152 lines
5.7 KiB
Markdown
152 lines
5.7 KiB
Markdown
---
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content_type: concept
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title: 调度 GPU
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description: 配置和调度 GPU 成一类资源以供集群中节点使用。
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---
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<!--
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reviewers:
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- vishh
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content_type: concept
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title: Schedule GPUs
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description: Configure and schedule GPUs for use as a resource by nodes in a cluster.
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-->
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<!-- overview -->
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{{< feature-state state="stable" for_k8s_version="v1.26" >}}
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<!--
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Kubernetes includes **stable** support for managing AMD and NVIDIA GPUs
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(graphical processing units) across different nodes in your cluster, using
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{{< glossary_tooltip text="device plugins" term_id="device-plugin" >}}.
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This page describes how users can consume GPUs, and outlines
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some of the limitations in the implementation.
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-->
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Kubernetes 支持使用{{< glossary_tooltip text="设备插件" term_id="device-plugin" >}}来跨集群中的不同节点管理
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AMD 和 NVIDIA GPU（图形处理单元），目前处于**稳定**状态。
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本页介绍用户如何使用 GPU 以及当前存在的一些限制。
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<!-- body -->
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<!--
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## Using device plugins
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Kubernetes implements device plugins to let Pods access specialized hardware features such as GPUs.
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-->
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## 使用设备插件 {#using-device-plugins}
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Kubernetes 实现了设备插件（Device Plugin），让 Pod 可以访问类似 GPU 这类特殊的硬件功能特性。
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{{% thirdparty-content %}}
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<!--
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As an administrator, you have to install GPU drivers from the corresponding
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hardware vendor on the nodes and run the corresponding device plugin from the
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GPU vendor. Here are some links to vendors' instructions:
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-->
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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
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GPU 厂商的对应设备插件。以下是一些厂商说明的链接：
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* [AMD](https://github.com/RadeonOpenCompute/k8s-device-plugin#deployment)
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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/k8s-device-plugin#quick-start)
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<!--
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Once you have installed the plugin, your cluster exposes a custom schedulable
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resource such as `amd.com/gpu` or `nvidia.com/gpu`.
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You can consume these GPUs from your containers by requesting
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the custom GPU resource, the same way you request `cpu` or `memory`.
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However, there are some limitations in how you specify the resource
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requirements for custom devices.
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-->
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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`。
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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。
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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。
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<!--
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GPUs are only supposed to be specified in the `limits` section, which means:
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* You can specify GPU `limits` without specifying `requests`, because
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Kubernetes will use the limit as the request value by default.
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* You can specify GPU in both `limits` and `requests` but these two values
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must be equal.
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* You cannot specify GPU `requests` without specifying `limits`.
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-->
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GPU 只能在 `limits` 部分指定，这意味着：
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* 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。
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* 你可以同时指定 `limits` 和 `requests`，不过这两个值必须相等。
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* 你不可以仅指定 `requests` 而不指定 `limits`。
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<!--
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Here's an example manifest for a Pod that requests a GPU:
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-->
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以下是一个 Pod 请求 GPU 的示例清单：
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```yaml
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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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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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```
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<!--
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## Clusters containing 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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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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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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Pod 调度到合适的节点上。
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例如：
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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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kubectl label nodes node2 accelerator=other-gpu-k915
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```
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<!--
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That label key `accelerator` is just an example; you can use
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a different label key if you prefer.
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-->
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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。
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<!--
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## Automatic node labelling {#node-labeller}
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-->
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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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-->
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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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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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