diff --git a/docs/installation/openshift-installation.md b/docs/installation/openshift-installation.md new file mode 100644 index 000000000..61e1f7c6b --- /dev/null +++ b/docs/installation/openshift-installation.md @@ -0,0 +1,227 @@ +--- +title: Install HAMi on Red Hat OpenShift +sidebar_label: HAMi on OpenShift +--- + +This guide covers installing HAMi on OpenShift clusters that use NVIDIA GPU Operator to manage GPU drivers and NVIDIA Container Toolkit. + +## Prerequisites + +For general environment requirements, see [Prerequisites](./prerequisites.md). OpenShift also requires: + +- `oc` connected to the cluster, with permission to create projects, SecurityContextConstraints (SCCs), and cluster-scoped RBAC resources. +- GPU Operator and Node Feature Discovery installed according to NVIDIA's [OpenShift installation guide](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/install-gpu-ocp.html), with CDI enabled in CRI-O. + +Check the cluster status and set the variables to the actual ClusterPolicy name, GPU Operator namespace, and GPU node name: + +```bash +oc whoami +oc version +oc get nodes -o wide +oc get clusterpolicy +oc get nodes -L nvidia.com/gpu.present + +export GPU_CLUSTER_POLICY='gpu-cluster-policy' +export GPU_OPERATOR_NAMESPACE='nvidia-gpu-operator' +export GPU_NODE='REPLACE_WITH_GPU_NODE_NAME' + +oc describe node "$GPU_NODE" +oc get pods -n "$GPU_OPERATOR_NAMESPACE" -o wide +``` + +## Disable the NVIDIA device plugin + +HAMi must be the only device plugin registering `nvidia.com/gpu` on the nodes it manages. Before replacing the existing plugin, finish or migrate its GPU workloads during a maintenance window. + +If HAMi will manage all NVIDIA GPU nodes, disable GPU Operator's device plugin across the cluster through its `ClusterPolicy`. If some nodes still need the NVIDIA device plugin, first deploy the two plugins to separate sets of nodes. + +```bash +oc patch clusterpolicy "$GPU_CLUSTER_POLICY" --type=merge \ + -p '{"spec":{"devicePlugin":{"enabled":false}}}' + +oc get clusterpolicy "$GPU_CLUSTER_POLICY" \ + -o jsonpath='state={.status.state}{"\n"}devicePlugin.enabled={.spec.devicePlugin.enabled}{"\n"}cdi.enabled={.spec.cdi.enabled}{"\n"}' +oc get daemonsets,pods -n "$GPU_OPERATOR_NAMESPACE" +``` + +Wait for the ClusterPolicy to report `state=ready`. Confirm `devicePlugin.enabled=false` and that the old device-plugin Pods have stopped. Keep the driver and Toolkit components enabled. + +## Add the Helm repository + +```bash +helm repo add hami-charts https://project-hami.github.io/HAMi/ +helm repo update +``` + +## Configure HAMi + +:::warning Unreleased Chart values + +The published Helm repository currently provides HAMi Chart v2.10.0. It does not include the `platform.openshift` and `selinux.enabled` values used in this guide. HAMi Chart v2.11.0, which includes these values, has not been released yet. + +Until v2.11.0 is released, install HAMi from the source repository rather than `hami-charts/hami`: + +```bash +git clone https://github.com/Project-HAMi/HAMi.git +cd HAMi +helm dependency build charts/hami +``` + +Use the local `./charts/hami` path in the installation command below. Do not use the published v2.10.0 Chart for this OpenShift configuration. + +::: + +Create a dedicated project: + +```bash +oc new-project hami-system +``` + +Save the following as `values-openshift.yaml` and adjust