Kubernetes Prow Robot
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e94ce37cc2
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@ -92,12 +92,14 @@ specification as they request other types of resources, with the following limit
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* 扩展资源仅可作为整数资源使用，并且不能被过量使用
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* 设备不能在容器之间共享
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### 示例 {#example-pod}
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<!--
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Suppose a Kubernetes cluster is running a device plugin that advertises resource `hardware-vendor.example/foo`
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on certain nodes. Here is an example of a pod requesting this resource to run a demo workload:
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-->
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假设 Kubernetes 集群正在运行一个设备插件，该插件在一些节点上公布的资源为 `hardware-vendor.example/foo`。
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下面就是一个 Pod 示例，请求此资源以运行某演示负载：
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下面就是一个 Pod 示例，请求此资源以运行一个工作负载的示例：
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```yaml
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---
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@ -140,8 +142,12 @@ The general workflow of a device plugin includes the following steps:
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一个 gRPC 服务，该服务实现以下接口：
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<!--
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```gRPC
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service DevicePlugin {
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// GetDevicePluginOptions returns options to be communicated with Device Manager.
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rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {}
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// ListAndWatch returns a stream of List of Devices
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// Whenever a Device state change or a Device disappears, ListAndWatch
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// returns the new list
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@ -168,6 +174,9 @@ The general workflow of a device plugin includes the following steps:
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-->
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```gRPC
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service DevicePlugin {
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// GetDevicePluginOptions 返回与设备管理器沟通的选项。
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rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {}
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// ListAndWatch 返回 Device 列表构成的数据流。
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// 当 Device 状态发生变化或者 Device 消失时，ListAndWatch
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// 会返回新的列表。
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@ -331,6 +340,8 @@ service PodResourcesLister {
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}
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```
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### `List` gRPC 端点 {#grpc-endpoint-list}
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<!--
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The `List` endpoint provides information on resources of running pods, with details such as the
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id of exclusively allocated CPUs, device id as it was reported by device plugins and id of
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@ -387,6 +398,51 @@ message ContainerDevices {
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}
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```
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<!--
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{{< note >}}
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cpu_ids in the `ContainerResources` in the `List` endpoint correspond to exclusive CPUs allocated
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to a partilar container. If the goal is to evaluate CPUs that belong to the shared pool, the `List`
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endpoint needs to be used in conjunction with the `GetAllocatableResources` endpoint as explained
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below:
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1. Call `GetAllocatableResources` to get a list of all the allocatable CPUs
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2. Call `GetCpuIds` on all `ContainerResources` in the system
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3. Subtract out all of the CPUs from the `GetCpuIds` calls from the `GetAllocatableResources` call
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{{< /note >}}
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-->
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{{< note >}}
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`List` 端点中的 `ContainerResources` 中的 cpu_ids 对应于分配给某个容器的专属 CPU。
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如果要统计共享池中的 CPU，`List` 端点需要与 `GetAllocatableResources` 端点一起使用，如下所述:
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1. 调用 `GetAllocatableResources` 获取所有可用的 CPUs。
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2. 在系统中所有的 `ContainerResources` 上调用 `GetCpuIds`。
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3. 用 `GetAllocatableResources` 获取的 CPU 数减去 `GetCpuIds` 获取的 CPU 数。
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{{< /note >}}
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### `GetAllocatableResources` gRPC 端点 {#grpc-endpoint-getallocatableresources}
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{{< feature-state state="beta" for_k8s_version="v1.23" >}}
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<!--
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{{< note >}}
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`GetAllocatableResources` should only be used to evaluate [allocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable)
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resources on a node. If the goal is to evaluate free/unallocated resources it should be used in
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conjunction with the List() endpoint. The result obtained by `GetAllocatableResources` would remain
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the same unless the underlying resources exposed to kubelet change. This happens rarely but when
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it does (for example: hotplug/hotunplug, device health changes), client is expected to call
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`GetAlloctableResources` endpoint.
