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力扣 (LeetCode) 全球极客挚爱的技术成长平台

LeetCode) 全球极客挚爱的技术成长平台学习题库竞赛讨论求职商店 推荐算法数据结构题目交流职业发展竞赛前端后端THU今FRI08SAT09SUN10MON11TUE12WED13每日 1 题零点揭晓关于我们·企业服务· 商务咨询·使用条款·隐私政策问题反馈·侵权投诉·证照中心沪ICP备18019787号-20·沪ICP证B2-20180578沪公网安备31010702007420号下载 App©2023 领扣网络(上海)有

产品简介 — LiCO Document 5.1.0 documentation

产品简介 — LiCO Document 5.1.0 documentation

5.1.0

前言

产品简介

运行环境

安装手册

前提和假设

特别说明

部署集群环境

安装操作系统

为管理节点安装OS

部署集群其它节点OS

配置环境变量

获取本地源

安装Lenovo xcat

为其他节点准备OS系统

设置xcat节点信息

添加hosts解析

配置DHCP及DNS服务

通过网络为节点安装操作系统

检查点A

安装集群基础软件

基础软件列表

为管理节点设置本地源

为计算及登录节点配置本地源

配置LiCO依赖源

安装slurm

配置nfs

配置ntp

安装cuda和cudnn

配置slurm

安装ganglia

安装mpi

安装singularity

检查点B

安装其它组件

组件列表

安裝rabbitmq

安裝postgresql

安装influxdb

安装confluent

配置用户认证

安装openldap-server

安装libuser

安装openldap-client

安装nss-pam-ldapd

安装Gmond GPU插件

安装LiCO

组件列表

获取安装包

配置本地源

安装节点

安装管理节点

安装登陆节点

安装计算节点

配置LiCO

初始化系统

初始化用户

导入系统镜像

启动

配置手册

配置服务账户

配置集群节点

机房信息

逻辑组信息

机房行信息

机架信息

刀箱信息

节点信息

配置LiCO服务

基础配置

数据库配置

登陆配置

存储配置

调度器配置

告警配置

集群配置

功能配置

配置LiCO组件

lico-vnc-mond

lico-env

lico-portal

lico-ganglia-mond

lico-confluent-proxy

lico-confluent-mond

lico-wechat-agent

HOWTOs

如何快速安装LiCO

如何安装Infiniband驱动

安装IB网卡

安装OPA网卡

如何配置vnc

如何配置confluent

如何手动创建Influxdb数据库

如何解决slurm常见问题

如何升级操作系统

附录

推荐配置

ganglia

管理节点

其他节点

slurm

slurm.conf

gres.conf

常用命令

设置ldap管理员密码

修改用户角色

恢复用户

导入用户

安全改进

绑定设置

防火墙设置

创建和导入系统镜像

创建镜像

导入系统镜像

集群服务汇总

物理视图列表

刀箱型号列表

系统产品列表

升级LiCO

升级步骤

卸载lico-core

删除配置文件

升级集群基础软件

升级LiCO组件

配置LiCO服务

初始化LiCO和用户

启动LiCO

LiCO Document

»

产品简介

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产品简介

联想智能超算平台( Lenovo Intelligent Compute Orchestration, 下称 LiCO )

是 HPC/AI 基础管理软件,其功能包括计算机集群管理,集群监控,作业调度管理,集群用户管理,

账户管理,文件系统管理等。通过联想智能超算平台可以实现在超算集群中,统一资源调度,

同时支持 HPC 作业和 AI 作业运行。

本管理软件支持通过浏览器轻松登陆到管理界面进行操作,同时也可以通过其他终端工具登陆到集群登录节点进行命令行操作。

运行环境

服务器

操作系统

RedHat 7.4 / CentOS 7.4

SLES 12 SP3

客户端

浏览器:推荐 Chrome >= 62.0 或 Firefox >= 56.0

显示分辨率:推荐 1280*800

Previous

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© Copyright 2018, Lenovo.

在LiCO中实现模型训练_lico专家模式-CSDN博客

>

在LiCO中实现模型训练_lico专家模式-CSDN博客

在LiCO中实现模型训练

最新推荐文章于 2023-08-24 22:09:18 发布

DTTRA

最新推荐文章于 2023-08-24 22:09:18 发布

阅读量1.1k

收藏

14

点赞数

3

文章标签:

docker

深度学习

tensorflow

singularity

云服务器

版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。

本文链接:https://blog.csdn.net/DTTRA/article/details/118761610

版权

        为了获得更加稳健的深度学习训练模型,一些庞大的训练在个人电脑上无法实现,本文主要以在LiCO中实现tensorflow训练模型为例,为读者提供参考,若文档中存在错误,请读者提出宝贵的意见。

文章目录

一、在ubuntu中下载docker  二、利用docker下载镜像 三、构造自己的容器 四、修改容器名,生成镜像 五、发布自定义镜像 六、在LiCO中下载镜像 七、实现深度学习模型训练

提示:以下是本篇文章正文内容,下面案例可供参考

一、在ubuntu中下载docker :https://blog.csdn.net/qq_40663357/article/details/83307338

一、下载

1、 安装docker的apt源

apt-get install apt-transport-https ca-certificates curl software-properties-common

2 、添加docker官方的GPG

curl -s https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

3 、添加docker的源。如果没有docker.list则自己创建一个

cd /etc/apt/sources.list.d

vim docker.list

清空原文件,加入以下内容

deb https://get.docker.io/ubuntu docker main

二、安装

1、 安装docker

apt install docker.io

2、 查看docker的版本:

docker version

 

二、利用docker下载镜像

1.查看当前docker镜像

sudo docker images

显示所有存在的镜像

 2.下载镜像

进入Docker Hub网页:https://hub.docker.com/

 在搜索框中输出自己需要的基础镜像,例如:tensorflow

点击右下角Docker Pull Command中命令的复制,将其复制到ubuntu下的命令行中;也可以通过点击Tag在其中寻找自己想要的不同版本。

下载完成后查看当前存在的镜像信息

docker images

 此时发现tensorflow/tensorflow:latest镜像就下载好了

三、构造自己的容器

 1.进入tensorflow/tensorflow镜像

docker run -it tensorflow/tensorflow:latest /bin/bash

 注:记住5e7ae5f8288d这个容器名称

2.在原始容器中添加自己的模块,例如:在容器中安装opencv-python

pip3 install opencv-python

 此时5e7ae5f8288d容器中就加入了我们需要的模块

四、修改容器名,生成镜像

1.显示所有正在运行的容器

docker ps -a

 可以发现 5e7ae5f8288d容器就是自定义模块的tensorflow/tensorflow容器

2.要将自定义的容器保存为镜像

docker commit 5e7ae5f8288d 1160966815/tensorflow1:latest

其中5e7ae5f8288d为容器名,1160966815/tensorflow1:latest为自定义的名称

注:自定义的名称要按照Docker  Hub自己的账号名称对应,我的账号名称是1160966815,因此设置为1160966815/tensorflow1:latest(之后会讲到为什么这样操作)。

五、发布自定义镜像:https://www.cnblogs.com/fanqisoft/p/11315392.html

⒈在Docker Hub(Docker官方镜像仓库)注册自己的用户名。

⒉在Docker中使用docker login命令登录自己的用户名密码。

 (首次登陆需要自己的用户名密码)

⒊使用docker push 镜像名称 上传本地镜像到镜像仓库

docker push 镜像名称

例如:

docker push 1160966815/tensorflow1

 此时在Docker Hub中就能够找到自己发布的镜像了:

六、在LiCO中下载镜像

LiCO服务器登陆网址

登陆LiCO之后进入专家模式:

 在专家模式命令行中下载之前发布的tensorflow镜像:

singularity build tensorflow.sif docker://1160966815/tensorflow1

 (其中tensorflow.sif为自命名文件)

此时镜像已下载好,可以在文件管理中查看

  tensorflow.sif文件即为刚下载的镜像文件

七、实现深度学习模型训练

1.上传训练文件

进入文件管理,点击右键,Upload files进行本地文件上传。

 2.开始训练

进入提交作业,选择General-Common Job——点击使用——自定义作业名称——选择自己的工作目录——填写运行脚本

 运行脚本:(第一个路径为镜像所在的路径,第二个路径为自己的运行代码所在的路径,python代表的意思是:运行的文件为py文件)

根据自己的需求选择资源选项,点击提交,即可开始训练。

参考链接

        在ubuntu中下载docker :https://blog.csdn.net/qq_40663357/article/details/83307338         进入Docker Hub网页:https://hub.docker.com/         发布自定义镜像:https://www.cnblogs.com/fanqisoft/p/11315392.html         

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在LiCO中实现模型训练

为了获得更加稳健的深度学习训练模型,一些庞大的训练在个人电脑上无法实现,本文主要以在LiCO中实现tensorflow2.0-gpu训练模型为例,为读者提供参考,若文档存在问题,请读者提出宝贵的意见。提示:文章写完后,目录可以自动生成,如何生成可参考右边的帮助文档文章目录 一、在ubuntu中下载docker 二、利用docker下载镜像 三、构造自己的容器 四、修改容器名,生成镜像 五、发布自定义镜像 六、在LiCO中下载镜像 七、singul...