the node labels, driver root, and Toolkit executable path for the cluster. + +Set `scheduler.kubeScheduler.image.tag` to match the Kubernetes server version from `oc version`. Use a published upstream Kubernetes image tag, without the OpenShift release number or an OpenShift-specific suffix. + +```yaml +platform: + openshift: true + +selinux: + enabled: true + +scheduler: + kubeScheduler: + image: + tag: "REPLACE_WITH_KUBERNETES_IMAGE_TAG" + +devicePlugin: + deviceListStrategy: cdi-annotations + nvidiaDriverRoot: /run/nvidia/driver + nvidiaHookPath: /usr/local/nvidia/toolkit/nvidia-ctk + nvidiaNodeSelector: + gpu: null + nvidia.com/gpu.present: "true" + service: + type: ClusterIP +``` + +- `gpu: null` removes the Chart's default `gpu=on` selector. Without it, Helm merges both selectors and the device plugin requires both labels. Add a dedicated node label to the selector if HAMi should manage only some GPU nodes. +- The Chart already tolerates `nvidia.com/gpu:NoSchedule`. Override `devicePlugin.tolerations` only if the GPU nodes have other taints. +- If the device plugin relies on the NVIDIA runtime to process `NVIDIA_VISIBLE_DEVICES`, check that the `nvidia` RuntimeClass exists, then set `devicePlugin.runtimeClassName: nvidia`. HAMi also adds this RuntimeClass to NVIDIA workloads. + + With CDI enabled in GPU Operator v25.10.0 or later, NVIDIA requires this RuntimeClass for these GPU management containers unless the NRI plugin handles device injection. See [CDI and GPU management containers](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/cdi.html#cdi-and-gpu-management-containers). + +- `nvidiaDriverRoot` must point to the host driver installation root, not the directory of an individual library. For host-installed drivers, use `/`. `nvidiaHookPath` must point to the host `nvidia-ctk` executable. See [CDI configuration](./configure-cdi.md). +- If the existing environment uses HAMi's `envvar` injection, set `deviceListStrategy: envvar` and omit `nvidiaHookPath`. Verify the NVIDIA runtime handles `NVIDIA_VISIBLE_DEVICES` for both the device plugin and workloads. Follow the [GPU Operator runtime troubleshooting guide](../troubleshooting/troubleshooting.md#nvidia-toolkit-gpu-operator-25-10) for that path. + + To disable CDI in an existing GPU Operator installation on CRI-O, follow NVIDIA's [Disabling CDI procedure](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/26.3/cdi.html#disabling-cdi): set the GPU nodes' `nvidia.com/gpu.deploy.operator-validator` label to `false`, set `cdi.enabled=false` in the ClusterPolicy, then restore the label to `true`. This sequence applies to an existing CDI-to-legacy transition; it is not needed when CDI remains enabled or is disabled from the initial GPU Operator installation. + +Enable `devicePlugin.gpuOperatorToolkitReady.enabled` only if GPU Operator creates the `toolkit-ready` file. Check that the file exists under `devicePlugin.gpuOperatorToolkitReady.hostPath`, normally `/run/nvidia/validations`. If the file is missing, the init container waits indefinitely. + +## Install HAMi + +OpenShift support requires HAMi Helm Chart v2.11.0 or later. Until that version is released, run this command from the source checkout prepared above. + +```bash +helm upgrade --install hami ./charts/hami \ + --namespace hami-system \ + -f values-openshift.yaml \ + --wait --timeout 10m +``` + +## Verify the deployment + +```bash +oc get pods -n hami-system +oc get node "$GPU_NODE" \ + -o 'custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\.com/gpu' +``` + +Check that the scheduler and device-plugin Pods are ready and the GPU node reports a non-zero `nvidia.com/gpu` allocatable value. + +### Run a GPU sharing workload + +Create a separate application project and ServiceAccount. Do not grant the ServiceAccount permission to use the device-plugin SCC. + +Run NVIDIA's [CUDA VectorAdd sample](https://docs.nvidia.com/datacenter/cloud-native/openshift/24.6.2/install-gpu-ocp.html#running-a-sample-gpu-application) as a Job. The Job requests one vGPU, 1024 MiB of device memory, and a 25% GPU core limit. The image requires a driver compatible with CUDA 12.5. If the installed driver is incompatible, use an equivalent sample image that supports it. + +```bash +oc new-project hami-test +oc create serviceaccount hami-smoke -n hami-test +``` + +Save as `hami-smoke.yaml`: + +```yaml +apiVersion: batch/v1 +kind: Job +metadata: + name: hami-smoke + namespace: hami-test +spec: + backoffLimit: 0 + template: + spec: + serviceAccountName: hami-smoke + restartPolicy: Never + tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + containers: + - name: vectoradd + image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0-ubi8 + securityContext: + runAsNonRoot: true + allowPrivilegeEscalation: false + capabilities: + drop: ["ALL"] + seccompProfile: + type: RuntimeDefault + resources: + limits: + nvidia.com/gpu: 1 + nvidia.com/gpumem: 1024 + nvidia.com/gpucores: 25 +``` + +If you changed the tolerations when installing HAMi, update the Job's `tolerations` to match the GPU nodes. Submit the Job and inspect its logs and Pod: + +```bash +oc apply -f hami-smoke.yaml +oc wait -n hami-test --for=condition=complete job/hami-smoke --timeout=300s +oc logs -n hami-test job/hami-smoke +oc get pods -n hami-test -l job-name=hami-smoke \ + -o 'custom-columns=NAME:.metadata.name,NODE:.spec.nodeName,SCHEDULER:.spec.schedulerName,RUNTIMECLASS:.spec.runtimeClassName,SCC:.metadata.annotations.openshift\.io/scc' +oc get pods -n hami-test -l job-name=hami-smoke -o yaml +``` + +Confirm the following: + +- The logs contain `Test PASSED`. +- The Pod uses scheduler `hami-scheduler`. If `devicePlugin.runtimeClassName` is set, the Pod uses that RuntimeClass. +- The Pod runs on a GPU node that HAMi manages. +- The Pod annotations contain HAMi allocation information. +- The application uses an appropriate restricted SCC. + +The Job controller creates the Pod with the application's ServiceAccount. Creating a Pod directly as an administrator could use the administrator's SCC permissions and hide application permission errors. + +This Job verifies Pod admission, scheduling, device allocation, and CUDA execution. To verify memory and core limits under load, also run the [memory allocation](../userguide/nvidia-device/examples/allocate-device-memory.md) and [core allocation](../userguide/nvidia-device/examples/allocate-device-core.md) examples. + +## Troubleshooting and cleanup + +| Symptom | Checks and action | +| --- | --- | +| SCC or SELinux init container missing after installation | Check that `platform.openshift` and `selinux.enabled` are enabled. | +| Device-plugin Pods rejected by SCC admission | Inspect DaemonSet events, the SCC, the ClusterRole