├── .gitignore ├── main.go ├── Dockerfile ├── go.mod ├── service ├── dockercli.go └── cmd.go ├── .gitlab-ci.yml ├── k8s-deploy.yml ├── handlers └── main.go ├── readme.md ├── LICENSE └── go.sum /.gitignore: -------------------------------------------------------------------------------- 1 | # Binaries for programs and plugins 2 | *.exe 3 | *.exe~ 4 | *.dll 5 | *.so 6 | *.dylib 7 | 8 | # Test binary, built with `go test -c` 9 | *.test 10 | 11 | # Output of the go coverage tool, specifically when used with LiteIDE 12 | *.out 13 | 14 | # Dependency directories (remove the comment below to include it) 15 | # vendor/ 16 | -------------------------------------------------------------------------------- /main.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | import ( 4 | "net/http" 5 | 6 | "github.com/gin-gonic/gin" 7 | "github.com/zlingqu/nvidia-gpu-mem-monitor/handlers" 8 | ) 9 | 10 | func main() { 11 | 12 | r := gin.Default() 13 | 14 | r.GET("/metrics", func(c *gin.Context) { 15 | r := handlers.Metrics() 16 | c.String(http.StatusOK, r) 17 | }) 18 | r.Run(":59500") 19 | 20 | } 21 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # FROM golang:1.15 as builder 2 | FROM docker.dm-ai.cn/public/golang:1.15-nvidia-gpu-mem-monitor as builder 3 | WORKDIR /data 4 | ADD . . 5 | RUN go env -w GO111MODULE=on && go env -w GOPROXY=https://goproxy.cn,direct && go build -o targets/nvidia-gpu-mem-monitor 6 | 7 | # FROM alpine:3.12.0 8 | FROM nvidia/cuda:11.0-base 9 | WORKDIR /data 10 | COPY --from=builder /data/targets/nvidia-gpu-mem-monitor . 11 | CMD /data/nvidia-gpu-mem-monitor 12 | EXPOSE 59500 -------------------------------------------------------------------------------- /go.mod: -------------------------------------------------------------------------------- 1 | module github.com/zlingqu/nvidia-gpu-mem-monitor 2 | 3 | go 1.15 4 | 5 | require ( 6 | github.com/Microsoft/go-winio v0.4.16 // indirect 7 | github.com/containerd/containerd v1.4.3 // indirect 8 | github.com/docker/distribution v2.7.1+incompatible // indirect 9 | github.com/docker/docker v20.10.0+incompatible 10 | github.com/docker/go-connections v0.4.0 // indirect 11 | github.com/docker/go-units v0.4.0 // indirect 12 | github.com/gin-gonic/gin v1.6.3 13 | github.com/gogo/protobuf v1.3.1 // indirect 14 | github.com/opencontainers/go-digest v1.0.0 // indirect 15 | github.com/opencontainers/image-spec v1.0.1 // indirect 16 | github.com/sirupsen/logrus v1.7.0 // indirect 17 | google.golang.org/grpc v1.34.0 // indirect 18 | ) 19 | -------------------------------------------------------------------------------- /service/dockercli.go: -------------------------------------------------------------------------------- 1 | package service 2 | 3 | import ( 4 | "context" 5 | 6 | "github.com/docker/docker/api/types" 7 | "github.com/docker/docker/client" 8 | ) 9 | 10 | type ContainersStruct struct { 11 | ContainerID string 12 | Image string 13 | ConName string 14 | } 15 | 16 | // ListContains 这个方法没有用到 17 | func ListContains(cli *client.Client) []ContainersStruct { 18 | containers, err := cli.ContainerList(context.Background(), types.ContainerListOptions{}) 19 | if err != nil { 20 | panic(err) 21 | } 22 | containerList := []ContainersStruct{} 23 | for _, container := range containers { 24 | containerList = append(containerList, ContainersStruct{container.ID[:10], container.Image, container.Names[0]}) 25 | } 26 | return containerList 27 | } 28 | 29 | // 根据containId获取podName和podNamespace。