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Linux Counter专案

这是Linux Counter Project的bash集合的存储库。 您可以在此处找到Linux Counter Blog上Bash Collection类别中提到的所有bash脚本: :

它们如下:

| 用于从大文件中切割零件的脚本

| 脚本来获取应用程序实际使用的内存量

| 如何递归替换源文件的文件头

| 服务器运行状况监控器,用于快速获取服务器状态信息

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07-17

### 回答1:

联想lico5.5使用手册是一份详细的指南,旨在帮助用户熟悉该设备的功能和操作方法。手册提供了关于设备的基本信息,包括硬件规格、屏幕大小、处理器类型等。

手册首先介绍了设备的外观和各个部件的功能,如电源键、音量控制键等。然后,它详细解释了如何打开和关闭设备,以及如何连接到无线网络。此外,手册还提供了一些有关如何保护设备安全的提示,例如设置屏幕锁定密码和启用指纹识别功能。

手册的进一步内容涵盖了设备的各种功能和应用程序的使用方法。例如,它解释了如何使用设备上的摄像头拍照和录制视频,以及如何编辑和共享这些媒体文件。此外,手册还讲解了如何使用设备上的浏览器进行网页浏览,以及如何下载和安装应用程序。

手册还提供了有关设备设置和个性化选项的说明。例如,它解释了如何更改设备的壁纸和主题,以及如何调整各种设置,如通知和声音设置、语言和输入法设置等。

最后,手册还包含了一些故障排除和常见问题解答。它指导用户如何解决可能遇到的一些常见问题,如应用程序崩溃、无法连接到无线网络等。

总之,联想lico5.5使用手册为用户提供了全面的指导,帮助他们更好地了解和使用该设备。无论是初次使用还是已经熟悉该设备,用户都可以在手册中找到有关各种功能和操作的详细说明。

### 回答2:

联想 lico5,5 是一款智能手机,它提供了详细的使用手册来帮助用户更好地了解和操作手机。以下是有关联想 lico5,5使用手册的回答:

1. 开始使用:使用手册首先介绍了联想 lico5,5的外观和基本操作,包括如何打开手机、插入SIM卡和扩展存储卡,以及如何充电和启动手机。

2. 主要功能:手册介绍了联想 lico5,5的主要功能,包括拨打电话、发送短信、浏览互联网、拍照、录制视频等。手册详细介绍了每个功能的操作步骤和注意事项。

3. 设置和调整:手册提供了关于手机设置和调整的详细说明,包括如何调整屏幕亮度、音量和振动设置,如何连接Wi-Fi和蓝牙设备,以及如何设置锁屏密码和指纹识别等。

4. 应用程序和功能扩展:手册介绍了联想 lico5,5的内置应用程序,如短信、电话簿、相册等,并提供了如何下载和安装其他应用程序的说明。手册还介绍了如何使用手机的GPS导航功能和支付功能等。

5. 常见问题和故障排除:在手册的最后部分,提供了一些常见问题和故障排除的解决方法,如手机无法开机、无法连接互联网等。这些解决方案可以帮助用户快速解决一些常见问题。

总的来说,联想 lico5,5的使用手册提供了全面而详细的指导,帮助用户了解和操作手机的各种功能。它是用户使用这款手机时的重要参考资料,可以帮助用户充分发挥手机的功能,并解决遇到的问题。用户只需仔细阅读手册,按照手册提供的步骤进行操作,就能轻松地使用联想 lico5,5手机。

### 回答3:

联想Lico 5.5是一款智能手机,使用手册提供了用户在使用该手机时的详细指导和说明。下面是对该使用手册的回答。

联想Lico 5.5使用手册对手机的各个方面进行了全面介绍。首先,手册中包含了关于手机的基本信息,例如尺寸、重量、屏幕大小等,这有助于用户了解手机的外观和规格。此外,手册详细介绍了手机的主要功能,如打电话、发送短信、拍照等。对于新手用户来说,这些介绍非常有用,可以帮助他们快速了解和掌握手机的基本操作。

除了基本功能,手册还介绍了手机的高级功能和设置选项。例如,手册中可能会详细介绍如何使用手机上的指纹识别功能、如何设置手机上的各种通知和提醒、如何连接和使用蓝牙设备等。这对于想要更深入了解并充分利用手机功能的用户来说非常有帮助。

此外,手册可能还包含了一些实用技巧和贴士,以帮助用户更好地使用手机。它可以包括如何省电、如何管理手机中的应用程序、如何优化手机的性能等。这些技巧可以帮助用户更好地使用手机,提高其使用体验。

总之,联想Lico 5.5使用手册是用户了解、掌握和充分利用手机功能的重要工具。它提供了对手机各个方面的详细介绍和说明,能够满足用户对手机使用的各种需求。无论是新手还是有经验的用户,使用手册都能为他们提供便利和指导,帮助他们更好地使用联想Lico 5.5智能手机。

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一文了解加拿大最低收入标准 LICO - 知乎切换模式写文章登录/注册一文了解加拿大最低收入标准 LICO飞出国​已认证账号在加拿大移民过程中,很多项目都会需要申请人提供充足的资金证明,证明作为新移民有足够的能力独立在加拿大生活,或者作为担保人支撑亲人的生活。无法满足资金要求,也是作为inadmissible一项,被禁止进入加拿大的。什么项目需要提供资金证明?提供多少资金?而此时,LICO就是要参考的标准。什么是LICO?LICO全称是Low Income Cut-Off低收入门槛,是一个基于家庭人口数而定的收入数额,加拿大政府每年会根据通货膨胀更新其标准,低于标准的家庭会将更多收入用于衣、食、住这些必需品上。低于该标准,也可以说you are poor。申请人需要证明financial admissibility有充足资金独立定居加拿大,在加拿大移民申请中,移民官可能会综合申请人工作经验,教育,定居资金,技能等因素验证是否符合移民要求,定居资金则会根据LICO来评估是否充足。哪些常见项目需要满足LICO?在经济类移民项目中计算人口包括申请人本人,配偶或同居伴侣,及未成年子女。既包括随行成员,也包括不随行家庭成员。联邦技术移民类别Federal Skilled Worker Program;联邦技工类别 Federal Trade Worker Program;联邦创业移民SUV一般要求申请人登陆后有足够的资金在加拿大找工作等,通常需要申请人证明手头能有支持半年生活的资金,因此需要根据家庭规模大小,提供对应至少6个月的LICO。大西洋移民项目AIP则仅需要提供12.5%LICO的资金证明。加拿大经验类别CEC,和申请人持有加拿大valid job offer和工作签证的移民项目,已经证明了经济的独立能力,因此,不强制要求提供存款证明。在家庭移民类别中家庭人口计算需要包括A、B、C三大部分A 、担保方及家庭成员担保人;担保人配偶或同居伴侣(或处于分居状态);双方受抚养子女(22周岁以下未婚);(即便担保人没有监护权,或不提供子女抚养费,也必须将符合受抚养子女定义的子女计算在内,这样是为了防止在申请过程中可能的监护权变化影响最低收入要求。)B、被担保方及家庭成员被担保人;被担保人配偶或同居伴侣(即便分居,仍需要计算为人数,除非配偶同时与他人处于同居关系);双方受抚养子女(22周岁以下未婚);(即便家庭成员不随行移民加拿大,或者即便家庭成员是加拿大永久居民或公民,也需要计算在家庭人数中。)C、资金担保期内成员及其家庭成员担保人或者作为共同担保人曾经担保过还在资金担保期内的成员及其家庭成员。父母/祖父母团聚移民在父母/祖父母团聚移民中,作为担保人的子女需要证明在过去3年税前收入满足LICO+30%。受疫情影响,原本可以达到团聚条件的家庭因为疫情收入下降,移民局也贴心的调整了标准,对于2020年收入,可以按照LICO低收入门槛计算,而不必再上浮30%,而且政府发放的失业保险,CERB加拿大紧急福利金,以及其他疫情期间补助都可以算成收入的一部分。配偶或同居伴侣也可以作为共同担保人,这种情况担保资金要求可以是两人收入之和。收入参考标准可以参看NOA的Line 15000.Sponsorship requirements for parents and grandparents are designed to ensure that sponsors provide evidence of their ability to provide long-term financial support. For sponsorship applications received by IRCC on or after January 1, 2014, sponsors and co-signers must meet or exceed the MNI of the LICO, plus 30% for each of the 3 taxation years immediately preceding the date of the application for which the CRA issues an NOA or Option C printout. Undertakings for parents and grandparents remain in effect for 20 years after the sponsored parent or grandparent (and any accompanying dependants) becomes a permanent resident.父母/祖父母超级签证作为父母/祖父母团聚移民的替代方案,超级签证有效期10年,单次停留不超过5年,可延期2年。作为邀请人的子女,年收入需要满足12个月的LICO的要求。proof of financial support by the child or grandchild. This means that the child or grandchild who invites the applicant must prove that their household meets the low income cut-off (LICO) . The Canadian or permanent resident spouse or common-law partner of the host child or grandchild may co-sign the invitation letter to meet the LICO minimum. The following documents are examples of what the child or grandchild, including the co-signer, can use as proof of financial support:notice of assessment (NOA) for the most recent tax yearT4 or T1 for the most recent tax yearpay stubsEmployment Insurance benefit statement, including:a letter from an accountant confirming the child’s or grandchild’s annual income, if they are self-employed, and/orproof of other sources of income (for example, pension statement, investments)an original letter from the employer statingjob titlejob descriptionsalarybank statements其他家庭成员团聚移民除了担保配偶,同居伴侣和子女以外的家庭成员团聚,均需要提供12个月或以上的 LICO证明。发布于 2022-08-17 15:11加拿大收入​赞同​​添加评论​分享​喜欢​收藏​申请

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Lenovo Intelligent Computing Orchestration (LiCO)

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Table of Contents

Introduction Did You Know? What's new in LiCO 7.2 Part numbers Features for LiCO users Additional features for LiCO HPC/AI Users Features for LiCO Administrators

Subscription and Support Validated software components Validated hardware components Supported servers (LiCO HPC/AI version) LiCO Implementation services Client PC requirements Related links Related product families Trademarks

Abstract

Lenovo Intelligent Computing Orchestration (LiCO) is a software solution that simplifies the use of clustered computing resources for Artificial Intelligence (AI) model development and training, and HPC workloads.

This product guide provides essential presales information to understand LiCO and its key features, specifications and compatibility. This guide is intended for technical specialists, sales specialists, sales engineers, IT architects, and other IT professionals who want to learn more about LiCO and consider its use in HPC solutions.

Change History

Changes in the February 8, 2024 update:

Updated for LiCO 7.2:

Updated login images

Cloud Tools menu

Adding a template to a workflow in LiCO

HPC runtime module list

Updated all the features under - What's new in LiCO 7.2 section

Added new supported GPU under - Validated hardware components section

NVIDIA L40

Added new servers support under - Supported servers (LiCO HPC/AI version) section

Lenovo ThinkSystem SR860 V3, SR850 V3, SR590 V2

Lenovo WenTian WR5220 G3 (C4C type)

Lenovo ThinkStation P620 (without out-of-band monitoring)

Introduction

Lenovo Intelligent Computing Orchestration (LiCO) is a software solution that simplifies the use of clustered computing resources for Artificial Intelligence (AI) model development and training, and HPC workloads. LiCO interfaces with an open-source software orchestration stack, enabling the convergence of AI onto an HPC or Kubernetes-based cluster.

