Merge pull request #37121 from windsonsea/schgpu

[zh] Sync scheduling-gpus.md

Author: k8s-ci-robot

content/zh-cn/docs/tasks/manage-gpus/scheduling-gpus.md

@@ -16,215 +16,90 @@ description: Configure and schedule GPUs for use as a resource by nodes in a clu
 {{< feature-state state="beta" for_k8s_version="v1.10" >}}
 
 <!--
-Kubernetes includes **experimental** support for managing AMD and NVIDIA GPUs
+Kubernetes includes **experimental** support for managing GPUs
 (graphical processing units) across several nodes.
 
-This page describes how users can consume GPUs across different Kubernetes versions
-and the current limitations.
+This page describes how users can consume GPUs, and outlines
+some of the limitations in the implementation.
 -->
-Kubernetes 支持对节点上的 AMD 和 NVIDIA GPU （图形处理单元）进行管理，目前处于**实验**状态。
+Kubernetes 支持对若干节点上的 GPU（图形处理单元）进行管理，目前处于**实验**状态。
 
-本页介绍用户如何在不同的 Kubernetes 版本中使用 GPU，以及当前存在的一些限制。
+本页介绍用户如何使用 GPU 以及当前存在的一些限制。
 
 <!-- body -->
 
 <!--
 ## Using device plugins
 
-Kubernetes implements {{< glossary_tooltip text="Device Plugins" term_id="device-plugin" >}}
+Kubernetes implements {{< glossary_tooltip text="device plugins" term_id="device-plugin" >}}
 to let Pods access specialized hardware features such as GPUs.
-
-As an administrator, you have to install GPU drivers from the corresponding
-hardware vendor on the nodes and run the corresponding device plugin from the
-GPU vendor:
 -->
 ## 使用设备插件  {#using-device-plugins}
 
-Kubernetes 实现了{{< glossary_tooltip text="设备插件（Device Plugins）" term_id="device-plugin" >}}
+Kubernetes 实现了{{< glossary_tooltip text="设备插件（Device Plugin）" term_id="device-plugin" >}}
 以允许 Pod 访问类似 GPU 这类特殊的硬件功能特性。
 
-作为集群管理员，你要在节点上安装来自对应硬件厂商的 GPU 驱动程序，并运行
-来自 GPU 厂商的对应的设备插件。
+{{% thirdparty-content %}}
 
-* [AMD](#deploying-amd-gpu-device-plugin)
-* [NVIDIA](#deploying-nvidia-gpu-device-plugin)
+<!--
+As an administrator, you have to install GPU drivers from the corresponding
+hardware vendor on the nodes and run the corresponding device plugin from the
+GPU vendor:
+-->
+作为集群管理员，你要在节点上安装来自对应硬件厂商的 GPU 驱动程序，并运行来自
+GPU 厂商的对应设备插件。
 
+* [AMD](https://github.com/RadeonOpenCompute/k8s-device-plugin#deployment)
+* [Intel](https://intel.github.io/intel-device-plugins-for-kubernetes/cmd/gpu_plugin/README.html)
+* [NVIDIA](https://github.com/NVIDIA/k8s-device-plugin#quick-start)
 <!--
-When the above conditions are true, Kubernetes will expose `amd.com/gpu` or
-`nvidia.com/gpu` as a schedulable resource.
+Once you have installed the plugin, your cluster exposes a custom schedulable
+resource such as `amd.com/gpu` or `nvidia.com/gpu`.
 
 You can consume these GPUs from your containers by requesting
-`<vendor>.com/gpu` the same way you request `cpu` or `memory`.
-However, there are some limitations in how you specify the resource requirements
-when using GPUs:
+the custom GPU resource, the same way you request `cpu` or `memory`.
+However, there are some limitations in how you specify the resource
+requirements for custom devices.
 -->
-当以上条件满足时，Kubernetes 将暴露 `amd.com/gpu` 或 `nvidia.com/gpu` 为
-可调度的资源。
+一旦你安装了插件，你的集群就会暴露一个自定义可调度的资源，例如 `amd.com/gpu` 或 `nvidia.com/gpu`。
 
