib/docker/overlay2/4cee062cf5bef4a43685e9e85531c20fb8b9cdfcbca79ecebf432ca442f3e499/diff:/var/lib/docker/overlay2/f8a3e6806a1464a4905f7a0aa7bcf25333e18f9ad2ddb56a9e0001bd8792f28e/diff:/var/lib/docker/overlay2/1cd44855a28cdd0506b113fd73c296a8761be0253d6fe6ca1d9db94b005b9f7b/diff:/var/lib/docker/overlay2/31d1b514bb95906f8d3106674f16472703e942bec3796162ace45fc36141304c/diff:/var/lib/docker/overlay2/9fdf9b9d162ffa05f103c66464ff92ed89e8d38ac51c7c759540747d09d79d37/diff:/var/lib/docker/overlay2/02579a706091284489d97589cca37a33e2d34839030940118eb4241921f144ce/diff","MergedDir": "/var/lib/docker/overlay2/ddf24cd9ee208b8219020834967b5e5cf84f570879a9ddd7cefaa5968db598b2/merged","UpperDir": "/var/lib/docker/overlay2/ddf24cd9ee208b8219020834967b5e5cf84f570879a9ddd7cefaa5968db598b2/diff","WorkDir": "/var/lib/docker/overlay2/ddf24cd9ee208b8219020834967b5e5cf84f570879a9ddd7cefaa5968db598b2/work"},"Name": "overlay2"}
}
  • LowerDir: 叠加文件系统的下层目录。
  • MergedDir: 叠加文件系统的合并目录。
  • UpperDir: 叠加文件系统的上层目录。
  • WorkDir: 叠加文件系统的工作目录。
  • Name: 使用的存储驱动名称，这里是 overlay2。

挂载点信息

{"Mounts": [{"Type": "bind","Source": "/home/hlj/.cache/huggingface","Destination": "/root/.cache/huggingface","Mode": "","RW": true,"Propagation": "rprivate"}]
}
  • Type: 挂载类型，这里是 bind。
  • Source: 主机上的源路径。
  • Destination: 容器内的目标路径。
  • Mode: 挂载模式。
  • RW: 是否可读写。
  • Propagation: 挂载传播设置，这里是 rprivate。