The unified platform simplifies interaction with the underlying compute resources, enabling customers to take advantage of popular open-source cluster tools while reducing the effort and complexity of using it for HPC and AI.

Figure 1. LiCO 7.2 login

Did You Know?

LiCO enables a single cluster to be used for multiple AI workloads simultaneously, with multiple users accessing the available cluster resources at the same time. Running more workloads can increase utilization of cluster resources, driving more user productivity and value from the environment.

What's new in LiCO 7.2

Lenovo recently announced LiCO Version 7.2, improving the functionality for both AI users, HPC users, and HPC administrators of LiCO, including:

Support OpenHPC v2.6.2

Support Nvidia L40

Support Lenovo ThinkSystem SR860 V3, SR850 V3, SR590 V2, Lenovo WenTian WR5220 G3

Support ThinkStation P620 Tower Workstation

Support JupyterLab, TensorBoard, LAMMPS

Support EasyBuild tool

Support Non-Lenovo Hardware in the cluster

Hybrid HPC supports Agnostic Cloud

Part numbers

The following table lists the ordering information for LiCO.

Note: Lenovo K8S AI LiCO Software updates are end of life (EOL) in June 2023. The last update is LiCO 6.4.

Table 1. LiCO HPC/AI version ordering information

Description

LFO

Software CTO

Feature code

Lenovo HPC AI LiCO Software 90 Day Evaluation License

7S090004WW

7S09CTO2WW

B1YC

Lenovo HPC AI LiCO Webportal w/1 yr S&S

7S09002BWW

7S09CTO6WW

S93A

Lenovo HPC AI LiCO Webportal w/3 yr S&S

7S09002CWW

7S09CTO6WW

S93B

Lenovo HPC AI LiCO Webportal w/5 yr S&S

7S09002DWW

7S09CTO6WW

S93C

Table 2. LiCO K8S/AI ordering information (Kubernetes)

Description

LFO

Software CTO

Feature code

Lenovo K8S AI LiCO Software Evaluation License (90 days)

7S090006WW

7S09CTO3WW

S21M

Lenovo K8S AI LiCO Software 4GPU w/1Yr S&S

7S090007WW

7S09CTO4WW

S21N

Lenovo K8S AI LiCO Software 4GPU w/3Yr S&S

7S090008WW

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Features for LiCO users

Topics in this section:

LiCO versions

Benefits to users

Features for users

Lenovo Accelerated AI

Cloud Tools

Workflow

Admin

LiCO versions

Note: There are two distinct versions of LiCO, LiCO HPC/AI (Host) and LiCO K8S/AI, to allow clients a choice for the which underlying orchestration stack is used, particularly when converging AI workloads onto an existing cluster. The user functionality is common across both versions, with minor environmental differences associated with the underlying orchestration being used.

A summary of the differences for user access is as follows:

LiCO K8S/AI version:

AI framework containers are docker-based and managed outside LiCO in the customer’s docker repository

Custom job submission templates are defined with YAML

Does not include HPC standard job submission templates

LiCO HPC/AI version:

AI framework containers are Singularity-based and managed inside the LiCO interface

Custom job submission templates are defined as batch scripts (for SLURM, LSF, PBS)

Includes HPC standard job submission templates

Benefits to users

LiCO provides users the following benefits:

A web-based portal to deploy, monitor and manage AI development and training jobs on a distributed cluster

Container-based deployment of supported AI frameworks for easy software stack configuration

Direct browser access to Jupyter Notebook instances running on the cluster

Standard and customized job templates to provide an intuitive starting point for less experienced users

Lenovo Accelerated AI pre-defined training and inference templates for many common AI use cases

Lenovo end-to-end workflow for Image Classification, Object Detection, Instance Segmentation, Image GAN, Text Classification, Seq2seq and Memory Network

Workflow to define multiple job submissions as an automated workflow to deploy in a single action

TensorBoard visualization tools integrated into the interface (TensorFlow-based)

Management of private space on shared storage through the GUI

Monitoring of job progress and log access

Features for users

Those designated as LiCO users have access to dashboards related primarily to HPC and AI development and training tasks. Users can submit jobs to the cluster, and monitor their results through the dashboards. The following menus are available to users:

Home menu for users – provides an overview of the resources available in the cluster. Jobs and job status are also given, indicating the runtime for the current job, and the order of jobs deployed. Users may click on jobs to access the associated logs and job files. The figure below displays the home menu.

Figure 2. User Home Menu

Job Templates – allows users to set up a job and submit it to the cluster. The user first picks a job template. After selecting the template, the user gives the job a name and inputs the relevant parameters, chooses the resources to be requested on the cluster and submits it.

Users can take advantage of Lenovo Accelerated AI templates, industry-standard AI templates, submit generic jobs via the Common Job template, as well as create their own templates requesting specified parameters.

Job Templates available in LICO:

Figure 3. HPC job templates available in LiCO

Figure 4. AI job templates available in LiCO

The figure below displays a job template for training with TensorFlow on a single node.

Figure 5. AI Job Template

LiCO also provides TensorBoard monitoring when running certain TensorFlow workloads, as shown in the following figure.

Figure 6. LiCO and TensorBoard monitoring

Jobs menu – displays a dashboard listing jobs and their statuses. In addition, users can select the job and see results and logs pertaining to the job in progress (or after completion). Tags and comments can be added to completed jobs for easier filtering.

Reports – displays a dashboard for obtaining reports on expenses. Expense Reports is supported currently, where the job and storage billing statistics are displayed.

Cloud Tools menu – enables users to create, run and view Jupyter Notebook and Jupyter Lab instances on the cluster from LiCO for model experimentation and development. Users will be able to lunch a CVAT labelling environment, Tiger VNC and the RStudio development environment. See the section for more information.

Lenovo Accelerated DL – provides users with the ability to label data, optimize hyperparameters, as well as test and publish trained models from within an end-to-end workflow in LiCO. LiCO supports Text Classification, Image Classification, Object Detection, and Instance Segmentation workflows. See the section for more information.

Workflow menu – allows users to create multi-step jobs that execute as a single action. Workflows can contain serially-executed steps as well as multiple jobs to execute in parallel within a step to take full advantage of cluster resources. See the section for more information.

Admin menu – allows users to access a number of capabilities not directly associated with deploying workloads to the cluster, including access to shared storage space on the cluster through a drag-and-drop interface and access to provision API and git interfaces. See the section for more information.

Lenovo Accelerated AI

Lenovo Accelerated AI provides a set of templates that aim to make AI training and inference simpler, more accessible, and faster to implement. The Accelerated AI templates differ from the other templates in LiCO in that they do not require the user to input a program; rather, they simply require a workspace (with associated directories) and a labelled dataset.

Lenovo Accelerated DL is based on the LeTrain project. LeTrain is a distributed training engine based on TensorFlow and optimized by Lenovo. Its goal is to make distributed training as easy as single GPU training and achieve linear scaling performance.

Lenovo Accelerated DL provides an end-to-end workflow for Text Classification, Image Classification, Object Detection, and Instance Segmentation, with training based on Lenovo Accelerated AI pre-defined models. A user can import an unprocessed, unlabeled data set of images, label them, train multiple instances with a grid of parameter values, test the output models for validation, and publish to a git repository for use in an application environment. Additionally, users can initiate the workflow steps from a REST API call to take advantage of LiCO as part of a DevOps toolchain.

Following is the workflow illustrating the main features of Lenovo Accelerated DL:

Figure 7. Lenovo Accelerated DL main features workflow

The following use cases are supported with Lenovo Accelerated AI templates:

Image Classification

Object Detection

Instance Segmentation

Medical Image Segmentation

Seq2Seq

Memory Network

Image GAN

Text Classification

The following figure displays the Lenovo Accelerated AI templates.

Figure 8. Lenovo Accelerated Deep Learning (DL) computer vision (CV) templates

Figure 9. Lenovo Accelerated Deep Learning (DL) natural language processing (NLP) templates

Each Lenovo Accelerated AI use-case is supported by both a training and inference template. The training templates provide parameter inputs such as batch size and learning rate. These parameter fields are pre-populated with default values, but are tunable by those with data science knowledge. The templates also provide visual analytics with TensorBoard; the TensorBoard graphs continually update in-flight as the job runs, and the final statistics are available after the job has completed.

In LiCO the Image Classification and Object Detection templates include the ability to select a topology based on the characteristics of a target inference device, such as an IoT Device, Edge Server, or Data Center server.

The following figure displays the embedded TensorBoard interface for a job. TensorBoard provides visualizations for TensorFlow jobs running in LiCO, whether through Lenovo Accelerated AI templates or the standard TensorFlow AI templates.

Figure 10: TensorBoard in LiCO

LiCO also provides inference templates which allow users to predict with new data based on models that have been trained with Lenovo Accelerated AI templates. For the inference templates, users only need to provide a workspace, an input directory (the location of the data on which inference will be performed), an output directory, and the location of the trained model. The job will run, and upon completion, the output directory will contain the analyzed data. For visual templates such as Object Detection, images can be previewed directly from within LiCO’s Manage Files interface.

The following two figures display an input file to the Object Detection inference template, as well as the corresponding output.

Figure 11: JPG file containing image of cat for input into inference job

Figure 12: LiCO output displaying the section of the JPG containing the cat image

Cloud Tools

LiCO includes the capability to create and deploy instances of Jupyter, RStudio Server and TigerVNC on the cluster. Users may create multiple instances, to customize for different software environments and projects. At the launch of an instance, the user can define the amount of compute resource requirements needed (CPU and GPU) to better optimize the performance of the task and optimize resource usage on the cluster.

Once a Jupyter, TigerVNC or an RStudio Server instance is created, the user can deploy it to the cluster and use the environment directly from their browser in a new tab. The user can leverage the interface directly to upload, download and run code as they normally would, utilizing the shared storage space used for LiCO.

Note: RStudio Server does not support the Chinese version.

Figure 13. Cloud Tools menu

Figure 14. Jupyter instance accessible in new browser tab

Figure 15. Integrated RStudio Server environment

 

Figure 16. Settings definition for an RStudio Server instance

LiCO includes the capability to launch a CVAT labelling environment for image annotation. Users may create and edit multiple CVAT instances for different projects, login to the CVAT web panel to label images and export the labelling as a dataset. The dataset which is created through CVAT can be managed through the dataset management page in LiCO.