-你可以通过请求 `<vendor>.com/gpu` 资源来使用 GPU 设备，就像你为 CPU
-和内存所做的那样。
-不过，使用 GPU 时，在如何指定资源需求这个方面还是有一些限制的：
+你可以通过请求这个自定义的 GPU 资源在你的容器中使用这些 GPU，其请求方式与请求 `cpu` 或 `memory` 时相同。
+不过，在如何指定自定义设备的资源请求方面存在一些限制。
 
 <!--
-- GPUs are only supposed to be specified in the `limits` section, which means:
-  * You can specify GPU `limits` without specifying `requests` because
-    Kubernetes will use the limit as the request value by default.
-  * You can specify GPU in both `limits` and `requests` but these two values
-    must be equal.
-  * You cannot specify GPU `requests` without specifying `limits`.
-- Containers (and Pods) do not share GPUs. There's no overcommitting of GPUs.
-- Each container can request one or more GPUs. It is not possible to request a
-  fraction of a GPU.
+GPUs are only supposed to be specified in the `limits` section, which means:
+* You can specify GPU `limits` without specifying `requests`, because
+  Kubernetes will use the limit as the request value by default.
+* You can specify GPU in both `limits` and `requests` but these two values
+  must be equal.
+* You cannot specify GPU `requests` without specifying `limits`.
 -->
-- GPU 只能设置在 `limits` 部分，这意味着：
-  * 你可以指定 GPU 的 `limits` 而不指定其 `requests`，Kubernetes 将使用限制
-    值作为默认的请求值；
+- GPU 只能在 `limits` 部分指定，这意味着：
+  * 你可以指定 GPU 的 `limits` 而不指定其 `requests`，因为 Kubernetes 将默认使用限制
+    值作为请求值。
   * 你可以同时指定 `limits` 和 `requests`，不过这两个值必须相等。
   * 你不可以仅指定 `requests` 而不指定 `limits`。
-- 容器（以及 Pod）之间是不共享 GPU 的。GPU 也不可以过量分配（Overcommitting）。
-- 每个容器可以请求一个或者多个 GPU，但是用小数值来请求部分 GPU 是不允许的。
 
 <!--
-Here's an example:
+Here's an example manifest for a Pod that requests a GPU:
 -->
-这里是一个例子：
+以下是一个 Pod 请求 GPU 的示例清单：
 
 ```yaml
 apiVersion: v1
 kind: Pod
 metadata:
-  name: cuda-vector-add
+  name: example-vector-add
 spec:
   restartPolicy: OnFailure
   containers:
-    - name: cuda-vector-add
-      # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
-      image: "registry.k8s.io/cuda-vector-add:v0.1"
+    - name: example-vector-add
+      image: "registry.example/example-vector-add:v42"
       resources:
         limits:
-          nvidia.com/gpu: 1 # requesting 1 GPU
-```
-
-<!--
-### Deploying AMD GPU device plugin
-
-The [official AMD GPU device plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin)
-has the following requirements:
--->
-### 部署 AMD GPU 设备插件   {#deploying-amd-gpu-device-plugin}
-
-[官方的 AMD GPU 设备插件](https://github.com/RadeonOpenCompute/k8s-device-plugin)有以下要求：
-
-<!--
-- Kubernetes nodes have to be pre-installed with AMD GPU Linux driver.
-
-To deploy the AMD device plugin once your cluster is running and the above
-requirements are satisfied:
--->
-- Kubernetes 节点必须预先安装 AMD GPU 的 Linux 驱动。
-
-如果你的集群已经启动并且满足上述要求的话，可以这样部署 AMD 设备插件：
-
-```shell
-kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/v1.10/k8s-ds-amdgpu-dp.yaml
+          gpu-vendor.example/example-gpu: 1 # 请求 1 个 GPU
 ```
 