这些信息通过docker inspect *也可以看到 30 | func GetContainsPodInfo(cli *client.Client, containID string) (string, string) { 31 | containers, _ := cli.ContainerInspect(context.Background(), containID) 32 | podName := containers.Config.Labels["io.kubernetes.pod.name"] 33 | podNamespace := containers.Config.Labels["io.kubernetes.pod.namespace"] 34 | // return containers.State.Pid 35 | return podName, podNamespace 36 | } 37 | -------------------------------------------------------------------------------- /service/cmd.go: -------------------------------------------------------------------------------- 1 | package service 2 | 3 | import ( 4 | "bytes" 5 | "encoding/csv" 6 | "fmt" 7 | "io/ioutil" 8 | "os/exec" 9 | ) 10 | 11 | // csv方式输出exec的执行结果 12 | func GetExecOutByCSV(args string) [][]string { 13 | 14 | out, err := exec.Command("/bin/bash", "-c", args).Output() 15 | 16 | if err != nil { 17 | fmt.Printf("%s\n", err) 18 | return nil 19 | } 20 | 21 | csvReader := csv.NewReader(bytes.NewReader(out)) 22 | csvReader.TrimLeadingSpace = true 23 | records, err := csvReader.ReadAll() 24 | if err != nil { 25 | fmt.Printf("%s\n", err) 26 | return nil 27 | } 28 | return records 29 | } 30 | 31 | // string方式输出exec的执行结果 32 | func GetExecOutByString(args string) string { 33 | 34 | cmd := exec.Command("/bin/bash", "-c", args) 35 | 36 | //创建获取命令输出管道 37 | stdout, err := cmd.StdoutPipe() 38 | if err != nil { 39 | fmt.Printf("Error:can not obtain stdout pipe for command:%s\n", err) 40 | return "error" 41 | } 42 | 43 | //执行命令 44 | if err := cmd.Start(); err != nil { 45 | fmt.Println("Error:The command is err,", err) 46 | return "error" 47 | } 48 | 49 | //读取所有输出 50 | myByte, err := ioutil.ReadAll(stdout) 51 | if err != nil { 52 | fmt.Println("ReadAll Stdout:", err.Error()) 53 | return "error" 54 | } 55 | 56 | if err := cmd.Wait(); err != nil { 57 | fmt.Println("wait:", err.Error()) 58 | return "error" 59 | } 60 | // fmt.Printf("stdout:\n\n %s", bytes) 61 | return string(myByte[0 : len(myByte)-1]) 62 | } 63 | -------------------------------------------------------------------------------- /.gitlab-ci.yml: -------------------------------------------------------------------------------- 1 | variables: 2 | DOCKER_REGIS_URL: "docker.dm-ai.cn" #docker仓库地址 3 | PROJECT_NAME: devops #项目代号，也是k8s的namespace的名字 4 | SERVER_NAME: nvidia-gpu-mem-monitor #服务名 5 | IMAGE_TAG_NAME: "${DOCKER_REGISTRY_URL}/${PROJECT_NAME}/${SERVER_NAME}:${CI_COMMIT_SHA}" #使用代码的commitid作为image的tag 6 | # IMAGE_TAG_NAME: "${DOCKER_REGIS_URL}/${PROJECT_NAME}/${SERVER_NAME}:${CI_RUNNER_TAGS}" #使用代码的tag名字作为image的tag 7 | 8 | stages: 9 | - build 10 | - make_and_push_image 11 | - k8s_dev_deploy 12 | - k8s_prd_deploy 13 | 14 | job_make_and_push_image: 15 | image: 16 | name: docker.dm-ai.cn/public/kaniko-executor:debug-v1.3.0 17 | entrypoint: [""] 18 | stage: make_and_push_image 19 | script: 20 | - echo "{\"auths\":{\"$DOCKER_REGISTRY_URL\":{\"username\":\"$DOCKER_REGISTRY_USERNAME\",\"password\":\"$DOCKER_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json 21 | - cat $CI_PROJECT_DIR/Dockerfile && echo "" 22 | - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $IMAGE_TAG_NAME #使用kaniko代替dind/sokcet模式，打镜像和上传 23 | - echo "docker image is:" && echo $IMAGE_TAG_NAME 24 | only: 25 | - dev 26 | tags: 27 | - k8s-runner 28 | 29 | job_k8s_dev_deploy: 30 | image: 31 | name: docker.dm-ai.cn/public/alpine:kubectl-1.18.10 32 | stage: k8s_dev_deploy 33 | script: 34 | - echo $K8S_DEV_TOKEN|base64 -d > ~/.kube/config 35 | - cd $CI_PROJECT_DIR 36 | - ls -l && pwd 37 | - sed -i "s#IMAGE_TAG_NAME#${IMAGE_TAG_NAME}#g" k8s-deploy.yml 38 | - cat k8s-deploy.yml 39 | - kubectl apply -f k8s-deploy.yml 40 | only: 41 | - dev 42 | tags: 43 | - k8s-runner -------------------------------------------------------------------------------- /k8s-deploy.yml: -------------------------------------------------------------------------------- 1 | --- 2 | apiVersion: v1 3 | kind: Service 4 | metadata: 5 | name: nvidia-gpu-mem-monitor 6 | namespace: devops 7 | labels: 8 | app: nvidia-gpu-mem-monitor 9 | annotations: 10 | prometheus.io/scrape: "true" 11 | prometheus.io/port: "59500" 12 | spec: 13 | type: ClusterIP 14 | ports: 15 | - port: 80 16 | name: nvidia-gpu-mem-monitor 17 | targetPort: 59500 18 | selector: 19 | app: nvidia-gpu-mem-monitor 20 | --- 21 | apiVersion: apps/v1 22 | kind: DaemonSet 23 | metadata: 24 | name: nvidia-gpu-mem-monitor 25 | namespace: devops 26 | spec: 27 | selector: 28 | matchLabels: 29 | app: nvidia-gpu-mem-monitor 30 | template: 31 | metadata: 32 | labels: 33 | app: nvidia-gpu-mem-monitor 34 | annotations: 35 | sidecar.istio.io/inject: 'false' 36 | spec: 37 | affinity: 38 | nodeAffinity: 39 | requiredDuringSchedulingIgnoredDuringExecution: 40 | nodeSelectorTerms: 41 | - matchExpressions: 42 | - key: gpu.card.type 43 | operator: Exists 44 | - matchExpressions: 45 | - key: gpu.mem.type 46 | operator: Exists 47 | tolerations: 48 | - operator: Exists 49 | effect: NoSchedule 50 | imagePullSecrets: 51 | - name: regsecret 52 | containers: 53 | - name: nvidia-gpu-mem-monitor 54 | image: IMAGE_TAG_NAME 55 | ports: 56 | - containerPort: 59500 57 | env: 58 | - name: TZ 59 | value: Asia/Shanghai 60 | - name: hostIP 61 | valueFrom: 62 | fieldRef: 63 | apiVersion: v1 64 | fieldPath: status.hostIP 65 | volumeMounts: 66 | - name: proc 67 | mountPath: /proc 68 | - name: socket 69 | mountPath: /var/run/docker.sock 70 | volumes: 71 | - name: proc 72 | hostPath: 73 | path: /proc 74 | - name: socket 75 | hostPath: 76 | path: /var/run/docker.sock -------------------------------------------------------------------------------- /handlers/main.go: -------------------------------------------------------------------------------- 1 | package handlers 2 | 3 | import ( 4 | "fmt" 5 | "log" 6 | "net" 7 | "os" 8 | 9 | "github.com/docker/docker/client" 10 | svc "github.com/zlingqu/nvidia-gpu-mem-monitor/service" 11 | ) 12 | 13 | // Metrics 提供metrics接口 