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update content in zh assign-cpu-resource docs

pull/20684/head
展望 2020-04-30 20:32:05 +08:00
parent bc3861ac11
commit 30a447f59f
1 changed files with 6 additions and 6 deletions
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content/zh/docs/tasks/configure-pod-container/assign-cpu-resource.md
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@ -61,7 +61,7 @@ API, `metrics.k8s.io`) is running, type the following command:
查看是 metrics-server（或者其他资源度量 API 服务提供者，`metrics.k8s.io` ）是否正在运行，请键入以下命令： 查看是 metrics-server（或者其他资源度量 API 服务提供者，`metrics.k8s.io` ）是否正在运行，请键入以下命令：
```shell ```shell
kubectl get API services kubectl get apiservices
``` ```
<!-- <!--
If the resource metrics API is available, the output will include a If the resource metrics API is available, the output will include a
@ -92,7 +92,7 @@ create in this exercise are isolated from the rest of your cluster.
创建一个命名空间 {{< glossary_tooltip term_id="namespace" >}}，以便在本练习中创建的资源与集群的其余部分资源隔离。 创建一个命名空间 {{< glossary_tooltip term_id="namespace" >}}，以便在本练习中创建的资源与集群的其余部分资源隔离。
```shell ```shell
kubectl create namespace CPU -example kubectl create namespace cpu-example
``` ```
<!-- <!--
@ -159,9 +159,9 @@ and a CPU limit of 1 CPU.
```yaml ```yaml
resources: resources:
limits: limits:
CPU : "1" cpu: "1"
requests: requests:
CPU : 500m cpu: 500m
``` ```
<!-- <!--
@ -180,7 +180,7 @@ just a bit less than the limit of 1 CPU specified in the Pod configuration.
此示例的输出，显示 Pod 使用的是974 milliCPU，即仅略低于 Pod 配置中指定的 1 个 CPU 的限制。 此示例的输出，显示 Pod 使用的是974 milliCPU，即仅略低于 Pod 配置中指定的 1 个 CPU 的限制。
``` ```
NAME CPU(cores) MEMORY(bytes) NAME CPU(cores) MEMORY(bytes)
CPU -demo 974m <something> cpu-demo 974m <something>
``` ```
<!-- <!--
Recall that by setting `-cpu "2"`, you configured the Container to attempt to use 2 CPUs, but the Container is only being allowed to use about 1 CPU. The container's CPU use is being throttled, because the container is attempting to use more CPU resources than its limit. Recall that by setting `-cpu "2"`, you configured the Container to attempt to use 2 CPUs, but the Container is only being allowed to use about 1 CPU. The container's CPU use is being throttled, because the container is attempting to use more CPU resources than its limit.