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new 9ee498b51d [YUNIKORN-1502] Chinese translation of Run NVIDIA GPU Jobs
in workload (#340)
9ee498b51d is described below
commit 9ee498b51d3caf4c1b8f7920124277316226e989
Author: rich7420 <[email protected]>
AuthorDate: Sat Oct 28 23:53:17 2023 +0800
[YUNIKORN-1502] Chinese translation of Run NVIDIA GPU Jobs in workload
(#340)
---
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1 file changed, 347 insertions(+)
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+---
+id: run_nvidia
+title: 运行NVIDIA GPU作业
+description: 如何使用Yunikorn运行通用的GPU调度示例
+keywords:
+ - NVIDIA GPU
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+## Yunikorn 与 NVIDIA GPUs
+本指南概述了如何设置NVIDIA设备插件，该插件使用户可以在Yunikorn上运行GPU。如需更详细信息，请查看
[**使用GPU的Kubernetes**](https://docs.nvidia.com/datacenter/cloud-native/kubernetes/install-k8s.html#option-2-installing-kubernetes-using-kubeadm)。
+
+### 先决条件
+在按照以下步骤之前，需要在
[**设置GPU的Kubernetes**](https://docs.nvidia.com/datacenter/cloud-native/kubernetes/install-k8s.html#install-kubernetes)上部署Yunikorn。
+
+### 安装NVIDIA设备插件
+添加nvidia-device-plugin Helm存储库。
+```
+helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
+helm repo update
+helm repo list
+```
+
+验证插件的最新发布版本是否可用。
+```
+helm search repo nvdp --devel
+NAME CHART VERSION APP VERSION DESCRIPTION
+nvdp/nvidia-device-plugin 0.12.3 0.12.3 A Helm chart
for ...
+```
+
+部署设备插件。
+```
+kubectl create namespace nvidia
+helm install --generate-name nvdp/nvidia-device-plugin --namespace nvidia
--version 0.12.3
+```
+
+检查Pod的状态以确保NVIDIA设备插件正在运行。
+```
+kubectl get pods -A
+
+NAMESPACE NAME READY STATUS
RESTARTS AGE
+kube-flannel kube-flannel-ds-j24fx 1/1 Running 1
(11h ago) 11h
+kube-system coredns-78fcd69978-2x9l8 1/1 Running 1
(11h ago) 11h
+kube-system coredns-78fcd69978-gszrw 1/1 Running 1
(11h ago) 11h
+kube-system etcd-katlantyss-nzxt 1/1 Running 3
(11h ago) 11h
+kube-system kube-apiserver-katlantyss-nzxt 1/1 Running 4
(11h ago) 11h
+kube-system kube-controller-manager-katlantyss-nzxt 1/1 Running 3
(11h ago) 11h
+kube-system kube-proxy-4wz7r 1/1 Running 1
(11h ago) 11h
+kube-system kube-scheduler-katlantyss-nzxt 1/1 Running 4
(11h ago) 11h
+kube-system nvidia-device-plugin-1659451060-c92sb 1/1 Running 1
(11h ago) 11h
+```
+
+### 测试NVIDIA设备插件
+创建一个GPU测试的YAML文件。
+```
+# gpu-pod.yaml
+ apiVersion: v1
+ kind: Pod
+ metadata:
+ name: gpu-operator-test
+ spec:
+ restartPolicy: OnFailure
+ containers:
+ - name: cuda-vector-add
+ image: "nvidia/samples:vectoradd-cuda10.2"
+ resources:
+ limits:
+ nvidia.com/gpu: 1
+```
+部署应用程序。
+```
+kubectl apply -f gpu-pod.yaml
+```
+检查日志以确保应用程序成功完成。
+```
+kubectl get pods gpu-operator-test
+
+NAME READY STATUS RESTARTS AGE
+gpu-operator-test 0/1 Completed 0 9d
+```
+检查结果。
+```
+kubectl logs gpu-operator-test
+
+[Vector addition of 50000 elements]
+Copy input data from the host memory to the CUDA device
+CUDA kernel launch with 196 blocks of 256 threads
+Copy output data from the CUDA device to the host memory
+Test PASSED
+Done
+```
+
+---
+## 启用GPU时间切片( 可选 )
+GPU时间分片允许多租户共享单个GPU。