Figure 17. CVAT instance accessible in a new browser tab

Opened instances of RStudio Server, Jupyter Notebook, Jupyer Lab, TigerVNC and CVAT can be shared using the online platform URL of that instance.

Figure 18. User can share the Cloud Tools with other or non-hpc users

Workflow

LiCO provides the ability to define multiple job submissions into a single execution action, called a Workflow. Steps are created to execute job submissions in serial, and within each step multiple job submissions may be executed in parallel. Workflow uses LiCO job submission templates to define the jobs for each step, and any template available including custom templates can be used in a workflow.

Figure 19. Defining a workflow in LiCO

Figure 20. Adding a template to a workflow in LiCO

LiCO workflows allow users to automate the deployment of multiple jobs that may be required for a project, so the user can execute and monitor as a single action. Workflows can be easily copied and edited, allowing users to quickly customize existing workflows for multiple projects.

Admin

The Admin tab for the user provides access to container and VNC management.

The Admin tab also enables users to publish a trained model to a git repository or as a docker container image.

LiCO can bill users for jobs and storage instances. Users can download their daily and monthly bills generated automatically on the system.

Some open application programming interfaces (APIs) are available in the API key sub-tab of the Admin tab.

Figure 21. API key page

Additional features for LiCO HPC/AI Users

In addition to the user features above, the LiCO HPC/AI version contains a number of features to simplify HPC workload deployment with a minimal learning curve for users vs. console-based scripting and execution. HPC users can submit jobs easily through standard or custom templates, utilize containers, pre-define runtime modules and environment variables for submission, and since LiCO 6.3 take advantage of advanced features such as Energy Aware Runtime and Intel oneAPI tools and optimizations all from within the LiCO interface.

Topics in this section:

Energy Aware Runtime

Intel oneAPI

HPC Runtime Module Management

Container-based HPC workload deployment

Singularity Container Image Management

Reports

System tools

Energy Aware Runtime

Energy Aware Runtime (EAR) is software technology designed to provide a solution for running MPI applications with higher energy efficiency. Developed in collaboration with Barcelona Supercomputing Center as part of the BSC-Lenovo Cooperation project, EAR is supported for use with the SLURM scheduler through a SPANK plugin. LiCO exposes EAR deployment options within the standard MPI template, allowing users to take advantage of the capability for MPI workloads.

Once the workload has been profiled through a learning phase, EAR will minimize CPU frequency to reduce energy consumption while maintaining a set threshold of performance. This is particularly helpful where MPI applications may not take significant advantage of higher clock frequencies, so the frequency can be reduced to save energy while maintaining expected performance.

Users can select EAR options at job submission in the standard MPI template, either to run the default set by the administrator, minimum time to solution, or minimum energy. Administrators can set the policies and thresholds for EAR usage within the LiCO Administrator portal, as well as which users are authorized to use EAR.

Figure 22. Selection of Energy Policy in MPI template

Figure 23. Administrator portal EAR power policy management

The software technology for EAR is supported separately by Energy Aware Solutions S.L. For more information see https://www.eas4dc.com.

Intel oneAPI

Intel oneAPI is an open-source and standard programming model designed for all industries, which provides the uniform service for the developers of CPU, GPU, and FPGA accelerators. Based on industrial standard and existing programming model of developers, oneAPI open standard can be widely used in varied structures and hardware from different suppliers. The use of Intel oneAPI improves the performance of MPI, OpenMP, TensorFLow, Pythorch, and other programs.

LiCO features templates base on Intel oneAPI – that are optimized to run on Intel processors – developed, tested and validated in collaboration with Intel.

Note: This function is unavailable if Intel oneAPI is not installed.

Figure 24. LiCO Templates for leveraging Intel oneAPI technology

Intel Neural Compressor

Intel Neural Compressor performs model compression to reduce the model size and increase the speed of deep learning inference for deployment on CPUs or GPUs. This open-source Python library automates popular model compression technologies, such as quantization, pruning, and knowledge distillation across multiple deep learning frameworks.

The Python library is integrated in LiCO and used for exporting an image classification model. Intel Neural Compressor FP32, BF16 and INT8 models can be selected.

Figure 25. Export an Image Classification – Intel Neural Compressor job

Intel MPI

Intel MPI Library is a multifabric message-passing library that implements the open-source MPICH specification. Use the library to create, maintain, and test advanced, complex applications that perform better on high-performance computing (HPC) clusters based on Intel processors.

Intel OpenMP

Using the OpenMP pragmas requires an OpenMP-compatible compiler and thread-safe libraries. A perfect choice is the Intel C++ Compiler version 7.0 or newer. (The Intel Fortran compiler also supports OpenMP.) Adding the following command-line option to the compiler instructs it to pay attention to the OpenMP pragmas and to insert threads.

Intel MPITune

The MPITune utility allows users to automatically adjust Intel MPI Library parameters, such as collective operation algorithms, to their cluster configuration or application. The tuner iteratively launches a benchmarking application with different configurations to measure performance and stores the results of each launch. Based on these results, the tuner generates optimal values for the parameters being tuned.

Intel VTune Profiler

Intel VTune Profiler optimizes application performance, system performance, and system configuration for HPC, cloud, IoT, media, storage, and more. Intel VTune Profiler, provided with Intel snapshot performance analyzer, enables users to analyze the serial and multi-threaded applications in hardware platforms (CPU, GPU, FPGA), and analyze the local and remote targets.

In LiCO, users can submit an Intel VTune Profiler job and administrators can perform a platform analysis.

For an Intel MPI, Intel OpenMP and Intel Distribution for Python jobs users can select the VTune Analysis Type.

Figure 26. Intel VTune Profiler integration

Intel Distribution for GDB

The Intel Distribution for GDB application debugger is a companion tool to Intel compilers and libraries. It delivers a unified debugging experience that allows users to efficiently and simultaneously debug cross-platform parallel and threaded applications developed in C, C++, SYCL, OpenMP, or Fortran.

When submitting an Intel MPI or OpenMP job in LiCO, while setting the Template Parameters, users can set the Remotely Debug option to Intel Distribution for GDB. With this setting the running program can be debugged.

Intel Extension for TensorFlow

Intel® Extension for TensorFlow* is a heterogeneous, high performance deep learning extension plugin based on TensorFlow PluggableDevice interface, aiming to bring Intel CPU or GPU devices into TensorFlow open source community for AI workload acceleration. It allows users to flexibly plug an XPU into TensorFlow on-demand, exposing the computing power inside Intel's hardware.

The Intel Extension for TensorFlow job template in LiCO supports running programs on one or more nodes using CPUs or Intel GPUs. For distributed training, using CPU for training currently only supports PS Worker distributed architecture, and using Intel GPU for training currently only supports Horovod distributed training framework.

Intel Extension for PyTorch

Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

LiCO supports users running Intel Extension for PyTorch program on HPC clusters. To use Intel GPU for your pytorch program, If your program contains code like torch.device("cpu"), you should make the corresponding simple modifications before running the job.

Intel Distribution for Python

The Intel Distribution for Python achieve fast math-intensive workload performance without code changes for data science and machine learning problems. Intel Distribution for Python is included as part of the Intel oneAPI AI Analytics Toolkit, which provides accelerated machine learning and data analytics pipelines with optimized deep-learning frameworks and high-performing Python libraries.

Intel Distribution of Modin

The Intel Distribution of Modin is a performant, parallel, and distributed dataframe system that is designed around enabling data scientists to be more productive with the tools that they love. This library is fully compatible with the pandas API. It is powered by OmniSci in the back end and provides accelerated analytics on Intel platforms.

In LiCO, Intel Distribution of Modin templates are available for a single node or multi nodes.

Intel Distribution of Modin Single Node

Intel Distribution of Modin Multi Node

Model Zoo for Intel Architecture

Model Zoo for Intel Architecture contains Intel optimizations for running deep learning workloads on Intel Xeon Scalable processors. In LiCO Image recognition and Object Detection jobs are available with both TensorFlow and PyTorch. Multiple models can be selected when the user is submitting the job.

TensorFlow Image Recognition of Intel Model Zoo

TensorFlow Object Detection of Intel Model Zoo

PyTorch Image Recognition of Intel Model Zoo

PyTorch Object Detection of Intel Model Zoo

HPC Runtime Module Management

LiCO HPC/AI version allows the user to pre-define modules and environmental variables to load at the time of job execution through Job submission templates. These user-defined modules eliminate the step of needing to manually load required modules before job submission, further simplifying the process of running HPC workloads on the cluster. Through the Runtime interface, users can choose from the modules available on the system, define their loading order, and specify environmental variables for repeatable, reliable job deployment.

Figure 27. HPC runtime module list

Figure 28. MPI job template with custom module setup

Container-based HPC workload deployment

Additional standard templates are provided to support deployment of containerized HPC workloads through Singularity or CharlieCloud. These templates simplify deploying containers for HPC workloads by eliminating the need to create custom runtimes and custom templates for these workloads unless needed for more granularity.

In addition to providing a certain number of basic container images, LiCO also allows users to upload customized container images. LiCO 5.2.0 and later versions support running jobs on NGC images.

Figure 29. CharlieCloud and Singularity standard job templates

Singularity Container Image Management

LiCO HPC/AI version provides both users and administrators with the ability to build, upload and manage application environment images through Singularity containers. These images can support users with AI frameworks and HPC workloads, as well as others. Singularity containers may be built from Docker containers, imported from NVIDIA GPU Cloud (NGC), or other image repositories such as the Intel Container Portal. Containers created by administrators are available to all users, and users can create container images for their individual use as well. Users looking to deploy a custom image can also create a custom template that will deploy the container and run workloads in that environment.

Figure 30. Singularity container management through the Administrator portal

Figure 31. Singularity container building within LiCO

Reports

LiCO HPC/AI version provides expanded billing capabilities and provides the user access to monitor charges incurred for a date range via the Expense Reports subtab. Users can also download daily or monthly billing reports as a .xlsx file from the Admin tab.

Figure 32. LiCO User view of Expense Reports

System tools

The system tools option for the user provides access to their storage space on the cluster. The user can upload, download, cut/copy/paste, preview and edit files on the cluster storage space from within the LiCO portal. The text editor within LiCO allows syntax-aware display and editing based on the file extension. Multiple files editor option is available.