granting permission to use it, and the RoleBinding subject. When reusing an SCC, check that its matching ClusterRole exists. | +| Device-plugin Pods remain `Pending` | Check node labels, merged selectors, taints, and tolerations. Remove the default `gpu=on` selector with `gpu: null` when using the NVIDIA label. | +| Device-plugin Pod remains in `Init` | Inspect init-container logs. If the Toolkit readiness check is enabled, confirm the `toolkit-ready` file exists. If `selinux-relabel` fails, check `chcon` errors and host paths. | +| NVIDIA hook, missing-library, or unresolved CDI errors | Check GPU Operator readiness, RuntimeClass handler, driver root, hook executable, and the generated CDI specification. Follow [GPU Operator runtime troubleshooting](../troubleshooting/troubleshooting.md#nvidia-toolkit-gpu-operator-25-10). | +| Workload cannot access HAMi shared files | Check the selected SCC, workload UID, directory modes, SELinux labels, and `selinux-relabel` logs. Do not disable SELinux to bypass the error. | +| GPU sharing workload does not complete | Run `oc describe job hami-smoke -n hami-test` and inspect Pod events and container logs. Confirm the webhook selected `hami-scheduler`, sufficient vGPU resources exist, and the image matches the driver. | + +After verification, delete the sample Job and ServiceAccount: + +```bash +oc delete job hami-smoke -n hami-test +oc delete serviceaccount hami-smoke -n hami-test +``` diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/installation/openshift-installation.md b/i18n/zh/docusaurus-plugin-content-docs/current/installation/openshift-installation.md new file mode 100644 index 000000000..f145a94e1 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/installation/openshift-installation.md @@ -0,0 +1,228 @@ +--- +title: 在 Red Hat OpenShift 上安装 HAMi +sidebar_label: OpenShift 上的 HAMi +translated: true +--- + +本文介绍如何在 OpenShift 上安装 HAMi，适用于通过 NVIDIA GPU Operator 管理 GPU 驱动和 NVIDIA Container Toolkit 的集群。 + +## 前置条件 + +通用环境要求参阅[前置条件](./prerequisites.md)。OpenShift 还需满足以下条件： + +- 已通过 `oc` 连接集群，且有权创建项目、安全上下文约束（SecurityContextConstraints，SCC）和集群级 RBAC 资源。 +- 已按照 NVIDIA 的 [OpenShift 安装指南](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/install-gpu-ocp.html)安装 GPU Operator 和 Node Feature Discovery，并在 CRI-O 中启用 CDI。 + +检查集群状态，并将以下变量设为实际的 ClusterPolicy 名称、GPU Operator 命名空间和 GPU 节点名称： + +```bash +oc whoami +oc version +oc get nodes -o wide +oc get clusterpolicy +oc get nodes -L nvidia.com/gpu.present + +export GPU_CLUSTER_POLICY='gpu-cluster-policy' +export GPU_OPERATOR_NAMESPACE='nvidia-gpu-operator' +export GPU_NODE='REPLACE_WITH_GPU_NODE_NAME' + +oc describe node "$GPU_NODE" +oc get pods -n "$GPU_OPERATOR_NAMESPACE" -o wide +``` + +## 禁用 NVIDIA device plugin + +在 HAMi 管理的节点上，只能由 HAMi device plugin 注册 `nvidia.com/gpu`。替换原有插件前，在维护窗口内结束或迁移使用原有插件的 GPU 工作负载。 + +如果所有 NVIDIA GPU 节点都由 HAMi 管理，通过 `ClusterPolicy` 在整个集群禁用 GPU Operator 的 device plugin。如果部分节点仍需使用 NVIDIA device plugin，应先将两种插件部署到不同的节点。 + +```bash +oc patch clusterpolicy "$GPU_CLUSTER_POLICY" --type=merge \ + -p '{"spec":{"devicePlugin":{"enabled":false}}}' + +oc get clusterpolicy "$GPU_CLUSTER_POLICY" \ + -o jsonpath='state={.status.state}{"\n"}devicePlugin.enabled={.spec.devicePlugin.enabled}{"\n"}cdi.enabled={.spec.cdi.enabled}{"\n"}' +oc get daemonsets,pods -n "$GPU_OPERATOR_NAMESPACE" +``` + +等待 ClusterPolicy 的状态变为 `state=ready`，确认 `devicePlugin.enabled=false` 且原 device-plugin Pod 已停止。