容器配置

{"Config": {"Hostname": "c2496d852ee3","Env": ["HUGGING_FACE_HUB_TOKEN=<secret>","PATH=/opt/py38/bin:/opt/tritonserver/bin:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin","CUDA_VERSION=11.8.0.065","CUDA_DRIVER_VERSION=520.61.05","CUDA_CACHE_DISABLE=1","NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=","_CUDA_COMPAT_PATH=/usr/local/cuda/compat","ENV=/etc/shinit_v2","BASH_ENV=/etc/bash.bashrc","SHELL=/bin/bash","NVIDIA_REQUIRE_CUDA=cuda>=9.0","NCCL_VERSION=2.15.5","CUBLAS_VERSION=11.11.3.6","CUFFT_VERSION=10.9.0.58","CURAND_VERSION=10.3.0.86","CUSPARSE_VERSION=11.7.5.86","CUSOLVER_VERSION=11.4.1.48","CUTENSOR_VERSION=1.6.1.5","NPP_VERSION=11.8.0.86","NVJPEG_VERSION=11.9.0.86","CUDNN_VERSION=8.7.0.84","TRT_VERSION=8.5.1.7","TRTOSS_VERSION=22.12","NSIGHT_SYSTEMS_VERSION=2022.4.2.1","NSIGHT_COMPUTE_VERSION=2022.3.0.22","DALI_VERSION=1.20.0","DALI_BUILD=6562491","POLYGRAPHY_VERSION=0.43.1","TRANSFORMER_ENGINE_VERSION=0.3","LD_LIBRARY_PATH=/opt/tritonserver/lib:/opt/tritonserver/backends/onnxruntime:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64","NVIDIA_VISIBLE_DEVICES=all","NVIDIA_DRIVER_CAPABILITIES=compute,utility,video","NVIDIA_PRODUCT_NAME=Triton Server","GDRCOPY_VERSION=2.3","HPCX_VERSION=2.13","MOFED_VERSION=5.4-rdmacore36.0","OPENUCX_VERSION=1.14.0","OPENMPI_VERSION=4.1.4","RDMACORE_VERSION=36.0","OPAL_PREFIX=/opt/hpcx/ompi","OMPI_MCA_coll_hcoll_enable=0","LIBRARY_PATH=/usr/local/cuda/lib64/stubs:","TRITON_SERVER_VERSION=2.29.0","NVIDIA_TRITON_SERVER_VERSION=22.12","TF_ADJUST_HUE_FUSED=1","TF_ADJUST_SATURATION_FUSED=1","TF_ENABLE_WINOGRAD_NONFUSED=1","TF_AUTOTUNE_THRESHOLD=2","TRITON_SERVER_GPU_ENABLED=1","TRITON_SERVER_USER=triton-server","DEBIAN_FRONTEND=noninteractive","TCMALLOC_RELEASE_RATE=200","DCGM_VERSION=2.2.9","NVIDIA_BUILD_ID=50109463","NCCL_LAUNCH_MODE=GROUP","TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas"],"Image": "lmdeploy:1.0","WorkingDir": "/opt/lmdeploy","Entrypoint": ["/startup.sh"],"OnBuild": null,"Labels": {"com.amazonaws.sagemaker.capabilities.accept-bind-to-port": "true","com.amazonaws.sagemaker.capabilities.multi-models": "true","com.nvidia.build.id": "50109463","com.nvidia.build.ref": "1a651ccb23c8f4416b5540653b207154a531194d","com.nvidia.cublas.version": "11.11.3.6","com.nvidia.cuda.version": "9.0","com.nvidia.cudnn.version": "8.7.0.84","com.nvidia.cufft.version": "10.9.0.58","com.nvidia.curand.version": "10.3.0.86","com.nvidia.cusolver.version": "11.4.1.48","com.nvidia.cusparse.version": "11.7.5.86","com.nvidia.cutensor.version": "1.6.1.5","com.nvidia.nccl.version": "2.15.5","com.nvidia.npp.version": "11.8.0.86","com.nvidia.nsightcompute.version": "2022.3.0.22","com.nvidia.nsightsystems.version": "2022.4.2.1","com.nvidia.nvjpeg.version": "11.9.0.86","com.nvidia.tensorrt.version": "8.5.1.7","com.nvidia.tensorrtoss.version": "22.12","com.nvidia.tritonserver.version": "2.29.0","com.nvidia.volumes.needed": "nvidia_driver"}}
}
  • Hostname: 容器的主机名。
  • Env: 容器的环境变量列表。
  • Image: 使用的镜像，这里是 lmdeploy:1.0。
  • WorkingDir: 容器的工作目录，这里是 /opt/lmdeploy。
  • Entrypoint: 容器启动时执行的命令，这里是 /startup.sh。
  • OnBuild: 构建时触发的指令，这里是 null。
  • Labels: 为容器设置的标签，用于元数据和管理。

