记NVIDIA显卡A100在K8S POD中“Failed to initialize NVML: Unknown Error“问题解决-Toy模板网

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记NVIDIA显卡A100在K8S POD中"Failed to initialize NVML: Unknown Error"问题解决

问题描述

因项目原因需要在k8s上跑GPU相关的代码，优选使用NVIDIA A100显卡，但在根据官方文档简单并部署GitHub - NVIDIA/k8s-device-plugin：适用于 Kubernetes 的 NVIDIA 设备插件后，出现了pod中GPU运行一段时间后丢失的问题，进入容器后发现nvidia-smi命令报错"Failed to initialize NVML: Unknown Error"。尝试删除并且重建容器后，刚开始nvidia-smi命令正常，但是在大约10秒过后，重复出现以上异常。

问题分析

对于出现的问题，github中有多人提到，如：

nvidia-smi command in container returns “Failed to initialize NVML: Unknown Error” after couple of times · Issue #1678 · NVIDIA/nvidia-docker · GitHub

“Failed to initialize NVML: Unknown Error” after random amount of time · Issue #1671 · NVIDIA/nvidia-docker · GitHub

通过讨论可以发现，我们的现象与其他人是相同的，该命令失效的原因为一段时间后，devices.list中丢失了GPU的设备（路径：/sys/fs/cgroup/devices/devices.list）

导致问题的原因为k8s的cpu管理策略为static，并且修改cpu的管理策略为none，该问题确实可以解决，建议对CPU管理策略研究没有那么严格时，操作到此即可。但是我们对于CPU的管理策略要求为static，所以我们继续追溯到github上以下issue。

Updating cpu-manager-policy=static causes NVML unknown error · Issue #966 · NVIDIA/nvidia-docker · GitHub

问题原因可以参考https://zhuanlan.zhihu.com/p/344561710

在https://github.com/NVIDIA/nvidia-docker/issues/966#issuecomment-610928514作者提到了解决方式，并且官方在几个版本之前提供了相关的解决方案，在部署官方插件的时候添加参数**–pass-device-specs=ture**，至此重新阅读官方部署文档，确实发现了相关参数的说明。但是在部署之后发现问题还是没有解决，再次阅读相关讨论，发现runc版本有限制（https://github.com/NVIDIA/nvidia-docker/issues/1671#issuecomment-1330466432），我们的版本为1.14，再次对runc降级后，该问题解决。

解决步骤

检查runc版本，如果版本小于1.1.3可以直接跳到第3步操作：

# runc -v
runc version 1.1.4
commit: v1.1.4-0-xxxxx
spec: 1.0.2-dev
go: go1.17.10
libseccomp: 2.5.3

更新runc版本：
- 下载指定版本的runc版本，本文下载的为1.1.2版本（https://github.com/opencontainers/runc/releases/tag/v1.1.2）
  
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- 将下载好的runc.amd64文件上传到服务器、修改文件名并赋权
```
mv runc.amd64 runc && chmod +x runc
```
- 备份原有的runc
```
mv /usr/bin/runc /home/runcbak
```
- 停止docker
```
systemctl stop docker
```
- 替换新版本runc
```
cp runc /usr/bin/runc
```
- 启动docker
```
systemctl start docker
```
- 检查runc是否升级成功
```
# runc -v
runc version 1.1.2
commit: v1.1.2-0-ga916309f
spec: 1.0.2-dev
go: go1.17.10
libseccomp: 2.5.3
```

安装NVIDIA GPU插件

创建plugin.yml，该yaml文件中跟普通部署的区别主要为PASS_DEVICE_SPECS

# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: nvidia-device-plugin-daemonset
  namespace: kube-system
spec:
  selector:
    matchLabels:
      name: nvidia-device-plugin-ds
  updateStrategy:
    type: RollingUpdate
  template:
    metadata:
      labels:
        name: nvidia-device-plugin-ds
    spec:
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
      # Mark this pod as a critical add-on; when enabled, the critical add-on
      # scheduler reserves resources for critical add-on pods so that they can
      # be rescheduled after a failure.
      # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/
      priorityClassName: "system-node-critical"
      containers:
      - image: nvcr.io/nvidia/k8s-device-plugin:v0.13.0
        name: nvidia-device-plugin-ctr
        env:
          - name: FAIL_ON_INIT_ERROR
            value: "false"
          - name: PASS_DEVICE_SPECS
            value: "true"
        securityContext:
          privileged: true
        volumeMounts:
        - name: device-plugin
          mountPath: /var/lib/kubelet/device-plugins
      volumes:
      - name: device-plugin
        hostPath:
          path: /var/lib/kubelet/device-plugins

创建插件
```
$ kubectl create -f plugin.yml
```

创建GPU POD并且验证

附

SEO切换cpu管理策略

关闭kubelet
```
systemctl stop kubelet
```
删除cpu_manager_state
```
rm /var/lib/kubelet/cpu_manager_state
```

修改config.yaml

vi /var/lib/kubelet/config.yaml

apiVersion: kubelet.config.k8s.io/v1beta1
authentication:
  anonymous:
    enabled: false
  webhook:
    cacheTTL: 0s
    enabled: true
  x509:
    clientCAFile: /etc/kubernetes/pki/ca.crt
authorization:
  mode: Webhook
  webhook:
    cacheAuthorizedTTL: 0s
    cacheUnauthorizedTTL: 0s
cgroupDriver: systemd
clusterDNS:
- 10.96.0.10
clusterDomain: cluster.local

# 修改cpu管理策略，none或者static
cpuManagerPolicy: static

cpuManagerReconcilePeriod: 0s
evictionPressureTransitionPeriod: 0s
featureGates:
  TopologyManager: true
fileCheckFrequency: 0s
healthzBindAddress: 127.0.0.1
healthzPort: 10248
httpCheckFrequency: 0s
imageMinimumGCAge: 0s
kind: KubeletConfiguration
logging: {}
memorySwap: {}
nodeStatusReportFrequency: 0s
nodeStatusUpdateFrequency: 0s
podPidsLimit: 4096
reservedSystemCPUs: 0,1
resolvConf: /run/systemd/resolve/resolv.conf
rotateCertificates: true
runtimeRequestTimeout: 0s
shutdownGracePeriod: 0s
shutdownGracePeriodCriticalPods: 0s
staticPodPath: /etc/kubernetes/manifests
streamingConnectionIdleTimeout: 0s
syncFrequency: 0s
tlsCipherSuites:
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384
tlsMinVersion: VersionTLS12
topologyManagerPolicy: best-effort
volumeStatsAggPeriod: 0s

启动kubelet
```
systemctl start kubelet
```

变更containerd版本

https://github.com/NVIDIA/nvidia-docker/issues/1671#issuecomment-1238644201

参考https://blog.csdn.net/Ivan_Wz/article/details/111932120

github下载二进制containerd（https://github.com/containerd/containerd/releases/tag/v1.6.16）
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解压containerd

tar -zxvf containerd-1.6.16-linux-amd64.tar.gz

检查当前containerd版本
```
docker info 
containerd -v
```
暂停docker
```
systemctl stop docker
```

替换containerd二进制文件

cp containerd /usr/bin/containerd
cp containerd-shim /usr/bin/containerd-shim
cp containerd-shim-runc-v1 /usr/bin/containerd-shim-runc-v1
cp containerd-shim-runc-v2 /usr/bin/containerd-shim-runc-v2
cp ctr /usr/bin/ctr