Figure 33. Cluster storage access

Figure 34. Text file editor

Features for LiCO Administrators

Topics in this section:

Features for LiCO K8S/AI version administrators

Features for LiCO HPC/AI version Administrators

Features for LiCO Operators

LiCO Deployment

LiCO Upgrade

Features for LiCO K8S/AI version administrators

For administrators of a Kubernetes-based LiCO environment, LiCO provides the ability to monitor activity, create and manage users, monitor LiCO-initiated activity, generate job and operational reports, enable container access for LiCO users, and view the software license currently installed in LiCO. LiCO K8S/AI version does not provide resource monitoring for the administrator, resources can be monitored at the Kubernetes level with a tool such as Kubernetes Dashboard. The following menus are available to administrators in LiCO K8S/AI:

Home menu for Administrators – provides an at-a-glance view of LiCO jobs running and operational messages. For monitoring and managing cluster resources, the administrator can use a tool such as Kubernetes dashboard, Grafana, or other Kubernetes monitoring tools.

User Management menu – provides dashboards to create, import and export LiCO users, and includes administrative actions to edit, suspend, or delete

Monitor menu – provides a view of LiCO jobs running, allocating to the Kubernetes cluster, and completed jobs. This menu also allows the administrator to query and filter operational logs.

Reports menu – allows administrators the ability to generate reports on jobs, for a given time interval. Administrators may export these reports as a spreadsheet, in a PDF, or in HTML. The reports menu also allows the administrator to view cluster utilization for a given date range.

Admin menu – Provides the administrator to map container images for use in job submission templates, and download operations and web logs for LiCO.

Settings menu – allows the administrator to view the currently active license for LiCO, including the license key, license tier and expiration date of the license.

Platform Analysis menu – allows the administrator to analyze and optimize program performance.

Figure 35. LiCO K8S/AI Administrator Home Menu

Features for LiCO HPC/AI version Administrators

For cluster administrators, LiCO provides a sophisticated monitoring solution, built on OpenHPC tooling. The following menus are available to administrators:

Home menu for administrators – provides dashboards giving a global overview of the health of the cluster. Utilization is given for the CPUs, GPUs, memory, storage, and network. Node status is given, indicating which nodes are being used for I/O, compute, login, and management. Job status is also given, indicating runtime for the current job, and the order of jobs in the queue. The Home menu is shown in the following figure.

Figure 36. LiCO HPC/AI Administrator Home Menu

User Management menu – provides dashboards to control user groups and users, determining permissions and access levels (based on LDAP) for the organization. Administrators can also control and provision billing groups for accurate accounting.

Monitor menu – provides dashboards for interactive monitoring and reporting on cluster nodes, including a list of the nodes, or a physical look at the node topology. Administrators may also use the Monitor menu to drill down to the component level, examining statistics on cluster CPUs, GPUs, networking, jobs, and operations. Administrators can access alerts that indicate when these statistics reach unwanted values (for instance, GPU temperature reaching critical levels). These alerts are created using the Setting menu. Additionally, a large screen view is available to display a high-level summary of cluster status, and a cluster view was added since LiCO 6.2 for a focused view of compute resource utilization across the cluster. The figures below display the component and alert dashboards.

Figure 37. LiCO HPC/AI Administrator Component dashboard

Figure 38. LiCO HPC/AI Administrator Alert dashboard

Figure 39. LiCO HPC/AI GPU View dashboard

Hybrid HPC - Bursting on cloud is now possible. LiCO 7.0 has support for hybrid cloud integration leveraging Microsoft Azure. That will allow our customers to add Microsoft Azure access as Supercomputer resources that will be included in the scheduling considerations. The Hybrid HPC enable users to leverage the public cloud to dynamically scale the computing resources of the local HPC cluster.

          Figure 40. LiCO Hybrid HPC for Microsoft Azure

LiCO version 7.2 introduces enhanced cloud integration capabilities through the incorporation of Covalent, an open-source Pythonic workflow orchestration platform. This integration allows LiCO to seamlessly operate in a Hybrid Cloud environment, supporting popular cloud providers such as AWS, Azure, and Google Cloud.

Figure 41. LiCO Hybrid HPC

Figure 42. LiCO integration with Covalent for Multi-Cloud support

Reports menu – allows administrators the ability to generate reports on jobs, cluster utilization, alerts, and view current charges and cluster utilization.

Admin menu – Provides the administrator with the capability to create Singularity images for use by all users, generate billing spreadsheets, examine processes and assets, monitor VNC sessions, and download web logs. The administrators can also publish announcements to users using the Notice function.

Settings menu – allows administrators to set up automated notifications and alerts.  Administrators may enable the notifications to reach users and interested parties via email, SMS, and WeChat. Administrators may also enable notifications and alerts via uploaded scripts.

The Settings menu also allows administrators to create and modify queues. These queues allow administrators to subdivide hardware based on different types or needs. For example, one queue may contain systems that are exclusively machines with GPUs, while another queue may contain systems that only contain CPUs. This allows the user running the job to select the queue that is more applicable to their requirement. Within the Settings menu, administrators can also set the status of queues, bringing them up or down, draining them, or marking them inactive. Administrators can also limit which queues are available to users by user group.

Starting with LiCO 7.1 administrators can configure Quality of Service (QOS) for each job submitted to Slurm from the Scheduler sub tab. Administrators can edit and delete a limitation, create a new limitation and then associate it with the corresponding Billing Group. This function requires to configure the SLURM account on the cluster.

Platform Analysis menu – allows the administrator to analyze and optimize program performance. Administrators can determine the cause of poor performance by finding software and hardware performance bottlenecks and identifying program hotspots. After that, developers can optimize programs according to the causes.

Figure 43. Platform analysis tools for HPC cluster administrator and end user

License menu – displays the software licenses active in LiCO including the number of licensed processing entitlements and the expiration date of the license.

Features for LiCO Operators

For the purpose of monitoring clusters but not overseeing user access, LiCO provides the Operator designation. LiCO Operators have access to a subset of the dashboards provided to Administrators; namely, the dashboards contained in the Home, Monitor, and Reports menus:

Home menu for operators – provides dashboards giving a global overview of the health of the cluster. Utilization is given for the CPUs, GPUs, memory, storage, and network. Node status is given, indicating which nodes are being used for I/O, compute, login, and management. Job status is also given, indicating runtime for the current job, and the order of jobs in the queue.

Monitor menu – Dashboard that enables interactive monitoring and reporting on cluster nodes, including a list of the nodes, or a physical look at the node topology. Operators may also use the Monitor menu to drill down to the component level, examining statistics on cluster CPUs, GPUs, jobs, and operations. Operators can access alarms that indicate when these statistics reach unwanted values (for instance, GPU temperature reaching critical levels.) These alarms are created by Administrators using the Settings menu (for more information on the Settings menu, see the Features for LiCO Administrators section.)

Reports menu – allows operators the ability to generate reports on jobs, alerts, or actions for a given time interval. Operators may export these reports as a spreadsheet, in a PDF, or in HTML.

LiCO Deployment

Docker containerized deployment

LiCO can be deployed in Docker containers with all the supported operating systems.

In LiCO we offer containerized deployment as a preferred method. This approach involves running the HPC cluster drivers, monitoring software, scheduler and applications on the host operating system, while the container instance contains the LiCO web portal, including the back-end LiCO service.

The container version of our product supports all the schedulers supported in the deployment.

Benefits of Containerized Deployment

Simplify the deployment process: Containerized deployment enables LiCO to work efficiently with the latest OpenHPC package, streamlining updates and maintaining compatibility with the latest features while the number of packages to be deployed is minimal.

Simplified Test Systems: Containerized deployment reduces the complexity of maintaining and testing multiple systems for different OS versions, leading to a more efficient and cost-effective deployment process.

Reduced Operating System Dependency: Containerization reduces the dependency on specific operating systems, enhancing portability and making it easier to deploy LiCO across diverse environments.

Supported Operating Systems

The container version of our product supports the following operating systems:

Rocky Linux 8 (Default Base OS)

RHEL (Red Hat Enterprise Linux) 8

Ubuntu (Only in Container Mode)

SUSE (Only in Container Mode)

By default, the container version of our product uses Rocky Linux 8 as the base operating system for container images.

Customers with specific security considerations for the operating system can opt for host OS deployment. For Rocky and RHEL operating systems, we offer support for both host OS deployment and container deployment. Customers can choose the mode that best suits their requirements.

For any further assistance or inquiries, please review the Deploy LiCO in container section from the installation guides available at https://support.lenovo.com/us/en/solutions/HT507011

LiCO Upgrade

Below you can find the instructions for upgrading LiCO (License and Configuration Optimizer) from version 7.x (where "x" represents the source version) to version 7.y (where "y" represents the latest available version) on different hosting environments.

This upgrade guide below outlines the steps for transitioning from LiCO version 7.x to version 7.y on different hosting environments. Follow the instructions carefully to ensure a successful upgrade while minimizing disruptions to your LiCO system. If you encounter any issues during the upgrade process, consult the official LiCO documentation or seek assistance from L3 support.

This guide is intended for system administrators, developers, or individuals responsible for managing LiCO installations.

Versions:

Source LiCO version: 7.x

Target LiCO version: 7.y

Upgrade Scenarios:

LiCO v7.x on Host > v7.y on Host:

In this scenario, you are upgrading LiCO on the host machine directly from version 7.x to version 7.y. No manual changes are required, and you can use an auto-upgrade script or guide provided by the LiCO team.

LiCO v7.x on Host > v7.y on Container:

For this upgrade, you will be migrating from the host-based installation of LiCO version 7.x to a containerized version 7.y. The process involves removing LiCO packages, retaining configuration files and database files, installing Docker, building or downloading the LiCO v7.y container image, and following the installation guide to configure LiCO v7.y.

LiCO v7.x on Container > v7.y on Container:

If you already have LiCO version 7.x running in a container and want to upgrade to a newer version 7.y, you need to replace the LiCO container image with the one corresponding to version 7.y. Afterward, follow the installation guide to configure LiCO v7.y.

LiCO v7.x on Container > v7.y on Host:

This upgrade scenario is not recommended and should only be attempted with the assistance of L3 support. The process involves migrating from LiCO version 7.x running in a container to a host-based installation of LiCO version 7.y.

Important Notes:

Always back up your configuration files and database files before proceeding with any upgrade.

Make sure to follow the official LiCO installation and upgrade guides provided by the vendor for each specific version.