网络设置

{"NetworkSettings": {"Bridge": "","SandboxID": "209975bf9949fed38210ded62b87b4487f78391ab994bb7e572ba9ed2dada380","SandboxKey": "/var/run/docker/netns/209975bf9949","Ports": {"23333/tcp": [{"HostIp": "0.0.0.0","HostPort": "23333"},{"HostIp": "::","HostPort": "23333"}]},"HairpinMode": false,"LinkLocalIPv6Address": "","LinkLocalIPv6PrefixLen": 0,"SecondaryIPAddresses": null,"SecondaryIPv6Addresses": null,"EndpointID": "8e1b1258d03f72237ebaecca9ff78d8951a4b281947bef16b8bc774feca42937","Gateway": "172.16.0.1","GlobalIPv6Address": "","GlobalIPv6PrefixLen": 0,"IPAddress": "172.16.0.5","IPPrefixLen": 16,"IPv6Gateway": "","MacAddress": "02:42:ac:10:00:05","Networks": {"bridge": {"IPAMConfig": null,"Links": null,"Aliases": null,"MacAddress": "02:42:ac:10:00:05","DriverOpts": null,"NetworkID": "b914ed43880d4258355ef85e24f301f098f06a0a4c1d17d826cbb7b4e3881fdb","EndpointID": "8e1b1258d03f72237ebaecca9ff78d8951a4b281947bef16b8bc774feca42937","Gateway": "172.16.0.1","IPAddress": "172.16.0.5","IPPrefixLen": 16,"IPv6Gateway": "","GlobalIPv6Address": "","GlobalIPv6PrefixLen": 0,"DNSNames": null}}}
}
  • Bridge: 桥接网络的名称，这里是空字符串。
  • SandboxID: 容器的网络沙箱ID。
  • SandboxKey: 网络沙箱的键。
  • Ports: 容器的端口映射，将主机的 23333 端口映射到容器的 23333 端口。
  • HairpinMode: 是否启用发夹模式，这里是 false。
  • LinkLocalIPv6Address: 链路本地IPv6地址，这里是空字符串。
  • LinkLocalIPv6PrefixLen: 链路本地IPv6前缀长度，这里是 0。
  • SecondaryIPAddresses: 第二个IP地址，这里是 null。
  • SecondaryIPv6Addresses: 第二个IPv6地址，这里是 null。
  • EndpointID: 网络端点ID。
  • Gateway: 网关地址，这里是 172.16.0.1。
  • GlobalIPv6Address: 全局IPv6地址，这里是空字符串。
  • GlobalIPv6PrefixLen: 全局IPv6前缀长度，这里是 0。
  • IPAddress: 容器的IP地址，这里是 172.16.0.5。
  • IPPrefixLen: 容器的IP前缀长度，这里是 16。
  • IPv6Gateway: IPv6网关地址，这里是空字符串。
  • MacAddress: 容器的MAC地址，这里是 02:42:ac:10:00:05。
  • Networks: 容器的网络配置，这里是 bridge 网络。
    • IPAMConfig: IP地址管理配置，这里是 null。
    • Links: 容器链接，这里是 null。
    • Aliases: 网络别名，这里是 null。
    • MacAddress: 网络的MAC地址，这里是 02:42:ac:10:00:05。
    • DriverOpts: 网络驱动选项，这里是 null。
    • NetworkID: 网络ID，这里是 b914ed43880d4258355ef85e24f301f098f06a0a4c1d17d826cbb7b4e3881fdb。
    • EndpointID: 网络端点ID，这里是 8e1b1258d03f72237ebaecca9ff78d8951a4b281947bef16b8bc774feca42937。
    • Gateway: 网关地址，这里是 172.16.0.1。
    • IPAddress: 容器的IP地址，这里是 172.16.0.5。
    • IPPrefixLen: 容器的IP前缀长度，这里是 16。
    • IPv6Gateway: IPv6网关地址，这里是空字符串。
    • GlobalIPv6Address: 全局IPv6地址，这里是空字符串。
    • GlobalIPv6PrefixLen: 全局IPv6前缀长度，这里是 0。
    • DNSNames: DNS名称，这里是 null。

总结

通过 docker inspect 命令的详细输出，我们可以看到容器的各种配置和状态信息，包括基本信息、主机配置、存储驱动信息、挂载点信息、容器配置和网络设置。这些信息可以帮助我们了解容器的运行环境、资源使用情况以及网络配置等。

