fix some layout
yanrongshi
parent
104f34049d
commit
b174bfed02
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@ -1,6 +1,6 @@
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---
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content_type: concept
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title: 调度 GPUs
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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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@ -76,7 +76,7 @@ when using GPUs:
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- Each container can request one or more GPUs. It is not possible to request a
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fraction of a GPU.
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-->
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- GPUs 只能设置在 `limits` 部分，这意味着：
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- GPU 只能设置在 `limits` 部分，这意味着：
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* 你可以指定 GPU 的 `limits` 而不指定其 `requests`，Kubernetes 将使用限制
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值作为默认的请求值；
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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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@ -87,6 +87,8 @@ when using GPUs:
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<!--
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Here's an example:
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-->
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这里是一个例子：
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```yaml
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apiVersion: v1
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kind: Pod
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@ -111,27 +113,20 @@ has the following requirements:
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-->
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### 部署 AMD GPU 设备插件 {#deploying-amd-gpu-device-plugin}
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[官方的 AMD GPU 设备插件](https://github.com/RadeonOpenCompute/k8s-device-plugin) 有以下要求：
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[官方的 AMD GPU 设备插件](https://github.com/RadeonOpenCompute/k8s-device-plugin)有以下要求：
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<!--
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- Kubernetes nodes have to be pre-installed with AMD GPU Linux driver.
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To deploy the AMD device plugin once your cluster is running and the above
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requirements are satisfied:
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```
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# For Kubernetes v1.9
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kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.9/k8s-ds-amdgpu-dp.yaml
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# For Kubernetes v1.10
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kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.10/k8s-ds-amdgpu-dp.yaml
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```
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-->
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- Kubernetes 节点必须预先安装 AMD GPU 的 Linux 驱动。
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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：
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```shell
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kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.10/k8s-ds-amdgpu-dp.yaml
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kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/v1.10/k8s-ds-amdgpu-dp.yaml
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```
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<!--
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@ -148,7 +143,7 @@ There are currently two device plugin implementations for NVIDIA GPUs:
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-->
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### 部署 NVIDIA GPU 设备插件 {#deploying-nvidia-gpu-device-plugin}
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对于 NVIDIA GPUs，目前存在两种设备插件的实现：
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对于 NVIDIA GPU，目前存在两种设备插件的实现：
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<!--
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#### Official NVIDIA GPU device plugin
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@ -163,24 +158,32 @@ has the following requirements:
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<!--
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- Kubernetes nodes have to be pre-installed with NVIDIA drivers.
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- Kubernetes nodes have to be pre-installed with [nvidia-docker 2.0](https://github.com/NVIDIA/nvidia-docker)
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- nvidia-container-runtime must be configured as the [default runtime](https://github.com/NVIDIA/k8s-device-plugin#preparing-your-gpu-nodes)
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for docker instead of runc.
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- NVIDIA drivers ~= 361.93
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- Kubelet must use Docker as its container runtime
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- `nvidia-container-runtime` must be configured as the [default runtime](https://github.com/NVIDIA/k8s-device-plugin#preparing-your-gpu-nodes)
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for Docker, instead of runc.
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- The version of the NVIDIA drivers must match the constraint ~= 384.81.
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To deploy the NVIDIA device plugin once your cluster is running and the above
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requirements are satisfied:
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-->
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- Kubernetes 的节点必须预先安装了 NVIDIA 驱动
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- Kubernetes 的节点必须预先安装 [nvidia-docker 2.0](https://github.com/NVIDIA/nvidia-docker)
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- 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
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- NVIDIA 驱动版本 ~= 384.81
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- Kubelet 的容器运行时必须使用 Docker
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- Docker 的[默认运行时](https://github.com/NVIDIA/k8s-device-plugin#preparing-your-gpu-nodes)必须设置为
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`nvidia-container-runtime`，而不是 `runc`。
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- NVIDIA 驱动程序的版本必须匹配 ~= 361.93
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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：
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```shell
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kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml
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```
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请到 [NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin)项目报告有关此设备插件的问题。
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<!--
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You can report issues with this third-party device plugin by logging an issue in
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[NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin).
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-->
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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。
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<!--
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#### NVIDIA GPU device plugin used by GCE
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@ -195,6 +198,9 @@ and has experimental code for Ubuntu from 1.9 onwards.
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[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。
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<!--
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You can use the following commands to install the NVIDIA drivers and device plugin:
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-->
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你可以使用下面的命令来安装 NVIDIA 驱动以及设备插件：
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```
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@ -209,13 +215,15 @@ kubectl create -f https://raw.githubusercontent.com/kubernetes/kubernetes/releas
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```
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<!--
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Report issues with this device plugin and installation method to [GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators).
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You can report issues with using or deploying this third-party device plugin by logging an issue in
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[GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators).
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Google publishes its own [instructions](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus) for using NVIDIA GPUs on GKE .
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-->
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请到 [GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators) 报告有关此设备插件以及安装方法的问题。
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你可以通过在 [GoogleCloudPlatform/container-engine-accelerators](https://github.com/GoogleCloudPlatform/container-engine-accelerators)
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中记录问题来报告使用或部署此第三方设备插件的问题。
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关于如何在 GKE 上使用 NVIDIA GPUs，Google 也提供自己的[指令](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus)。
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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)。
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<!--
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## Clusters containing different types of GPUs
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@ -249,14 +257,14 @@ kubectl label nodes <node-with-p100> accelerator=nvidia-tesla-p100
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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 properties.
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labels your nodes with GPU device properties.
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At the moment, that controller can add labels for:
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-->
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如果你在使用 AMD GPUs，你可以部署
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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 属性标签。目前支持的属性：
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会自动给节点打上 GPU 设备属性标签。目前支持的属性：
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<!--
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* Device ID (-device-id)
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@ -307,7 +315,7 @@ kubectl describe node cluster-node-23
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kubernetes.io/hostname=cluster-node-23
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Annotations: kubeadm.alpha.kubernetes.io/cri-socket: /var/run/dockershim.sock
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node.alpha.kubernetes.io/ttl: 0
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......
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…
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```
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<!--
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