+要了解GPU时间分片的工作原理，请参阅[**在Kubernetes中使用GPU的时间切片**](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/gpu-sharing.html#introduction)。此页面介绍了使用
[**NVIDIA GPU
Operator**](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/gpu-operator)启用Yunikorn中GPU调度的方法。
+
+
+### 配置
+以下示例说明在ConfigMap中指定多个配置。
+```yaml
+# time-slicing-config.yaml
+apiVersion: v1
+kind: ConfigMap
+metadata:
+ name: time-slicing-config
+ namespace: nvidia
+data:
+ a100-40gb: |-
+ version: v1
+ sharing:
+ timeSlicing:
+ resources:
+ - name: nvidia.com/gpu
+ replicas: 8
+ - name: nvidia.com/mig-1g.5gb
+ replicas: 2
+ - name: nvidia.com/mig-2g.10gb
+ replicas: 2
+ - name: nvidia.com/mig-3g.20gb
+ replicas: 3
+ - name: nvidia.com/mig-7g.40gb
+ replicas: 7
+ rtx-3070: |-
+ version: v1
+ sharing:
+ timeSlicing:
+ resources:
+ - name: nvidia.com/gpu
+ replicas: 8
+```
+
+:::note
+如果节点上的GPU类型不包括a100-40gb或rtx-3070，您可以根据现有的GPU类型修改YAML文件。例如，在本地Kubernetes集群中只有多个rtx-2080ti，而rtx-2080ti不支持MIG，因此无法替代a100-40gb。但rtx-2080ti支持时间切片，因此可以替代rtx-3070。
+:::
+
+:::info
+MIG支持于2020年添加到Kubernetes中。有关其工作原理的详细信息，请参阅
[**在Kubernetes中支援MIG**](https://www.google.com/url?q=https://docs.google.com/document/d/1mdgMQ8g7WmaI_XVVRrCvHPFPOMCm5LQD5JefgAh6N8g/edit&sa=D&source=editors&ust=1655578433019961&usg=AOvVaw1F-OezvM-Svwr1lLsdQmu3)
。
+:::
+
+在operator命名空间nvidia，创建一个ConfigMap。
+```bash
+kubectl create namespace nvidia
+kubectl create -f time-slicing-config.yaml
+```
+
+### 安装NVIDIA GPU Operator
+添加nvidia-gpu-operator Helm存储库。
+```bash
+helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
+helm repo update
+helm repo list
+```
+
+
+使用NVIDIA GPU Operator启用共享GPU。
+- 在启用时间切片的情况下首次安装NVIDIA GPU Operator。
+ ```bash
+ helm install gpu-operator nvidia/gpu-operator \
+ -n nvidia \
+ --set devicePlugin.config.name=time-slicing-config
+ ```
+
+- 对于已安装GPU Operator的情况下，动态启用时间切片。
+ ```bash
+ kubectl patch clusterpolicy/cluster-policy \
+ -n nvidia --type merge \
+ -p '{"spec": {"devicePlugin": {"config": {"name": "time-slicing-config"}}}}'
+ ```
+
+### 应用时间分片配置
+有两种方法：
+- 集群范围
+
+通过传递时间分片ConfigMap名称和默认配置来安装GPU Operator。
+ ```bash
+ kubectl patch clusterpolicy/cluster-policy \
+ -n nvidia --type merge \
+ -p '{"spec": {"devicePlugin": {"config": {"name": "time-slicing-config",
"default": "rtx-3070"}}}}'
+ ```
+
+- 在特定节点上
+
+使用ConfigMap中所需的时间切片配置对节点进行标记。
+ ```bash
+ kubectl label node <node-name> nvidia.com/device-plugin.config=rtx-3070
+ ```
+
+
+一旦安装了GPU Operator和时间切片GPU，检查Pod的状态以确保所有容器都在运行，并且验证已完成。
+```bash
+kubectl get pods -n nvidia
+```
+
+```bash
+NAME READY STATUS
RESTARTS AGE
+gpu-feature-discovery-qbslx 2/2 Running
0 20h
+gpu-operator-7bdd8bf555-7clgv 1/1 Running
0 20h
+gpu-operator-node-feature-discovery-master-59b4b67f4f-q84zn 1/1 Running
0 20h
+gpu-operator-node-feature-discovery-worker-n58dv 1/1 Running
0 20h
+nvidia-container-toolkit-daemonset-8gv44 1/1 Running
0 20h
+nvidia-cuda-validator-tstpk 0/1
Completed 0 20h
+nvidia-dcgm-exporter-pgk7v 1/1 Running
1 20h
+nvidia-device-plugin-daemonset-w8hh4 2/2 Running
0 20h
+nvidia-device-plugin-validator-qrpxx 0/1
Completed 0 20h
+nvidia-operator-validator-htp6b 1/1 Running
0 20h
+```
+验证时间分片配置是否成功应用。
+```bash
+kubectl describe node <node-name>
+```
+
+```bash
+...