LiCO GUI Installer is a tool that simplifies HPC cluster deployment and LiCO setup. It runs on the management node and it can use Confluent to deploy the OS on the compute nodes.

The user can define the following node types:

head node (currently only a single head node is supported. (This is the same machine on which the installer runs)

login nodes - one or more

compute nodes - one or more

The compute nodes that have at least 1 GPU defined in the config file are treated as GPU nodes and NVIDIA drivers will be installed on these.

You can download LiCO Installation GUI from here, and follow this guide to deploy HPC cluster and LiCO easily.

Figure 44. LiCO GUI Installer

Diskless installation

A diskless boot system (otherwise known as a PXE boot setup) is a computer system without hard drives. Instead, each computer uses network-attached storage drives on a server to store data.

LiCO support the option to have a diskless installation. You can follow this guide to deploy HPC cluster with the diskless option.

Subscription and Support

LiCO HPC/AI is enabled through a per-CPU and per-GPU subscription and support entitlement model, which once entitled for the all the processors contained within the cluster, gives the customer access to LiCO package updates and Lenovo support for the length of the acquired term.

LiCO K8S/AI is enabled through tiered subscription and support entitlement licensing based on the number of GPU accelerators being accessed by running LiCO workloads (tiers are up to 4 GPU in use, up to 16 GPU in use, and up to 64 GPU in use). Additional licensing beyond 64 GPUs can be provided by contacting your Lenovo sales representative.

Lenovo will provide interoperability support for all software tools defined as validated with LiCO, and development support (Level 3) for specific Lenovo-supported tools only. Open source and supported-vendor bugs/issues will be logged and tracked with their respective communities or companies if desired, with no guarantee from Lenovo for bug fixes. Full support details are provided at the support links below for each respective version of LiCO. Additional support options may be available; please contact your Lenovo sales representative for more information.

LiCO can be acquired as part of a Lenovo Scalable Infrastructure (LeSI) solution or for “roll your own” (RYO) solutions outside of the LeSI framework, and LiCO software package updates are provided directly through the Lenovo Electronic Delivery system. More information on LeSI is available in the LeSI product guide, available from https://lenovopress.com/lp0900.

Lenovo provides support in English globally and in Chinese for China (24x7)

Support response times are as follows:

Severity 1 issues response is 1 business day

Other issues: 3 business days

LiCO has 1-year lifecycle for each release, customer should upgrade to latest version if out of support.

The following table lists end of support for LiCO versions.

Table 3. End of support list

Version 

Date

LiCO 7.2

2/06/2025

LiCO 7.1

6/28/2024

LiCO 7.0

12/20/2023

LiCO 6.4

6/24/2023

LiCO 6.3.1

3/29/2023

LiCO 6.3

12/15/2022

LiCO 6.2

6/2/2022

LiCO 6.1

12/15/2021

LiCO 6.0

8/3/2021

LiCO 5.5.0

4/15/2021

LiCO 5.4.0

11/5/2020

LiCO 5.3.1

6/18/2020

LiCO 5.3.0

4/12/2020

LiCO 5.2.1

1/9/2020

LiCO 5.2.0

11/21/2019

LiCO 5.1.0

5/3/2019

Validated software components

LiCO’s software packages are dependent on a number of software components that need to be installed prior to LiCO in order to function properly. Each LiCO software release is validated against a defined configuration of software tools and Lenovo systems, to make deployment more straightforward and enable support. Other management tools, hardware systems and configurations outside the defined stack may be compatible with LiCO, though not formally supported; to determine compatibility with other solutions, please check with your Lenovo sales representative.

The following software components are validated by Lenovo as part of the overall LiCO software solution entitlement:

LiCO HPC/AI version support

Lenovo Development Support (L1-L3)

Graphical User Interface: LiCO

System Management & Provisioning: Confluent

Lenovo LiCO HPC/AI Configuration Support (L1 only)

Job Scheduling & Orchestration: SLURM, OpenPBS, Torque/Maui (HPC only)

System Monitoring: Icinga v2

Container Support (AI): Singularity, CharlieCloud, NGC

AI Frameworks (AI): Caffe, Intel-Caffe, TensorFlow, MxNet, Neon, Chainer, Pytorch, Scikit-learn, PaddlePaddle, NVIDIA TensorRT, TensorBoard

Visualization: Grafana

The following software components are validated for compatibility with LiCO HPC/AI:

Supported by their respective software provider

Operating System: RHEL 8.6, Rocky Linux 8.6, SUSE SLES 15 SP3, CentOS 7.9, Ubuntu 22.04 LTS

File Systems: IBM Spectrum Scale, Lustre GPFS

Job Scheduling & Orchestration: IBM Spectrum LSF v10, Altair PBS Pro

Development Tools: GNU compilers, Intel Cluster Toolkit

LiCO K8S/AI version support

Lenovo Development Support (L1-L3)

Graphical User Interface: LiCO

Lenovo LiCO K8S/AI Configuration Support (L1 only)

AI Frameworks (AI): Caffe, Intel-Caffe, TensorFlow, MxNet, Neon, Chainer, Pytorch, Scikit-learn, PaddlePaddle

Validated hardware components

Supported GPUs

NVIDIA L40, NVIDIA H100, NVIDIA A100, NVIDIA A30, NVIDIA A40, NVIDIA T4, NVIDIA V100, NVIDIA RTX8000, NVIDIA RTX6000

Intel Flex Series 140 GPU, Intel Flex Series 170 GPU

NVIDIA H100 Multi-Instance GPU (MIG), NVIDIA A100 Multi-Instance GPU (MIG)

Note: Subject to specific ThinkSystem platform support, not all GPUs available on all systems

Supported Networks

Intel OmniPath 100

Mellanox Infiniband (FDR, EDR, HDR, NDR)

Gb Ethernet (1, 10, 25, 40,50, 100)

Supported servers (LiCO HPC/AI version)

LiCO seamlessly integrates with both Lenovo servers and workstations, offering robust support for Lenovo hardware within the cluster. Additionally, LiCO extends its compatibility beyond Lenovo infrastructure, providing full support for non-Lenovo hardware within the cluster environment. This versatility ensures optimal performance and flexibility, allowing organizations to leverage LiCO's capabilities across a diverse range of hardware configurations for efficient and scalable computing orchestration.

The following Lenovo systems are supported to run with LiCO HPC/AI. This systems must run one of the supported operating systems as well as the validated software stack, as described in the Validated Software Components section.

ThinkSystem SR860 V3 – The Lenovo ThinkSystem SR860 V3 is a 4-socket server that features a 4U rack design with support for up to eight high-performance GPUs. The server offers technology advances, including 4th Gen Intel Xeon Scalable processors, 4800 MHz DDR5 memory, and PCIe 5.0. For more information, see the SR860 V3 product guide.

ThinkSystem SR850 V3 – The Lenovo ThinkSystem SR850 V3 is a 4-socket server that is densely packed into a 2U rack design. The server offers technology advances, including 4th Gen Intel Xeon Scalable processors, 4800 MHz DDR5 memory, and PCIe Gen 5. For more information, see the SR850 V3 product guide.

ThinkSystem SR675 V3 – The Lenovo ThinkSystem SR675 V3 is a versatile GPU-rich 3U rack server that supports eight double-wide GPUs including the new NVIDIA H100 and L40 Tensor Core GPUs, or the NVIDIA HGX H100 4-GPU offering with NVLink and Lenovo Neptune hybrid liquid-to-air cooling. The server is based on the new AMD EPYC 9004 Series processors (formerly codenamed "Genoa"). For more information, see the SR675 V3 product guide.

ThinkSystem SD665 V3 – The ThinkSystem SD665 V3 Neptune DWC server is the next-generation high-performance server based on the fifth generation Lenovo Neptune™ direct water cooling platform. For more information, see the SD665 V3 product guide.

ThinkSystem SR655 V3 – The Lenovo ThinkSystem SR655 V3 is a 1-socket 2U server that features the AMD EPYC 9004 "Genoa" family of processors. With up to 96 cores per processor and support for the new PCIe 5.0 standard for I/O, the SR655 V3 offers the ultimate in one-socket server performance in a 2U form factor. For more information, see the SR655 V3 product guide.

ThinkSystem SR635 V3 – The Lenovo ThinkSystem SR635 V3 is a 1-socket 1U server that features the AMD EPYC 9004 "Genoa" family of processors. With up to 96 processor cores and support for the new PCIe 5.0 standard for I/O, the SR635 V3 offers the ultimate in one-socket server performance in a 1U form factor. For more information, see the SR635 V3 product guide.

ThinkSystem SD650-I V3 – The ThinkSystem SD650-I V3 Neptune DWC server is the next-generation high-performance server based on the fifth generation Lenovo Neptune™ direct water cooling platform. For more information, see the SD650-I V3 product guide.

ThinkSystem SD650 V3 – The ThinkSystem SD650 V3 Neptune DWC server is the next-generation high-performance server based on the fifth generation Lenovo Neptune™ direct water cooling platform. For more information, see the SD650 V3 product guide.

ThinkSystem SR650 V3 – The Lenovo ThinkSystem SR650 V3 is an ideal 2-socket 2U rack server for small businesses up to large enterprises that need industry-leading reliability, management, and security, as well as maximizing performance and flexibility for future growth. For more information, see the SR650 V3 product guide.

ThinkSystem SR630 V3 – The Lenovo ThinkSystem SR630 V3 is an ideal 2-socket 1U rack server for small businesses up to large enterprises that need industry-leading reliability, management, and security, as well as maximizing performance and flexibility for future growth. For more information, see the SR630 V3 product guide.

ThinkSystem SR665 V3 – The Lenovo ThinkSystem SR665 V3 is a 2-socket 2U server that features the AMD EPYC 9004 "Genoa" family of processors. With up to 96 cores per processor and support for the new PCIe 5.0 standard for I/O, the SR665 V3 offers the ultimate in two-socket server performance in a 2U form factor. For more information, see the SR665 V3 product guide.

ThinkSystem SR645 V3 – The Lenovo ThinkSystem SR645 V3 is a 2-socket 1U server that features the AMD EPYC 9004 "Genoa" family of processors. With up to 96 cores per processor and support for the new PCIe 5.0 standard for I/O, the SR645 V3 offers the ultimate in two-socket server performance in a 1U form factor. For more information, see the SR645 V3 product guide.

