对于Windows节点的工作负载,GPU相比于CPU可提供更大规模的并行计算能力,且能够将操作速度提高几个数量级,从而降低成本并提高吞吐量。Windows容器支持对基于DirectX构建的所有框架进行GPU加速。本文介绍在Windows节点如何安装DirectX设备插件以及在Windows容器中如何使用基于DirectX构建的GPU加速功能。
背景信息
DirectX(Direct eXtension,简称DX)是一种应用程序接口(API)。DirectX可以使以Windows为平台的游戏和多媒体程序获得更高的执行效率,加强3D图形和声音效果,并向设计人员提供一个共同的硬件驱动标准,降低安装及设置硬件的复杂度。DirectX可以允许GPU从事更多的通用计算工作,同时减轻过载,鼓励开发人员更好地将GPU作为并行处理器使用。
步骤一:为Windows节点安装DirectX设备插件
将DirectX设备插件以DaemonSet方式部署到Windows节点上。
- 使用以下内容创建directx-device-plugin-windows.yaml文件。
apiVersion: apps/v1
kind: DaemonSet
metadata:
labels:
k8s-app: directx-device-plugin-windows
name: directx-device-plugin-windows
namespace: kube-system
spec:
revisionHistoryLimit: 10
selector:
matchLabels:
k8s-app: directx-device-plugin-windows
template:
metadata:
annotations:
scheduler.alpha.kubernetes.io/critical-pod: ""
labels:
k8s-app: directx-device-plugin-windows
spec:
tolerations:
- operator: Exists
# since 1.18, we can specify "hostNetwork: true" for Windows workloads, so we can deploy an application without NetworkReady.
hostNetwork: true
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: type
operator: NotIn
values:
- virtual-kubelet
- key: beta.kubernetes.io/os
operator: In
values:
- windows
- key: windows.alibabacloud.com/deployment-topology
operator: In
values:
- "2.0"
- key: windows.alibabacloud.com/directx-supported
operator: In
values:
- "true"
- matchExpressions:
- key: type
operator: NotIn
values:
- virtual-kubelet
- key: kubernetes.io/os
operator: In
values:
- windows
- key: windows.alibabacloud.com/deployment-topology
operator: In
values:
- "2.0"
- key: windows.alibabacloud.com/directx-supported
operator: In
values:
- "true"
containers:
- name: directx
command:
- pwsh.exe
- -NoLogo
- -NonInteractive
- -File
- entrypoint.ps1
# 根据不同集群的地域,您需修改以下镜像地址中的地域<cn-hangzhou>信息。
image: registry-vpc.cn-hangzhou.aliyuncs.com/acs/directx-device-plugin-windows:v1.0.0
imagePullPolicy: IfNotPresent
volumeMounts:
- name: host-binary
mountPath: c:/host/opt/bin
- name: wins-pipe
mountPath: \\.\pipe\rancher_wins
volumes:
- name: host-binary
hostPath:
path: c:/opt/bin
type: DirectoryOrCreate
- name: wins-pipe
hostPath:
path: \\.\pipe\rancher_wins
- 执行以下命令安装DirectX设备插件。
kubectl create -f directx-device-plugin-windows.yaml
步骤二:部署使用基于DirectX的GPU加速的Windows工作负载
DirectX设备插件可以为Windows容器自动添加
class/<interface class GUID>
设备,以支持调用ECS实例主机的DirectX服务。更多信息,请参见
Windows上的容器中的设备。在需要使用GPU加速的Windows工作负载内添加以下
resources资源信息并重新部署:
spec:
...
template:
...
spec:
...
containers:
- name: gpu-user
...
+ resources:
+ limits:
+ windows.alibabacloud.com/directx: "1"
+ requests:
+ windows.alibabacloud.com/directx: "1"
注意 上述配置不会将整个ECS实例主机的GPU资源专门分配给容器,也不会阻止ECS实例主机上的GPU被别的应用访问。相反,GPU资源在ECS实例主机和容器之间动态调度,这表示您可以在主机上运行多个Windows容器,且每个容器都可以使用支持硬件加速的DirectX功能。
关于Windows容器中的GPU加速的更多信息,请参见Windows容器中的GPU加速。
验证在Windows工作负载是否成功使用GPU加速功能
在Windows节点上添加DirectX设备插件后,使用以下示例应用验证DirectX设备插件是否成功部署到Windows节点上。
- 使用以下内容创建gpu-job-windows.yaml文件。
apiVersion: batch/v1
kind: Job
metadata:
labels:
k8s-app: gpu-job-windows
name: gpu-job-windows
namespace: default
spec:
parallelism: 1
completions: 1
backoffLimit: 3
manualSelector: true
selector:
matchLabels:
k8s-app: gpu-job-windows
template:
metadata:
labels:
k8s-app: gpu-job-windows
spec:
restartPolicy: Never
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: type
operator: NotIn
values:
- virtual-kubelet
- key: beta.kubernetes.io/os
operator: In
values:
- windows
- matchExpressions:
- key: type
operator: NotIn
values:
- virtual-kubelet
- key: kubernetes.io/os
operator: In
values:
- windows
tolerations:
- key: os
value: windows
containers:
- name: gpu
# 根据不同集群的地域,您需修改以下镜像地址中的地域<cn-hangzhou>信息。
image: registry-vpc.cn-hangzhou.aliyuncs.com/acs/sample-gpu-windows:v1.0.0
imagePullPolicy: IfNotPresent
resources:
limits:
windows.alibabacloud.com/directx: "1"
requests:
windows.alibabacloud.com/directx: "1"
说明
- 镜像
registry-vpc.{region}.aliyuncs.com/acs/sample-gpu-windows
是阿里云容器服务提供的Windows GPU加速容器示例镜像(该镜像基于microsoft-windows制作。更多信息,请参见mcr.microsoft.com/windows)。
- 因镜像文件较大(文件大小为15.3 GB),所以在部署应用时拉取镜像需要较长时间,请耐心等待。
- 该示例通过WinMLRunner产生模拟输入数据,对
gpu-job-windows
任务使用GPU加速后,通过Tiny YOLOv2模型进行100次评估,最终输出相应的性能测量数据。
- 执行以下命令创建示例应用。
kubectl create -f gpu-job-windows.yaml
- 执行以下命令查看示例应用gpu-job-windows的日志信息。
kubectl logs -f gpu-job-windows
预期输出:
INFO: Executing model of "tinyyolov2-7" 100 times within GPU driver ...
Created LearningModelDevice with GPU: NVIDIA GRID T4-8Q
Loading model (path = c:\data\tinyyolov2-7\model.onnx)...
=================================================================
Name: Example Model
Author: OnnxMLTools
Version: 0
Domain: onnxconverter-common
Description: The Tiny YOLO network from the paper 'YOLO9000: Better, Faster, Stronger' (2016), arXiv:1612.08242
Path: c:\data\tinyyolov2-7\model.onnx
Support FP16: false
Input Feature Info:
Name: image
Feature Kind: Image (Height: 416, Width: 416)
Output Feature Info:
Name: grid
Feature Kind: Float
从预期输出可得,在示例应用gpu-job-windows中已成功使用GPU加速功能。