-<!--
-You can report issues with this third-party device plugin by logging an issue in
-[RadeonOpenCompute/k8s-device-plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin).
--->
-你可以到 [RadeonOpenCompute/k8s-device-plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin)
-项目报告有关此设备插件的问题。
-
-<!--
-### Deploying NVIDIA GPU device plugin
-
-There are currently two device plugin implementations for NVIDIA GPUs:
--->
-### 部署 NVIDIA GPU 设备插件  {#deploying-nvidia-gpu-device-plugin}
-
-对于 NVIDIA GPU，目前存在两种设备插件的实现：
-
-<!--
-#### Official NVIDIA GPU device plugin
-
-The [official NVIDIA GPU device plugin](https://github.com/NVIDIA/k8s-device-plugin)
-has the following requirements:
--->
-#### 官方的 NVIDIA GPU 设备插件
-
-[官方的 NVIDIA GPU 设备插件](https://github.com/NVIDIA/k8s-device-plugin) 有以下要求:
-
-<!--
-- Kubernetes nodes have to be pre-installed with NVIDIA drivers.
-- Kubernetes nodes have to be pre-installed with [nvidia-docker 2.0](https://github.com/NVIDIA/nvidia-docker)
-- Kubelet must use Docker as its container runtime
-- `nvidia-container-runtime` must be configured as the [default runtime](https://github.com/NVIDIA/k8s-device-plugin#preparing-your-gpu-nodes)
-  for Docker, instead of runc.
-- The version of the NVIDIA drivers must match the constraint ~= 384.81.
-
-To deploy the NVIDIA device plugin once your cluster is running and the above
-requirements are satisfied:
--->
-- Kubernetes 的节点必须预先安装了 NVIDIA 驱动
-- Kubernetes 的节点必须预先安装 [nvidia-docker 2.0](https://github.com/NVIDIA/nvidia-docker)
-- Kubelet 的容器运行时必须使用 Docker
-- Docker 的[默认运行时](https://github.com/NVIDIA/k8s-device-plugin#preparing-your-gpu-nodes)必须设置为
-  `nvidia-container-runtime`，而不是 `runc`。
-- NVIDIA 驱动程序的版本必须匹配 ~= 384.81
-
-如果你的集群已经启动并且满足上述要求的话，可以这样部署 NVIDIA 设备插件：
-
-```shell
-kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml
-```
-
-<!--
-You can report issues with this third-party device plugin by logging an issue in
-[NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin).
--->
-你可以通过在 [NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin) 中记录问题来报告此第三方设备插件的问题。
-
-<!--
-#### NVIDIA GPU device plugin used by GCE
-
-The [NVIDIA GPU device plugin used by GCE](https://github.com/GoogleCloudPlatform/container-engine-accelerators/tree/master/cmd/nvidia_gpu)
-doesn't require using nvidia-docker and should work with any container runtime
-that is compatible with the Kubernetes Container Runtime Interface (CRI). It's tested
-on [Container-Optimized OS](https://cloud.google.com/container-optimized-os/)
-and has experimental code for Ubuntu from 1.9 onwards.
--->
-#### GCE 中使用的 NVIDIA GPU 设备插件
-
-[GCE 使用的 NVIDIA GPU 设备插件](https://github.com/GoogleCloudPlatform/container-engine-accelerators/tree/master/cmd/nvidia_gpu) 并不要求使用 nvidia-docker，并且对于任何实现了 Kubernetes CRI 的容器运行时，都应该能够使用。这一实现已经在 [Container-Optimized OS](https://cloud.google.com/container-optimized-os/) 上进行了测试，并且在 1.9 版本之后会有对于 Ubuntu 的实验性代码。
-
-<!--
-You can use the following commands to install the NVIDIA drivers and device plugin:
--->
-你可以使用下面的命令来安装 NVIDIA 驱动以及设备插件：
-
-```shell
-# 在 Container-Optimized OS 上安装 NVIDIA 驱动:
-kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/daemonset.yaml
-
-# 在 Ubuntu 上安装 NVIDIA 驱动 (实验性质):
-kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/nvidia-driver-installer/ubuntu/daemonset.yaml
-
-# 安装设备插件:
-kubectl create -f https://raw.githubusercontent.com/kubernetes/kubernetes/release-1.12/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml
-```
-
-<!--
-You can report issues with using or deploying this third-party device plugin by logging an issue in
-[GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators).
-
-Google publishes its own [instructions](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus) for using NVIDIA GPUs on GKE .
--->
-你可以通过在 [GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators)
-中记录问题来报告使用或部署此第三方设备插件的问题。
-
-关于如何在 GKE 上使用 NVIDIA GPU，Google 也提供自己的[指令](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus)。
-
 <!--
 ## Clusters containing different types of GPUs
 
@@ -234,20 +109,26 @@ to schedule pods to appropriate nodes.
 