推测的 Docker 运行命令

通过分析 docker inspect 的输出，我们可以推测出容器的运行命令：

docker run -d \--name lmdeploy \--runtime nvidia \--gpus all \-v /home/hlj/.cache/huggingface:/root/.cache/huggingface \-p 23333:23333 \--ipc=host \-w /opt/lmdeploy \--env "HUGGING_FACE_HUB_TOKEN=<secret>" \--env "PATH=/opt/py38/bin:/opt/tritonserver/bin:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin" \--env "CUDA_VERSION=11.8.0.065" \--env "CUDA_DRIVER_VERSION=520.61.05" \--env "CUDA_CACHE_DISABLE=1" \--env "NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=" \--env "_CUDA_COMPAT_PATH=/usr/local/cuda/compat" \--env "ENV=/etc/shinit_v2" \--env "BASH_ENV=/etc/bash.bashrc" \--env "SHELL=/bin/bash" \--env "NVIDIA_REQUIRE_CUDA=cuda>=9.0" \--env "NCCL_VERSION=2.15.5" \--env "CUBLAS_VERSION=11.11.3.6" \--env "CUFFT_VERSION=10.9.0.58" \--env "CURAND_VERSION=10.3.0.86" \--env "CUSPARSE_VERSION=11.7.5.86" \--env "CUSOLVER_VERSION=11.4.1.48" \--env "CUTENSOR_VERSION=1.6.1.5" \--env "NPP_VERSION=11.8.0.86" \--env "NVJPEG_VERSION=11.9.0.86" \--env "CUDNN_VERSION=8.7.0.84" \--env "TRT_VERSION=8.5.1.7" \--env "TRTOSS_VERSION=22.12" \--env "NSIGHT_SYSTEMS_VERSION=2022.4.2.1" \--env "NSIGHT_COMPUTE_VERSION=2022.3.0.22" \--env "DALI_VERSION=1.20.0" \--env "DALI_BUILD=6562491" \--env "POLYGRAPHY_VERSION=0.43.1" \--env "TRANSFORMER_ENGINE_VERSION=0.3" \--env "LD_LIBRARY_PATH=/opt/tritonserver/lib:/opt/tritonserver/backends/onnxruntime:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64" \--env "NVIDIA_VISIBLE_DEVICES=all" \--env "NVIDIA_DRIVER_CAPABILITIES=compute,utility,video" \--env "NVIDIA_PRODUCT_NAME=Triton Server" \--env "GDRCOPY_VERSION=2.3" \--env "HPCX_VERSION=2.13" \--env "MOFED_VERSION=5.4-rdmacore36.0" \--env "OPENUCX_VERSION=1.14.0" \--env "OPENMPI_VERSION=4.1.4" \--env "RDMACORE_VERSION=36.0" \--env "OPAL_PREFIX=/opt/hpcx/ompi" \--env "OMPI_MCA_coll_hcoll_enable=0" \--env "LIBRARY_PATH=/usr/local/cuda/lib64/stubs:" \--env "TRITON_SERVER_VERSION=2.29.0" \--env "NVIDIA_TRITON_SERVER_VERSION=22.12" \--env "TF_ADJUST_HUE_FUSED=1" \--env "TF_ADJUST_SATURATION_FUSED=1" \--env "TF_ENABLE_WINOGRAD_NONFUSED=1" \--env "TF_AUTOTUNE_THRESHOLD=2" \--env "TRITON_SERVER_GPU_ENABLED=1" \--env "TRITON_SERVER_USER=triton-server" \--env "DEBIAN_FRONTEND=noninteractive" \--env "TCMALLOC_RELEASE_RATE=200" \--env "DCGM_VERSION=2.2.9" \--env "NVIDIA_BUILD_ID=50109463" \--env "NCCL_LAUNCH_MODE=GROUP" \--env "TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas" \lmdeploy:1.0 /startup.sh

解释

  • -d：以后台模式运行容器。
  • --name lmdeploy：容器名称为 lmdeploy。
  • --runtime nvidia：指定使用 NVIDIA 运行时。
  • --gpus all：允许容器访问所有 GPU。
  • -v /home/hlj/.cache/huggingface:/root/.cache/huggingface：挂载主机目录到容器内。
  • -p 23333:23333：将宿主机的 23333 端口映射到容器的 23333 端口。
  • --ipc=host：使容器使用主机的 IPC 命名空间。
  • -w /opt/lmdeploy：设置工作目录。
  • --env：设置多个环境变量。
  • lmdeploy:1.0：使用 lmdeploy:1.0 镜像。
  • /startup.sh：执行 /startup.sh 脚本。

通过这些信息，我们可以准确地重现容器的启动命令。

你可以将通过 -e 参数设置的环境变量放到 startup.sh 脚本中。这样可以使你的 Docker 启动命令更加简洁，并且将环境变量的配置集中到一个地方，便于管理。