保留驱动和 Toolkit 组件。 + +## 添加 Helm 仓库 + +```bash +helm repo add hami-charts https://project-hami.github.io/HAMi/ +helm repo update +``` + +## 配置 HAMi + +:::warning 尚未发布的 Chart values + +当前已发布的 Helm 仓库提供 HAMi Chart v2.10.0，其中不包含本指南使用的 `platform.openshift` 和 `selinux.enabled` values。包含这些 values 的 HAMi Chart v2.11.0 尚未发布。 + +在 v2.11.0 发布前，必须从源码仓库安装 HAMi，不能使用 `hami-charts/hami`： + +```bash +git clone https://github.com/Project-HAMi/HAMi.git +cd HAMi +helm dependency build charts/hami +``` + +在下方安装命令中使用本地 `./charts/hami` 路径。此 OpenShift 配置不能使用已发布的 v2.10.0 Chart。 + +::: + +创建独立项目： + +```bash +oc new-project hami-system +``` + +将以下内容保存为 `values-openshift.yaml`，并按集群配置调整节点标签、驱动根目录和 Toolkit 可执行文件路径。 + +根据 `oc version` 返回的 Kubernetes 服务端版本，填写 `scheduler.kubeScheduler.image.tag`。使用已发布的上游 Kubernetes 镜像标签，不要使用 OpenShift 版本号或带 OpenShift 专用后缀的版本号。 + +```yaml +platform: + openshift: true + +selinux: + enabled: true + +scheduler: + kubeScheduler: + image: + tag: "REPLACE_WITH_KUBERNETES_IMAGE_TAG" + +devicePlugin: + deviceListStrategy: cdi-annotations + nvidiaDriverRoot: /run/nvidia/driver + nvidiaHookPath: /usr/local/nvidia/toolkit/nvidia-ctk + nvidiaNodeSelector: + gpu: null + nvidia.com/gpu.present: "true" + service: + type: ClusterIP +``` + +- `gpu: null` 移除 Chart 默认的 `gpu=on` 节点选择条件。省略此项时，Helm 会合并两个条件，节点必须同时具备两种标签。如果 HAMi 只管理部分 GPU 节点，在选择器中添加专用节点标签。 +- Chart 已容忍 `nvidia.com/gpu:NoSchedule` 污点。仅当 GPU 节点有其他污点时，覆盖 `devicePlugin.tolerations`。 +- 如果 device plugin 依赖 NVIDIA 运行时处理 `NVIDIA_VISIBLE_DEVICES`，先确认 `nvidia` RuntimeClass 已存在，再设置 `devicePlugin.runtimeClassName: nvidia`。此值也用于 HAMi 为 NVIDIA 工作负载添加的 RuntimeClass。 + + GPU Operator v25.10.0 及以后版本启用 CDI 时，NVIDIA 要求此类 GPU 管理容器指定该 RuntimeClass；由 NRI 插件处理设备注入时无需设置。详见 [CDI 与 GPU 管理容器](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/cdi.html#cdi-and-gpu-management-containers)。 + +- `nvidiaDriverRoot` 指向宿主机上的驱动安装根目录，不能填写单个库文件所在的目录。驱动直接安装在宿主机上时，使用 `/`。`nvidiaHookPath` 指向宿主机上的 `nvidia-ctk` 可执行文件。详见 [CDI 配置](./configure-cdi.md)。 +- 如果现有环境使用 HAMi 的 `envvar` 注入，设置 `deviceListStrategy: envvar` 并省略 `nvidiaHookPath`。确认 NVIDIA 运行时能为 device plugin 和工作负载处理 `NVIDIA_VISIBLE_DEVICES`。排查方法见 [GPU Operator 运行时故障排查](../troubleshooting/troubleshooting.md#nvidia-toolkit-gpu-operator-25-10)。 + + 在 CRI-O 上关闭已有 GPU Operator 的 CDI 时，按 NVIDIA 的[关闭 CDI 步骤](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/26.3/cdi.html#disabling-cdi)操作：先将 GPU 节点的 `nvidia.com/gpu.deploy.operator-validator` 标签设为 `false`，再将 ClusterPolicy 的 `cdi.enabled` 设为 `false`，完成后将节点标签恢复为 `true`。保持 CDI 启用，或首次安装 GPU Operator 时就禁用 CDI，均无需执行此切换流程。 + +仅当 GPU Operator 会创建 `toolkit-ready` 文件时，才启用 `devicePlugin.gpuOperatorToolkitReady.enabled`。启用前，确认文件位于 `devicePlugin.gpuOperatorToolkitReady.hostPath` 指定的目录下，通常是 `/run/nvidia/validations`。文件缺失时，初始化容器会一直等待。 + +## 安装 HAMi + +OpenShift 支持需要 HAMi Helm Chart v2.11.0 或更高版本。在该版本发布前，在前面准备的源码目录中执行以下命令。 + +```bash +helm upgrade --install hami ./charts/hami \ + --namespace hami-system \ + -f values-openshift.yaml \ + --wait --timeout 10m +``` + +## 验证部署 + +```bash +oc get pods -n hami-system +oc get node "$GPU_NODE" \ + -o 'custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\.com/gpu' +``` + +确认调度器和 device-plugin Pod 已就绪，且 GPU 节点上报的 `nvidia.com/gpu` 可分配数量大于 0。 + +### 运行 GPU 共享任务 + +创建独立的应用项目和 ServiceAccount，不授予 ServiceAccount 使用 device-plugin SCC 的权限。 + +使用 Job 运行 NVIDIA 的 [CUDA VectorAdd 示例](https://docs.nvidia.com/datacenter/cloud-native/openshift/24.6.2/install-gpu-ocp.html#running-a-sample-gpu-application)。该任务申请一个 vGPU、1024 MiB 显存和 25% GPU 核心配额。镜像要求驱动兼容 CUDA 12.5；如果当前驱动不兼容，替换为功能相同且兼容的示例镜像。 + +```bash +oc new-project hami-test +oc create serviceaccount hami-smoke -n hami-test +``` + +保存为 `hami-smoke.yaml`： + +```yaml +apiVersion: batch/v1 +kind: Job +metadata: + name: hami-smoke + namespace: hami-test +spec: + backoffLimit: 0 + template: + spec: + serviceAccountName: hami-smoke + restartPolicy: Never + tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + containers: + - name: vectoradd + image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0-ubi8 + securityContext: + runAsNonRoot: true + allowPrivilegeEscalation: false + capabilities: + drop: ["ALL"] + seccompProfile: + type: RuntimeDefault + resources: + limits: + nvidia.com/gpu: 1 + nvidia.com/gpumem: 1024 + nvidia.com/gpucores: 25 +``` + +如果安装 HAMi 时调整了容忍度，相应修改 Job 的 `tolerations`，使其匹配 GPU 节点。提交 Job，查看日志和创建的 Pod： + +```bash +oc apply -f hami-smoke.yaml +oc wait -n hami-test --for=condition=complete job/hami-smoke --timeout=300s +oc logs -n hami-test job/hami-smoke +oc get pods -n hami-test -l job-name=hami-smoke \ + -o 'custom-columns=NAME:.metadata.name,NODE:.spec.nodeName,SCHEDULER:.spec.schedulerName,RUNTIMECLASS:.spec.runtimeClassName,SCC:.metadata.annotations.openshift\.io/scc' +oc get pods -n hami-test -l job-name=hami-smoke -o yaml +``` + +确认以下结果： + +- 日志包含 `Test PASSED`。 +- Pod 使用 `hami-scheduler` 调度器。如果设置了 `devicePlugin.runtimeClassName`，Pod 使用对应的 RuntimeClass。 +- Pod 运行在 HAMi 管理的 GPU 节点上。 +- Pod 注解包含 HAMi 分配信息。 +- 应用使用合适的受限 SCC。 + +Job 控制器使用应用的 ServiceAccount 创建 Pod。直接以管理员身份创建 Pod，可能使用管理员的 SCC 权限而掩盖应用权限问题。 + +该任务用于验证 Pod 准入、调度、设备分配和 CUDA 程序运行。验证负载下的显存与核心配额限制，还需运行[显存分配](../userguide/nvidia-device/examples/allocate-device-memory.md)和[核心分配](../userguide/nvidia-device/examples/allocate-device-core.md)示例。 + +## 故障排查与清理 + +| 现象 | 检查与处理 | +| --- | --- | +| 安装后缺少 SCC 或 SELinux 初始化容器 | 检查是否启用了 `platform.openshift` 和 `selinux.enabled`。 | +| device-plugin Pod 被 SCC 准入拒绝 | 查看 DaemonSet 事件，检查 SCC、授予 SCC 使用权限的 ClusterRole，以及 RoleBinding 的绑定对象。复用 SCC 时，确认对应的 ClusterRole 已存在。 | +| device-plugin Pod 一直处于 `Pending` | 检查节点标签、合并后的选择器、污点和容忍度。使用 NVIDIA 标签时，通过 `gpu: null` 移除默认的 `gpu=on` 选择条件。 | +| device-plugin Pod 停留在 `Init` | 查看初始化容器日志。若启用了 Toolkit 就绪检查，确认 `toolkit-ready` 文件存在；若 `selinux-relabel` 失败，检查 `chcon` 错误和宿主机路径。 | +| 出现 NVIDIA hook、缺少库文件或 CDI 设备无法解析错误 | 检查 GPU Operator 状态、RuntimeClass handler、驱动根目录、hook 可执行文件及生成的 CDI 规范。参考 [GPU Operator 运行时故障排查](../troubleshooting/troubleshooting.md#nvidia-toolkit-gpu-operator-25-10)。 | +| 工作负载无法访问 HAMi 共享文件 | 检查实际使用的 SCC、工作负载 UID、目录权限、SELinux 标签和 `selinux-relabel` 日志。不要关闭 SELinux 来绕过错误。 | +| GPU 共享任务未完成 | 运行 `oc describe job hami-smoke -n hami-test`，查看 Pod 事件和容器日志。确认 webhook 选择了 `hami-scheduler`，vGPU 资源充足，且镜像与驱动兼容。 | + +验证完成后，删除示例 Job 和 ServiceAccount： + +```bash +oc delete job hami-smoke -n hami-test +oc delete serviceaccount hami-smoke -n hami-test +``` diff --git a/sidebars.js b/sidebars.js index 407a4d582..b5ee794f4 100644 --- a/sidebars.js +++ b/sidebars.js @@ -53,6 +53,7 @@ module.exports = { "installation/aws-installation", "installation/k3s-installation", "installation/gke-installation", + "installation/openshift-installation", ], }, {