14 | func Metrics() string { 15 | 16 | cli, err := client.NewClientWithOpts(client.WithHost("unix:///var/run/docker.sock"), client.WithVersion("v1.38")) //使用socket通信 17 | 18 | if err != nil { 19 | log.Print("docker client 初始化错误" + err.Error()) 20 | return "" 21 | } 22 | 23 | if cli == nil { 24 | log.Print("docker client 初始化错误") 25 | return "" 26 | } 27 | defer cli.Close() //记得释放 28 | 29 | records := svc.GetExecOutByCSV("nvidia-smi --query-compute-apps=pid,used_gpu_memory,gpu_name,gpu_uuid --format=csv,noheader,nounits") 30 | /* 31 | 31756, 1267, GeForce GTX 1080 Ti, GPU-78d64296-8254-ef39-35ec-cb35bd6e6192 32 | 25580, 753, GeForce GTX 1080 Ti, GPU-78d64296-8254-ef39-35ec-cb35bd6e6192 33 | */ 34 | gpuLists := svc.GetExecOutByCSV("nvidia-smi -L|awk '{print $NF,$2}'|sed 's/)/,/g'|sed 's/://g'") 35 | /* 36 | GPU-78d64296-8254-ef39-35ec-cb35bd6e6192, 0 37 | GPU-2b8215f8-eb7c-0ae4-328f-a678a84f8d08, 1 38 | GPU-9d5d5439-4397-7189-1a46-801b59248301, 2 39 | GPU-55da7249-18e7-c3e7-beb8-4e1f661f5461, 3 40 | */ 41 | response := `# HELP pod_used_gpu_mem_MB . Pod使用的GPU显存大小 42 | # TYPE pod_used_gpu_mem_MB gauge 43 | ` 44 | gpu := "" 45 | for _, row := range records { 46 | cmd := "cat /proc/" + row[0] + "/cgroup |head -1 | awk -F'/' '{print $NF}'" 47 | containID := svc.GetExecOutByString(cmd) 48 | podName, podNamespace := "服务器直接运行的程序", "null" //非pod使用gpu的进程 49 | if containID != "" { 50 | podName, podNamespace = svc.GetContainsPodInfo(cli, containID) //获取pod信息 51 | if podName == "" || podNamespace == "" { //排除docker run起来的进程 52 | podName = "docker run运行的程序" 53 | podNamespace = "null" 54 | } 55 | } 56 | 57 | for _, gpuOne := range gpuLists { 58 | if gpuOne[0] == row[3] { 59 | gpu = gpuOne[1] 60 | break 61 | } 62 | } 63 | response = fmt.Sprintf("%spod_used_gpu_mem_MB{hostIP=\"%s\",app_pid=\"%s\",gpu_name=\"%s\",UUID=\"%s\",gpu=\"%s\",pod=\"%s\",namespace=\"%s\"} %s\n", 64 | response, getIP(), row[0], row[2], row[3], gpu, podName, podNamespace, row[1]) 65 | } 66 | return response 67 | } 68 | 69 | func getIP() string { 70 | if hostIP := os.Getenv("hostIP"); hostIP != "" { //如果部署到k8s中会注入hostIP变量 71 | return hostIP 72 | } 73 | netInterfaces, err := net.Interfaces() 74 | if err != nil { 75 | fmt.Println("net.Interfaces failed, err:", err.Error()) 76 | return "" 77 | } 78 | for i := 0; i < len(netInterfaces); i++ { 79 | //fmt.Println(netInterfaces[i],net.FlagUp) 80 | if (netInterfaces[i].Flags&net.FlagUp) != 0 && interFaceFields(netInterfaces[i]) { 81 | adds, _ := netInterfaces[i].Addrs() 82 | 83 | for _, address := range adds { 84 | //fmt.Println(address) 85 | if inet, ok := address.(*net.IPNet); ok && !inet.IP.IsLoopback() { 86 | if inet.Contains(inet.IP) && inet.IP.To4() != nil { 87 | return inet.IP.String() 88 | } 89 | } 90 | } 91 | } 92 | } 93 | return "" 94 | } 95 | 96 | func interFaceFields(myInterFace net.Interface) bool { 97 | if myInterFace.MTU != 1500 { 98 | return false 99 | } 100 | if len(myInterFace.HardwareAddr) > 17 { //排除ib网络的网卡 101 | return false 102 | } 103 | for _, v := range []string{"cni0", "flannel.1", "docker0", "virbr0"} { //排除特殊的网卡设备 104 | if myInterFace.Name == v { 105 | return false 106 | } 107 | } 108 | return true 109 | } 110 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | ## gpu节点加入k8s集群，做AI分析是一种比较常见的情景，当pod使用GPU卡时，通常有以下两种方案 2 | ## 一、安装nvidia官方插件 3 | 此时资源分配时，是按照`卡的数量`进行资源分配，k8s的yml文件类似于如下 4 | ```yaml 5 | 6 | resources: 7 | limits: 8 | nvidia.com/gpu: '1' 9 | requests: 10 | nvidia.com/gpu: '1' 11 | 12 | ``` 13 | 其中1表示使用1张GPU卡。 