ThinkSystem SR670 V2 – The Lenovo ThinkSystem SR670 V2 is a versatile GPU-rich 3U rack server that supports eight double-wide GPUs including the new NVIDIA A100 and A40 Tensor Core GPUs, or the NVIDIA HGX A100 4-GPU offering with NVLink and Lenovo Neptune hybrid liquid-to-air cooling. The server is based on the new third-generation Intel Xeon Scalable processor family (formerly codenamed "Ice Lake"). The server delivers optimal performance for Artificial Intelligence (AI), High Performance Computing (HPC) and graphical workloads across an array of industries. For more information, see the SR670 V2 product guide.

ThinkSystem SD650 V2 – The ThinkSystem SD650 V2 server is the next-generation high-performance server based on Lenovo's fourth generation Lenovo Neptune™ direct water cooling platform. With two third-generation Intel Xeon Scalable processors, the ThinkSystem SD650 V2 server combines the latest Intel processors and Lenovo's market-leading water cooling solution, which results in extreme performance in an extreme dense packaging, supporting your application From Exascale to Everyscale™. For more information, see the SD650 V2 product guide.

ThinkSystem SD650-N V2 – The ThinkSystem SD650-N V2 server is the next-generation high-performance GPU-rich server based on Lenovo's fourth generation Lenovo Neptune™ direct water cooling platform. With four NVIDIA A100 SXM4 GPUs and two third-generation Intel Xeon Scalable processors, the ThinkSystem SD650-N V2 server combines advanced NVIDIA acceleration technology with the latest Intel processors and Lenovo's market-leading water cooling solution, which results in extreme performance in an extreme dense packaging supporting your accelerated application From Exascale to Everyscale™. For more information, see the SD650-N V2 product guide.

ThinkSystem SR650 V2 – The Lenovo ThinkSystem SR650 V2 is an ideal 2-socket 2U rack server for small businesses up to large enterprises that need industry-leading reliability, management, and security, as well as maximizing performance and flexibility for future growth. The SR650 V2 is a very configuration-rich offering, supporting 28 different drive bay configurations in the front, middle and rear of the server and 5 different slot configurations at the rear of the server. This level of flexibility ensures that you can configure the server to meet the needs of your workload. For more information, see the SR650 V2 product guide.

ThinkSystem SR630 V2 – The Lenovo ThinkSystem SR630 V2 is an ideal 2-socket 1U rack server designed to take full advantage of the features of the 3rd generation Intel Xeon Scalable processors, such as the full performance of 270W 40-core processors, support for 3200 MHz memory and PCIe Gen 4.0 support. The server also offers onboard NVMe PCIe ports that allow direct connections to 12x NVMe SSDs, which results in faster access to store and access data to handle a wide range of workloads. For more information, see the SR630 V2 product guide.

ThinkSystem SD530 – The Lenovo ThinkSystem SD530 is an ultra-dense and economical two-socket server in a 0.5U rack form factor. With up to four SD530 server nodes installed in the ThinkSystem D2 enclosure, and the ability to cable and manage up to four D2 enclosures as one asset, you have an ideal high-density 2U four-node (2U4N) platform for enterprise and cloud workloads. The SD530 also supports a number of high-end GPU options with the optional GPU tray installed, making it an ideal solution for AI Training workloads. For more information, see the SD530 product guide.

ThinkSystem SD650 – The Lenovo ThinkSystem SD650 direct water cooled server is an open, flexible and simple data center solution for users of technical computing, grid deployments, analytics workloads, and large-scale cloud and virtualization infrastructures. The direct water cooled solution is designed to operate by using warm water, up to 50°C (122°F). Chillers are not needed for most customers, meaning even greater savings and a lower total cost of ownership. The ThinkSystem SD650 is designed to optimize density and performance within typical data center infrastructure limits, being available in a 6U rack mount unit that fits in a standard 19-inch rack and houses up to 12 water-cooled servers in 6 trays. For more information, see the SD650 product guide.

ThinkSystem SR630 – Lenovo ThinkSystem SR630 is an ideal 2-socket 1U rack server for small businesses up to large enterprises that need industry-leading reliability, management, and security, as well as maximizing performance and flexibility for future growth. The SR630 server is designed to handle a wide range of workloads, such as databases, virtualization and cloud computing, virtual desktop infrastructure (VDI), infrastructure security, systems management, enterprise applications, collaboration/email, streaming media, web, and HPC. For more information, see the SR630 product guide.

ThinkSystem SR650 – The Lenovo ThinkSystem SR650 is an ideal 2-socket 2U rack server for small businesses up to large enterprises that need industry-leading reliability, management, and security, as well as maximizing performance and flexibility for future growth. The SR650 server is designed to handle a wide range of workloads, such as databases, virtualization and cloud computing, virtual desktop infrastructure (VDI), enterprise applications, collaboration/email, and& business analytics and big data. For more information, see the SR650 product guide.

ThinkSystem SR670 – The Lenovo ThinkSystem SR670 is a purpose-built 2 socket 2U accelerated server, supporting up to 8 single-wide or 4 double-wide GPUs and designed for optimal performance required by both Artificial Intelligence and High Performance Computing workloads. Supporting the latest NVIDIA GPUs and Intel Xeon Scalable processors, the SR670 supports hybrid clusters for organizations that may want to consolidate infrastructure, improving performance and compute power, while maintaining optimal TCO. For more information, see the SR670 product guide.

ThinkSystem SR950 – The Lenovo ThinkSystem SR950 is Lenovo’s flagship server, suitable for mission-critical applications that need the most processing power possible in a single server. The powerful 4U ThinkSystem SR950 can expand from two to as many as eight Intel Xeon Scalable Family processors. The modular design of SR950 speeds upgrades and servicing with easy front or rear access to all major subsystems that ensures maximum performance and maximum server uptime. For more information, see the SR950 product guide.

ThinkSystem SR655 – The Lenovo ThinkSystem SR655 is a 1-socket 2U server that features the AMD EPYC 7002 "Rome" family of processors. With up to 64 cores per processor and support for the new PCIe 4.0 standard for I/O, the SR655 offers the ultimate in single-socket server performance. ThinkSystem SR655 is a multi-GPU optimized rack server, providing support for up to 6 low-profile GPUs or 3 double-wide GPUs. For more information, see the SR655 product guide.

ThinkSystem SR635 – The Lenovo ThinkSystem SR635 is a 1-socket 1U server that features the AMD EPYC 7002 "Rome" family of processors. With up to 64 cores per processor and support for the new PCIe 4.0 standard for I/O, the SR635 offers the ultimate in single-socket server performance. For more information, see the SR635 product guide.

ThinkSystem SR645 – The Lenovo ThinkSystem SR645 is a 2-socket 1U server that features the AMD EPYC 7002 "Rome" family of processors. With up to 64 cores per processor and support for the new PCIe 4.0 standard for I/O, the SR645 offers the ultimate in two-socket server performance in a space-saving 1U form factor. For more information, see the SR645 product guide.

ThinkSystem SR665 – The Lenovo ThinkSystem SR665 is a 2-socket 2U server that features the AMD EPYC 7002 "Rome" family of processors. With support for up to 8 single-wide or 3 double-wide GPUs, up to 64 cores per processor and support for the new PCIe 4.0 standard for I/O, the SR665 offers the ultimate in two-socket server performance in a 2U form factor. ThinkSystem SR665 is a multi-GPU optimized rack server, providing support for up to 8 low-profile GPUs or 3 double-wide GPUs. For more information, see the SR665 product guide.

ThinkSystem SR850 – The Lenovo ThinkSystem SR850 is a 4-socket server that features a streamlined 2U rack design that is optimized for price and performance, with best-in-class flexibility and expandability. The SR850 now supports second-generation Intel Xeon Scalable Family processors, up to a total of four, each with up to 28 cores. The ThinkSystem SR850’s agile design provides rapid upgrades for processors and memory, and its large, flexible storage capacity helps to keep pace with data growth. For more information, see the SR850 product guide.

China only:

ThinkServer SR660 V2 - The Lenovo ThinkServer SR660 V2 is an ideal 2-socket 2U rack server for SMB, large enterprises and cloud service provider that need industry-leading performance and flexibility for future growth. The SR660 V2 is based on the new 3rd generation Intel Xeon Scalable processor, with the new Intel Optane Persistent Memory 200 Series, the low latency NVMe SSD and the powerful GPU to support most customers workload such as databases, virtualization and cloud computing, virtual desktop infrastructure (VDI), infrastructure security, systems management, enterprise applications, collaboration/email, streaming media, web, and HPC. For more information, see the SR660 V2 product guide.

ThinkServer SR590 V2 – The Lenovo ThinkServer SR590 V2 is an ideal 2-socket 2U rack server for small businesses up to large enterprises that need industry-leading reliability, management, and security, as well as maximizing performance and flexibility for future growth. The SR590 V2 is based on the 3rd generation Intel Xeon Scalable processor family (formerly codenamed "Ice Lake") and the Intel Optane Persistent Memory 200 Series. For more information, see the SR590 V2 product guide.

WenTian WR5220 G3 – Lenovo WenTian WR5220 G3 is designed for customers of large, SMB enterprises and cloud service providers. It is 2-socket 2U rack server with excellent performance and high scalability. It is based on the 4th or 5th generation Intel Xeon Scalable processor family (codenamed "Sapphire Rapids", "Emerald Rapids") which can reach up to 385W TDP*, it can also support high-performance and high-frequency DDR5 memory, low latency NVMe SSD, and strong GPU performance to meet the most of customer workloads, such as databases, virtualization and cloud computing, AI, high-performance computing, virtual desktop infrastructure, infrastructure security, system management, enterprise applications, collaboration/email, streaming media, etc. For more information, see the WR5220 G3 product guide.

Workstations:

ThinkStation P620 – The ThinkStation P620 workstation tower is equipped with abundant storage and memory capacity, numerous expansion slots, enterprise-class AMD Ryzen PRO manageability, and security features. With unprecedented visual computing powered by NVIDIA® professional graphics support, this eminently configurable workstation is equipped with up to two NVIDIA® RTX™ A6000 graphics cards with NVLink.

Additional Lenovo ThinkSystem and System x servers and workstations may be compatible with LiCO. Contact your Lenovo sales representative for more information.