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However, calling `GetAllocatableResources` endpoint is not sufficient in case of cpu and/or memory
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update and Kubelet needs to be restarted to reflect the correct resource capacity and allocatable.
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{{< /note >}}
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-->
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{{< note >}}
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`GetAllocatableResources` 应该仅被用于评估一个节点上的[可分配的](/zh/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable)
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资源。如果目标是评估空闲/未分配的资源，此调用应该与 List() 端点一起使用。
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除非暴露给 kubelet 的底层资源发生变化 否则 `GetAllocatableResources` 得到的结果将保持不变。
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这种情况很少发生，但当发生时（例如：热插拔，设备健康状况改变），客户端应该调用 `GetAlloctableResources` 端点。
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然而，调用 `GetAllocatableResources` 端点在 cpu、内存被更新的情况下是不够的，
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Kubelet 需要重新启动以获取正确的资源容量和可分配的资源。
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{{< /note >}}
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<!--
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GetAllocatableResources provides information on resources initially available on the worker node.
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It provides more information than kubelet exports to APIServer.
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@ -394,7 +450,6 @@ It provides more information than kubelet exports to APIServer.
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端点 `GetAllocatableResources` 提供最初在工作节点上可用的资源的信息。
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此端点所提供的信息比导出给 API 服务器的信息更丰富。
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```gRPC
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// AllocatableResourcesResponses 包含 kubelet 所了解到的所有设备的信息
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message AllocatableResourcesResponse {
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@ -405,6 +460,23 @@ message AllocatableResourcesResponse {
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```
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<!--
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Starting from Kubernetes v1.23, the `GetAllocatableResources` is enabled by default.
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You can disable it by turning off the
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`KubeletPodResourcesGetAllocatable` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/).
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Preceding Kubernetes v1.23, to enable this feature `kubelet` must be started with the following flag:
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`--feature-gates=KubeletPodResourcesGetAllocatable=true`
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-->
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从 Kubernetes v1.23 开始，`GetAllocatableResources` 被默认启用。
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你可以通过关闭 `KubeletPodResourcesGetAllocatable`
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[特性门控](/zh/docs/reference/command-line-tools-reference/feature-gates/) 来禁用。
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在 Kubernetes v1.23 之前，要启用这一功能，`kubelet` 必须用以下标志启动：
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`--feature-gates=KubeletPodResourcesGetAllocatable=true`
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<!--
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`ContainerDevices` do expose the topology information declaring to which NUMA cells the device is affine.
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The NUMA cells are identified using a opaque integer ID, which value is consistent to what device
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@ -515,6 +587,7 @@ Here are some examples of device plugin implementations:
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* 需要 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) 2.0，以允许运行 Docker 容器的时候启用 GPU。
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* [为 Container-Optimized OS 所提供的 NVIDIA GPU 设备插件](https://github.com/GoogleCloudPlatform/container-engine-accelerators/tree/master/cmd/nvidia_gpu)
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* [RDMA 设备插件](https://github.com/hustcat/k8s-rdma-device-plugin)
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* [SocketCAN 设备插件](https://github.com/collabora/k8s-socketcan)
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* [Solarflare 设备插件](https://github.com/vikaschoudhary16/sfc-device-plugin)
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* [SR-IOV 网络设备插件](https://github.com/intel/sriov-network-device-plugin)
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* [Xilinx FPGA 设备插件](https://github.com/Xilinx/FPGA_as_a_Service/tree/master/k8s-fpga-device-plugin)
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@ -531,5 +604,3 @@ Here are some examples of device plugin implementations:
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* 查看在上如何[公布节点上的扩展资源](/zh/docs/tasks/administer-cluster/extended-resource-node/)
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* 阅读如何在 Kubernetes 中使用 [TLS Ingress 的硬件加速](https://kubernetes.io/blog/2019/04/24/hardware-accelerated-ssl/tls-termination-in-ingress-controllers-using-kubernetes-device-plugins-and-runtimeclass/)
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* 学习[拓扑管理器](/zh/docs/tasks/administer-cluster/topology-manager/)
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