14 | 15 | 16 | 使用prometheus从cAdvisor总获取的有效监控项有： 17 | 18 | ```bash 19 | container_accelerator_memory_total_bytes #pod所在卡的显存总大小 20 | container_accelerator_memory_used_bytes #pod所在卡的显存使用 21 | ``` 22 | 23 | 24 | 25 | ## 二、安装第三方插件 26 | 27 | 比如阿里云的插件，git地址：https://github.com/AliyunContainerService/gpushare-device-plugin/ 28 | 29 | 此时资源分配时，是可以按照`卡的显存大小`进行资源分配，k8s的yml文件类似于如下 30 | 31 | ```yaml 32 | resources: 33 | limits: 34 | aliyun.com/gpu-mem: '4' 35 | requests: 36 | aliyun.com/gpu-mem: '2' 37 | ``` 38 | 其中2、4表示使用的显存大小 39 | 查看现场资源分配 40 | ``` 41 | # kubectl-inspect-gpushare 42 | NAME IPADDRESS GPU0(Allocated/Total) GPU1(Allocated/Total) GPU2(Allocated/Total) GPU3(Allocated/Total) GPU4(Allocated/Total) GPU5(Allocated/Total) GPU6(Allocated/Total) GPU7(Allocated/Total) GPU Memory(GiB) 43 | 192.168.3.4 192.168.3.4 4/11 6/11 10/11 0/11 0/11 0/11 0/11 0/11 20/88 44 | 192.168.68.4 192.168.68.4 4/10 0/10 10/10 9/10 0/10 10/10 6/10 6/10 45/80 45 | ------------------------------------------------------------------------------------------- 46 | Allocated/Total GPU Memory In Cluster: 47 | 65/168 (38%) 48 | ------------------------------------------------------------------------------------------- 49 | Allocated/Total GPU Memory In Cluster: 50 | 65/168 (38%) 51 | ``` 52 | 53 | 54 | 55 | 使用prometheus从cAdvisor总获取的有效监控项有： 56 | 57 | ```bash 58 | container_accelerator_memory_total_bytes #pod所在卡的显存总大小 59 | container_accelerator_memory_used_bytes #pod所在卡的显存使用 60 | ``` 61 | 62 | ## 注意 63 | 使用第二种方式时container_accelerator_memory_used_bytes获取的仍然pod所在卡的显存使用情况，而不是这个pod使用的显存情况。 64 | 65 | 当一张卡上跑多个进程时，此时获取到的数据是失真的，比如下面，第2张第6张卡都跑了2个进程 66 | 67 | ``` 68 | # nvidia-smi 69 | Thu Dec 17 09:36:05 2020 70 | +-----------------------------------------------------------------------------+ 71 | | NVIDIA-SMI 450.57 Driver Version: 450.57 CUDA Version: 11.0 | 72 | |-------------------------------+----------------------+----------------------+ 73 | | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | 74 | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | 75 | | | | MIG M. | 76 | |===============================+======================+======================| 77 | | 0 TITAN V Off | 00000000:1A:00.0 Off | N/A | 78 | | 29% 42C P8 26W / 250W | 2639MiB / 12066MiB | 0% Default | 79 | | | | N/A | 80 | +-------------------------------+----------------------+----------------------+ 81 | | 1 TITAN V Off | 00000000:1B:00.0 Off | N/A | 82 | | 28% 42C P8 26W / 250W | 4MiB / 12066MiB | 0% Default | 83 | | | | N/A | 84 | +-------------------------------+----------------------+----------------------+ 85 | | 2 TITAN V Off | 00000000:3D:00.0 Off | N/A | 86 | | 28% 39C P2 36W / 250W | 8702MiB / 12066MiB | 1% Default | 87 | | | | N/A | 88 | +-------------------------------+----------------------+----------------------+ 