Merge pull request #44961 from windsonsea/gpuyh
[zh] Add translations to scheduling-gpus.mdpull/45407/head
Kubernetes Prow Robot
GitHub
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@ -100,7 +100,7 @@ spec:
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```
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<!--
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## Clusters containing different types of GPUs
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## Manage clusters with different types of GPUs
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If different nodes in your cluster have different types of GPUs, then you
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can use [Node Labels and Node Selectors](/docs/tasks/configure-pod-container/assign-pods-nodes/)
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@ -108,7 +108,7 @@ to schedule pods to appropriate nodes.
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For example:
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-->
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## 集群内存在不同类型的 GPU {#clusters-containing-different-types-of-gpus}
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## 管理配有不同类型 GPU 的集群 {#manage-clusters-with-different-types-of-gpus}
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如果集群内部的不同节点上有不同类型的 NVIDIA GPU，
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那么你可以使用[节点标签和节点选择器](/zh-cn/docs/tasks/configure-pod-container/assign-pods-nodes/)来将
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@ -116,6 +116,13 @@ Pod 调度到合适的节点上。
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例如：
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<!--
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```shell
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# Label your nodes with the accelerator type they have.
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kubectl label nodes node1 accelerator=example-gpu-x100
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kubectl label nodes node2 accelerator=other-gpu-k915
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```
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-->
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```shell
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# 为你的节点加上它们所拥有的加速器类型的标签
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kubectl label nodes node1 accelerator=example-gpu-x100
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@ -134,18 +141,92 @@ a different label key if you prefer.
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## 自动节点标签 {#node-labeller}
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<!--
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If you're using AMD GPU devices, you can deploy
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[Node Labeller](https://github.com/RadeonOpenCompute/k8s-device-plugin/tree/master/cmd/k8s-node-labeller).
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Node Labeller is a {{< glossary_tooltip text="controller" term_id="controller" >}} that automatically
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labels your nodes with GPU device properties.
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Similar functionality for NVIDIA is provided by
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[GPU feature discovery](https://github.com/NVIDIA/gpu-feature-discovery/blob/main/README.md).
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As an administrator, you can automatically discover and label all your GPU enabled nodes
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by deploying Kubernetes [Node Feature Discovery](https://github.com/kubernetes-sigs/node-feature-discovery) (NFD).
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NFD detects the hardware features that are available on each node in a Kubernetes cluster.
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Typically, NFD is configured to advertise those features as node labels, but NFD can also add extended resources, annotations, and node taints.
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NFD is compatible with all [supported versions](/releases/version-skew-policy/#supported-versions) of Kubernetes.
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By default NFD create the [feature labels](https://kubernetes-sigs.github.io/node-feature-discovery/master/usage/features.html) for the detected features.
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Administrators can leverage NFD to also taint nodes with specific features, so that only pods that request those features can be scheduled on those nodes.
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-->
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如果你在使用 AMD GPU，你可以部署
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[Node Labeller](https://github.com/RadeonOpenCompute/k8s-device-plugin/tree/master/cmd/k8s-node-labeller)，
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它是一个 {{< glossary_tooltip text="控制器" term_id="controller" >}}，
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会自动给节点打上 GPU 设备属性标签。
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作为管理员，你可以通过部署 Kubernetes
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[Node Feature Discovery](https://github.com/kubernetes-sigs/node-feature-discovery) (NFD)
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来自动发现所有启用 GPU 的节点并为其打标签。NFD 检测 Kubernetes 集群中每个节点上可用的硬件特性。
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通常，NFD 被配置为以节点标签广告这些特性，但 NFD 也可以添加扩展的资源、注解和节点污点。
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NFD 兼容所有[支持版本](/zh-cn/releases/version-skew-policy/#supported-versions)的 Kubernetes。
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NFD 默认会为检测到的特性创建[特性标签](https://kubernetes-sigs.github.io/node-feature-discovery/master/usage/features.html)。
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管理员可以利用 NFD 对具有某些具体特性的节点添加污点，以便只有请求这些特性的 Pod 可以被调度到这些节点上。
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对于 NVIDIA GPU，[GPU feature discovery](https://github.com/NVIDIA/gpu-feature-discovery/blob/main/README.md)
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提供了类似功能。
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<!--
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You also need a plugin for NFD that adds appropriate labels to your nodes; these might be generic
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labels or they could be vendor specific. Your GPU vendor may provide a third party
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plugin for NFD; check their documentation for more details.
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-->
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你还需要一个 NFD 插件，将适当的标签添加到你的节点上；
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这些标签可以是通用的，也可以是供应商特定的。你的 GPU 供应商可能会为 NFD 提供第三方插件；
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更多细节请查阅他们的文档。
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<!--
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{{< highlight yaml "linenos=false,hl_lines=6-18" >}}
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apiVersion: v1
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kind: Pod
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metadata:
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name: example-vector-add
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spec:
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# You can use Kubernetes node affinity to schedule this Pod onto a node
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# that provides the kind of GPU that its container needs in order to work
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affinity:
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nodeAffinity:
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requiredDuringSchedulingIgnoredDuringExecution:
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nodeSelectorTerms:
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- matchExpressions:
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- key: "gpu.gpu-vendor.example/installed-memory"
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operator: Gt # (greater than)
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values: ["40535"]
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- key: "feature.node.kubernetes.io/pci-10.present" # NFD Feature label
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values: ["true"] # (optional) only schedule on nodes with PCI device 10
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restartPolicy: OnFailure
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containers:
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- name: example-vector-add
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image: "registry.example/example-vector-add:v42"
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resources:
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limits:
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gpu-vendor.example/example-gpu: 1 # requesting 1 GPU
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{{< /highlight >}}
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-->
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{{< highlight yaml "linenos=false,hl_lines=6-18" >}}
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apiVersion: v1
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kind: Pod
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metadata:
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name: example-vector-add
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spec:
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# 你可以使用 Kubernetes 节点亲和性将此 Pod 调度到提供其容器所需的那种 GPU 的节点上
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affinity:
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nodeAffinity:
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requiredDuringSchedulingIgnoredDuringExecution:
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nodeSelectorTerms:
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- matchExpressions:
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- key: "gpu.gpu-vendor.example/installed-memory"
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operator: Gt #（大于）
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values: ["40535"]
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- key: "feature.node.kubernetes.io/pci-10.present" # NFD 特性标签
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values: ["true"] #（可选）仅调度到具有 PCI 设备 10 的节点上
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restartPolicy: OnFailure
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containers:
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- name: example-vector-add
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image: "registry.example/example-vector-add:v42"
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resources:
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limits:
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gpu-vendor.example/example-gpu: 1 # 请求 1 个 GPU
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{{< /highlight >}}
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<!--
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#### GPU vendor implementations
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- [Intel](https://intel.github.io/intel-device-plugins-for-kubernetes/cmd/gpu_plugin/README.html)
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- [NVIDIA](https://github.com/NVIDIA/gpu-feature-discovery/#readme)
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-->
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#### GPU 供应商实现
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- [Intel](https://intel.github.io/intel-device-plugins-for-kubernetes/cmd/gpu_plugin/README.html)
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- [NVIDIA](https://github.com/NVIDIA/gpu-feature-discovery/#readme)
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