 For example:
 -->
-## 集群内存在不同类型的 GPU
+## 集群内存在不同类型的 GPU  {#clusters-containing-different-types-of-gpus}
 
-如果集群内部的不同节点上有不同类型的 NVIDIA GPU，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 pod 调度到合适的节点上。
+如果集群内部的不同节点上有不同类型的 NVIDIA GPU，
+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
+Pod 调度到合适的节点上。
 
 例如：
 
 ```shell
 # 为你的节点加上它们所拥有的加速器类型的标签
-kubectl label nodes <node-with-k80> accelerator=nvidia-tesla-k80
-kubectl label nodes <node-with-p100> accelerator=nvidia-tesla-p100
+kubectl label nodes node1 accelerator=example-gpu-x100
+kubectl label nodes node2 accelerator=other-gpu-k915
 ```
 
+<!--
+That label key `accelerator` is just an example; you can use
+a different label key if you prefer.
+-->
+这个标签键 `accelerator` 只是一个例子；如果你愿意，可以使用不同的标签键。
+
 <!--
 ## Automatic node labelling {#node-labeller}
 -->
@@ -280,7 +161,6 @@ At the moment, that controller can add labels for:
   * CZ - Carrizo
   * AI - Arctic Islands
   * RV - Raven
-Example result:
 --->
 * 设备 ID (-device-id)
 * VRAM 大小 (-vram)
@@ -296,26 +176,23 @@ Example result:
   * AI - Arctic Islands
   * RV - Raven
 
-示例:
-
 ```shell
 kubectl describe node cluster-node-23
 ```
 
 ```
-    Name:               cluster-node-23
-    Roles:              <none>
-    Labels:             beta.amd.com/gpu.cu-count.64=1
-                        beta.amd.com/gpu.device-id.6860=1
-                        beta.amd.com/gpu.family.AI=1
-                        beta.amd.com/gpu.simd-count.256=1
-                        beta.amd.com/gpu.vram.16G=1
-                        beta.kubernetes.io/arch=amd64
-                        beta.kubernetes.io/os=linux
-                        kubernetes.io/hostname=cluster-node-23
-    Annotations:        kubeadm.alpha.kubernetes.io/cri-socket: /var/run/dockershim.sock
-                        node.alpha.kubernetes.io/ttl: 0
-    …
+Name:               cluster-node-23
+Roles:              <none>
+Labels:             beta.amd.com/gpu.cu-count.64=1
+                    beta.amd.com/gpu.device-id.6860=1
+                    beta.amd.com/gpu.family.AI=1
+                    beta.amd.com/gpu.simd-count.256=1
+                    beta.amd.com/gpu.vram.16G=1
+                    kubernetes.io/arch=amd64
+                    kubernetes.io/os=linux
+                    kubernetes.io/hostname=cluster-node-23
+Annotations:        node.alpha.kubernetes.io/ttl: 0
+…
 ```
 
 <!--
@@ -337,12 +214,17 @@ spec:
       resources:
         limits:
           nvidia.com/gpu: 1
-  nodeSelector:
-    accelerator: nvidia-tesla-p100 # or nvidia-tesla-k80 etc.
+  affinity:
+    nodeAffinity:
+      requiredDuringSchedulingIgnoredDuringExecution:
+        nodeSelectorTerms:
+        – matchExpressions:
+          – key: beta.amd.com/gpu.family.AI # Arctic Islands GPU 系列
+            operator: Exist
 ```
 
 <!--
-This will ensure that the pod will be scheduled to a node that has the GPU type
+This ensures that the Pod will be scheduled to a node that has the GPU type
 you specified.
 -->
 这能够保证 Pod 能够被调度到你所指定类型的 GPU 的节点上去。