89 | | 3 TITAN V Off | 00000000:3E:00.0 Off | N/A | 90 | | 28% 40C P8 27W / 250W | 4MiB / 12066MiB | 0% Default | 91 | | | | N/A | 92 | +-------------------------------+----------------------+----------------------+ 93 | | 4 TITAN V Off | 00000000:88:00.0 Off | N/A | 94 | | 28% 37C P8 27W / 250W | 4MiB / 12066MiB | 0% Default | 95 | | | | N/A | 96 | +-------------------------------+----------------------+----------------------+ 97 | | 5 TITAN V Off | 00000000:89:00.0 Off | N/A | 98 | | 28% 39C P8 26W / 250W | 4MiB / 12066MiB | 0% Default | 99 | | | | N/A | 100 | +-------------------------------+----------------------+----------------------+ 101 | | 6 TITAN V Off | 00000000:B1:00.0 Off | N/A | 102 | | 33% 48C P2 43W / 250W | 7778MiB / 12066MiB | 16% Default | 103 | | | | N/A | 104 | +-------------------------------+----------------------+----------------------+ 105 | | 7 TITAN V Off | 00000000:B2:00.0 Off | N/A | 106 | | 28% 38C P8 25W / 250W | 4MiB / 12066MiB | 0% Default | 107 | | | | N/A | 108 | +-------------------------------+----------------------+----------------------+ 109 | 110 | +-----------------------------------------------------------------------------+ 111 | | Processes: | 112 | | GPU GI CI PID Type Process name GPU Memory | 113 | | ID ID Usage | 114 | |=============================================================================| 115 | | 0 N/A N/A 159584 C ./bin/main 2635MiB | 116 | | 2 N/A N/A 41728 C python 5133MiB | 117 | | 2 N/A N/A 152095 C ./bin/main 3565MiB | 118 | | 6 N/A N/A 3148 C python3.6 3887MiB | 119 | | 6 N/A N/A 11020 C python3.6 3887MiB | 120 | +-----------------------------------------------------------------------------+ 121 | ``` 122 | 123 | 124 | 该项目就是为了解决这个文件，通过以下监控线即可获取到pod本身使用的显存大小。 125 | ``` 126 | pod_used_gpu_mem_MB 127 | ``` 128 | 129 | 获取到的监控项类似如下 130 | ``` 131 | pod_used_gpu_mem_MB{app="nvidia-gpu-mem-monitor",app_pid="31563",gpu_name="GeForce GTX 1080 Ti",gpu_uuid="GPU-78d64296-8254-ef39-35ec-cb35bd6e6192",instance="10.244.19.248:80",job="nvidia-gpu-mem-monitor",kubernetes_name="nvidia-gpu-mem-monitor",kubernetes_namespace="devops",pod_name="xmcvt-speech-speed-detect-77b7bbdb96-sjjdq",pod_namespace="xmcvt"} 132 | ``` 133 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= 2 | github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU= 3 | github.com/Microsoft/go-winio v0.4.16 h1:FtSW/jqD+l4ba5iPBj9CODVtgfYAD8w2wS923g/cFDk= 4 | github.com/Microsoft/go-winio v0.4.16/go.mod h1:XB6nPKklQyQ7GC9LdcBEcBl8PF76WugXOPRXwdLnMv0= 5 | github.com/census-instrumentation/opencensus-proto v0.2.1/go.mod h1:f6KPmirojxKA12rnyqOA5BBL4O983OfeGPqjHWSTneU= 6 | github.com/client9/misspell v0.3.4/go.mod h1:qj6jICC3Q7zFZvVWo7KLAzC3yx5G7kyvSDkc90ppPyw= 7 | github.com/cncf/udpa/go v0.0.0-20200629203442-efcf912fb354/go.mod h1:WmhPx2Nbnhtbo57+VJT5O0JRkEi1Wbu0z5j0R8u5Hbk= 8 | github.com/containerd/containerd v1.4.3 h1:ijQT13JedHSHrQGWFcGEwzcNKrAGIiZ+jSD5QQG07SY= 9 | github.com/containerd/containerd v1.4.3/go.mod h1:bC6axHOhabU15QhwfG7w5PipXdVtMXFTttgp+kVtyUA= 10 | github.com/davecgh/go-spew 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