+Capacity:
+ nvidia.com/gpu: 8
+...
+Allocatable:
+ nvidia.com/gpu: 8
+...
+```
+
+### 测试GPU时间切片
+创建一个工作负载测试文件 plugin-test.yaml。
+```yaml
+# plugin-test.yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+ name: nvidia-plugin-test
+ labels:
+ app: nvidia-plugin-test
+spec:
+ replicas: 5
+ selector:
+ matchLabels:
+ app: nvidia-plugin-test
+ template:
+ metadata:
+ labels:
+ app: nvidia-plugin-test
+ spec:
+ tolerations:
+ - key: nvidia.com/gpu
+ operator: Exists
+ effect: NoSchedule
+ containers:
+ - name: dcgmproftester11
+ image: nvidia/samples:dcgmproftester-2.1.7-cuda11.2.2-ubuntu20.04
+ command: ["/bin/sh", "-c"]
+ args:
+ - while true; do /usr/bin/dcgmproftester11 --no-dcgm-validation -t
1004 -d 300; sleep 30; done
+ resources:
+ limits:
+ nvidia.com/gpu: 1
+ securityContext:
+ capabilities:
+ add: ["SYS_ADMIN"]
+```
+
+
+创建一个具有多个副本的部署。
+```bash
+kubectl apply -f plugin-test.yaml
+```
+
+验证所有五个副本是否正在运行。
+
+- 在Pod群中
+ ```bash
+ kubectl get pods
+ ```
+
+ ```bash
+ NAME READY STATUS RESTARTS AGE
+ nvidia-plugin-test-677775d6c5-bpsvn 1/1 Running 0 8m8s
+ nvidia-plugin-test-677775d6c5-m95zm 1/1 Running 0 8m8s
+ nvidia-plugin-test-677775d6c5-9kgzg 1/1 Running 0 8m8s
+ nvidia-plugin-test-677775d6c5-lrl2c 1/1 Running 0 8m8s
+ nvidia-plugin-test-677775d6c5-9r2pz 1/1 Running 0 8m8s
+ ```
+- 在节点中
+ ```bash
+ kubectl describe node <node-name>
+ ```
+
+ ```bash
+ ...
+ Allocated resources:
+ (Total limits may be over 100 percent, i.e., overcommitted.)
+ Resource Requests Limits
+ -------- -------- ------
+ ...
+ nvidia.com/gpu 5 5
+ ...
+ ```
+- 在NVIDIA系统管理界面中
+ ```bash
+ nvidia-smi
+ ```
+
+ ```bash
+
+-----------------------------------------------------------------------------+
+ | NVIDIA-SMI 520.61.05 Driver Version: 520.61.05 CUDA Version: 11.8
|
+
|-------------------------------+----------------------+----------------------+
+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr.
ECC |
+ | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute
M. |
+ | | | MIG
M. |
+
|===============================+======================+======================|
+ | 0 NVIDIA GeForce ... On | 00000000:01:00.0 On |
N/A |
+ | 46% 86C P2 214W / 220W | 4297MiB / 8192MiB | 100%
Default |
+ | | |
N/A |
+
+-------------------------------+----------------------+----------------------+
+
+
+-----------------------------------------------------------------------------+
+ | Processes:
|
+ | GPU GI CI PID Type Process name GPU
Memory |
+ | ID ID Usage
|
+
|=============================================================================|
+ | 0 N/A N/A 1776886 C /usr/bin/dcgmproftester11
764MiB |
+ | 0 N/A N/A 1776921 C /usr/bin/dcgmproftester11
764MiB |
+ | 0 N/A N/A 1776937 C /usr/bin/dcgmproftester11
764MiB |
+ | 0 N/A N/A 1777068 C /usr/bin/dcgmproftester11
764MiB |
+ | 0 N/A N/A 1777079 C /usr/bin/dcgmproftester11
764MiB |
+
+-----------------------------------------------------------------------------+
+ ```
+
+- 在Yunikorn用户界面中的应用程序中。
+
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