将环境变量移到 startup.sh

你可以在 startup.sh 脚本的顶部添加所有需要的环境变量。例如：

#!/bin/bash# 设置环境变量
export HUGGING_FACE_HUB_TOKEN=<secret>
export PATH=/opt/py38/bin:/opt/tritonserver/bin:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin
export CUDA_VERSION=11.8.0.065
export CUDA_DRIVER_VERSION=520.61.05
export CUDA_CACHE_DISABLE=1
export NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
export _CUDA_COMPAT_PATH=/usr/local/cuda/compat
export ENV=/etc/shinit_v2
export BASH_ENV=/etc/bash.bashrc
export SHELL=/bin/bash
export NVIDIA_REQUIRE_CUDA=cuda>=9.0
export NCCL_VERSION=2.15.5
export CUBLAS_VERSION=11.11.3.6
export CUFFT_VERSION=10.9.0.58
export CURAND_VERSION=10.3.0.86
export CUSPARSE_VERSION=11.7.5.86
export CUSOLVER_VERSION=11.4.1.48
export CUTENSOR_VERSION=1.6.1.5
export NPP_VERSION=11.8.0.86
export NVJPEG_VERSION=11.9.0.86
export CUDNN_VERSION=8.7.0.84
export TRT_VERSION=8.5.1.7
export TRTOSS_VERSION=22.12
export NSIGHT_SYSTEMS_VERSION=2022.4.2.1
export NSIGHT_COMPUTE_VERSION=2022.3.0.22
export DALI_VERSION=1.20.0
export DALI_BUILD=6562491
export POLYGRAPHY_VERSION=0.43.1
export TRANSFORMER_ENGINE_VERSION=0.3
export LD_LIBRARY_PATH=/opt/tritonserver/lib:/opt/tritonserver/backends/onnxruntime:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
export NVIDIA_VISIBLE_DEVICES=all
export NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
export NVIDIA_PRODUCT_NAME=Triton Server
export GDRCOPY_VERSION=2.3
export HPCX_VERSION=2.13
export MOFED_VERSION=5.4-rdmacore36.0
export OPENUCX_VERSION=1.14.0
export OPENMPI_VERSION=4.1.4
export RDMACORE_VERSION=36.0
export OPAL_PREFIX=/opt/hpcx/ompi
export OMPI_MCA_coll_hcoll_enable=0
export LIBRARY_PATH=/usr/local/cuda/lib64/stubs:
export TRITON_SERVER_VERSION=2.29.0
export NVIDIA_TRITON_SERVER_VERSION=22.12
export TF_ADJUST_HUE_FUSED=1
export TF_ADJUST_SATURATION_FUSED=1
export TF_ENABLE_WINOGRAD_NONFUSED=1
export TF_AUTOTUNE_THRESHOLD=2
export TRITON_SERVER_GPU_ENABLED=1
export TRITON_SERVER_USER=triton-server
export DEBIAN_FRONTEND=noninteractive
export TCMALLOC_RELEASE_RATE=200
export DCGM_VERSION=2.2.9
export NVIDIA_BUILD_ID=50109463
export NCCL_LAUNCH_MODE=GROUP
export TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas# 运行主程序或其他命令
# 在这里添加你的主程序启动命令，例如：
# ./my_main_program

更新 Docker 启动命令

在将环境变量移到 startup.sh 后，你可以简化 Docker 启动命令：

docker run -d \--gpus all \--name lmdeploy \-p 23333:23333 \--runtime=nvidia \-v /home/hlj/.cache/huggingface:/root/.cache/huggingface \-w /opt/lmdeploy \lmdeploy:1.0 /startup.sh

注意事项

  1. 权限：确保 startup.sh 脚本具有可执行权限。如果没有，可以使用以下命令添加可执行权限：

    chmod +x /path/to/startup.sh

  2. 保密信息：如果 HUGGING_FACE_HUB_TOKEN 或其他环境变量包含敏感信息，确保 startup.sh 的访问权限是安全的，以防止未经授权的访问。

  3. 调试和验证：在修改 startup.sh 脚本后，建议先在本地运行并验证其功能，然后再将其用于 Docker 容器中，以确保一切按预期工作。

通过将环境变量移到 startup.sh，你可以使 Docker 启动命令更加简洁，同时也方便环境变量的管理。如果你在任何步骤遇到问题，请随时提问！

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