LiCO Implementation services

Customers who do not have the cluster management software stack required to run with LiCO may engage Lenovo Professional Services to install LiCO and the necessary open-source software. Lenovo Professional Services can provide comprehensive installation and configuration of the software stack, including operation verification, as well as post-installation documentation for reference. Contact your Lenovo sales representative for more information.

Client PC requirements

A web browser is used to access LiCO's monitoring dashboards. To fully utilize LiCO’s monitoring and visualization capabilities, the client PC should meet the following specifications:

Hardware: CPU of 2.0 GHz or above and 8 GB or more of RAM

Display resolution: 1280 x 800 or higher

Browser: Chrome (v62.0 or higher) or Firefox (v56.0 or higher) is recommended

Related links

For more information, see the following resources:

LiCO website:https://www.lenovo.com/us/en/data-center/software/lico/

LiCO HPC/AI (Host) Support website:https://support.lenovo.com/us/en/solutions/HT507011

LiCO K8S/AI (Kubernetes) Support website:https://support.lenovo.com/us/en/solutions/HT509422

Technical LiCO Documentation:https://hpc.lenovo.com/users/lico/

Lenovo HPC & AI Software Stack Product Guidehttps://lenovopress.lenovo.com/lp1651-lenovo-hpc-ai-software-stack

Lenovo DCSC configurator:https://dcsc.lenovo.com

Lenovo AI website:https://www.lenovo.com/us/en/data-center/solutions/analytics-ai/

Lenovo HPC website:https://www.lenovo.com/us/en/data-center/solutions/hpc/

LeSI website:https://www.lenovo.com/us/en/p/data-center/servers/high-density/lenovo-scalable-infrastructure/wmd00000276

OpenHPC User Resources:https://github.com/openhpc/ohpc/wiki/User-Resources

Intel oneAPI:https://software.intel.com/content/www/us/en/develop/tools.html

Altair PBS Professional Documentation:https://www.altair.com/pbs-professional/

Lenovo Compute Orchestration in HPC Data Centers with Slurmhttps://lenovopress.lenovo.com/lp1701-lenovo-compute-orchestration-in-hpc-data-centers-with-slurm

Related product families

Product families related to this document are the following:

Artificial Intelligence

High Performance Computing

Trademarks

Lenovo and the Lenovo logo are trademarks or registered trademarks of Lenovo in the United States, other countries, or both. A current list of Lenovo trademarks is available on the Web at https://www.lenovo.com/us/en/legal/copytrade/.

The following terms are trademarks of Lenovo in the United States, other countries, or both:

Lenovo®

From Exascale to Everyscale

Lenovo Neptune®

System x®

ThinkServer®

ThinkStation®

ThinkSystem®

The following terms are trademarks of other companies:

Intel®, Intel Optane™, Xeon®, and VTune™ are trademarks of Intel Corporation or its subsidiaries.

Linux® is the trademark of Linus Torvalds in the U.S. and other countries.

Microsoft® and Azure® are trademarks of Microsoft Corporation in the United States, other countries, or both.

Other company, product, or service names may be trademarks or service marks of others.

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Full Change History

Changes in the February 8, 2024 update:

Updated for LiCO 7.2:

Updated login images

Cloud Tools menu

Adding a template to a workflow in LiCO

HPC runtime module list

Updated all the features under - What's new in LiCO 7.2 section

Added new supported GPU under - Validated hardware components section

NVIDIA L40

Added new servers support under - Supported servers (LiCO HPC/AI version) section

Lenovo ThinkSystem SR860 V3, SR850 V3, SR590 V2

Lenovo WenTian WR5220 G3 (C4C type)

Lenovo ThinkStation P620 (without out-of-band monitoring)

Changes in the August 24, 2023 update:

Updated for LiCO 7.1:

Updated login images

Updated Jupyter notebook to official name Jupyter Notebook

Rebranding on features occurred in - Features for LiCO users section and in Additional features for LiCO HPC/AI Users section:

Merged Intel Optimization for TensorFlow2 Single Node and Intel Optimization for TensorFlow2 Multi Node, changed them to Intel Extension for TensorFlow.

Changed Intel Optimization for PyTorch Single Node to Intel Extension for PyTorch. 

Changes in the March 21, 2023 update:

Updated URLs and minor changes for PyTorch - Additional features for LiCO HPC/AI Users section

Changes in the February 22, 2023 update:

Updated for LiCO 7.0:

New features

User Home Menu & job templates

New features to Lenovo Accelerated AI

New Deep Learning (DL) computer vision (CV) templates

Removed AI Studio feature

New Intel features

New features with Hybrid HPC with Microsoft Azure

New LiCO deployment section

New table subscription and support

Changes in the August 8, 2022 update:

Updated for LiCO 6.4:

Support NVIDIA Multi-Instance GPU(MIG) on Slurm, LSF and PBS

Expanded support for Intel oneAPI tools and templates (HPC/AI version)

Lenovo Accelerated AI and AI Studio support configurable Early Stopping strategy

Integrated RStudio Server

Integrated CVAT labelling tool

Add Platform Analysis tools for HPC cluster administrator and end user.

Updated LiCO HPC/AI version ordering information

Changes in the June 1, 2021 update:

Updated for LiCO 6.2

Support for new ThinkSystem V2 servers (SR670 V2, SR650 V2, SR630 V2, SD650 V2, SD650-N V2)

Lenovo Accelerated AI for Text Classification

Trained model packaging into a docker container image

Intel OneAPI tools and templates (HPC/AI version)

Cluster View for more detailed resource monitoring (HPC/AI version)

Changes in the December 15, 2020 update:

Updated for LiCO 6.1

Improved text editor with syntax-aware formatting based on extension

Ability to add tags and comments to completed jobs for easy filtering

Integrated Singularity container image builder (HPC/AI)

Support for CharlieCloud (HPC)

Support for NVIDIA A100 (HPC/AI)

Changes in the August 4, 2020 update:

Updated for LiCO 6.0

Workflow feature to pre-define multiple job steps

Infiniband Monitoring for administrators (HPC/AI version)

Estimated job start times (HPC/AI version)

Support for ThinkSystem SR850, SR635, SR645, SR665

Support for NVIDIA RTX 8000

Changes in the April 15, 2020 update:

Updated for LiCO 5.5

TensorFlow 2 standard template support

Cut/Copy/Paste/Duplicate files and folders from within the LiCO storage interface

User notification via email when a job completes or is cancelled

Added CPU and GPU utilization monitoring for K8S/AI running jobs

Expanded billing support to include memory, GPU, and storage utilization (HPC/AI version)

Ability to export daily and monthly billing reports for administrators and users (HPC/AI version)

Cluster utilization monitoring for administrators

Changes in the November 5, 2019 update:

Updated for LiCO 5.4

Jupyter notebook access from the cluster

“Favorites” tab for quick access to frequently used job submission templates

Import/Export of custom job submission templates for ease of sharing between users

Job submission template support for PyTorch and scikit-learn

Additional version of LiCO to support AI workloads on a Kubernetes-based cluster

Changes in the April 16, 2019 update:

Updated for LiCO 5.3

End-to-end AI training workflows for Image Classification, Object Detection, and Instance Segmentation

Option to copy existing jobs into the original template, with existing parameters pre-filled and modifiable

Enablement on the Lenovo ThinkSystem SR950

Support for Keras, Chainer AI framework, and latest MxNet optimizations for Intel CPU training

Integration support for HBase and MongoDB BigData sources

Integration support for trained AI model publishing to git repositories

REST interface to instantiate LiCO AI training functions from DevOps tools

Changes in the February 13, 2019 update:

Corrected the link to the support page - Related links section

Changes in the November 12, 2018 update:

Updates for LiCO Version 5.2

Queue management functionality, providing the ability to create and manage workload queues from within the GUI

Enablement on the Lenovo ThinkSystem SD650 and SR670 systems

Exclusive mode, to select whether to dedicate or share systems when requesting resources

Support for NVIDIA GPU Cloud (NGC) Container images

Lenovo Accelerated AI templates to provide easy-to-use training and inference functionality for a variety of AI use cases

Enhancements to storage management within LiCO

Changes in the August 7, 2018 update:

Revised list of validated software components

Added information on LiCO installation through Lenovo Professional Services

First published: 26 March 2018

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LiCO 7.0.0 Installation Guide (for EL7)_zh-cn

LiCO 7.0.0 Installation Guide (for EL7)_zh-cn

LiCO 7.0.0安装指南(适用于EL7.9)

第 1 章 概述

典型的集群部署

本指南基于包含管理节点、登录节点和计算节点的典型集群部署进行介绍。

下表对集群中的元素进行了说明。

表1. 典型集群中的元素说明

元素

描述

管理 节点

HPC/AI 集群的核心,承载集群管理、监控、调度、策略管理以及用户与帐户管理等主要功能。

计算节点

完成计算任务。

登录节点

将集群连接到外部网络或集群。用户必须使用登录节点登录并上传应用程序数据、开发编译器以及提交调度的任务。

文件服务

文件服务提供共享存储功能。它通过高速网络连接到集群节点。文件服务设置不在本指南的讨论范围内。我们使用简单的NFS 设置。

节点BMC接口

用于访问节点BMC 系统。

节点eth接口

用于管理集群中的节点。还可以用于传输计算数据。

高速网路接口

可选。用于支持文件服务。还可以用于传输计算数据。

注:LiCO 也支持仅包含管理节点和计算节点的集群部署。在这种情况下,所有安装在登录节点上的LiCO 模块都需要安装在管理节点上。

第2 章 部署集群环境

如果集群环境已存在,请跳过本章。

安装操作系统

安装正式版centos7.9。您可以选择最小安装。配置内存并重新启动操作系统:

echo '* soft memlock unlimited' >> /etc/security/limits.conf

echo '* hard memlock unlimited' >> /etc/security/limits.conf

reboot

在集群中的其他节点上部署操作系统

配置环境变量

步骤1. 登录到管理节点。

步骤2. 编辑/root/lico_env.local 并更新该文件中列出的环境变量:

# Management node hostname

sms_name="head"

# IP address of management node in the cluster intranet

sms_ip="192.168.0.2"

# Network interface card MAC address corresponding to the management node IP

sms_mac='b8:59:9f:2b:a2:e2'

# Management node BMC address.

sms_bmc='192.168.1.2'

# set the dns server

dns_server="192.168.10.10"

# set the ipv4 gateway

ipv4_gateway="192.168.0.1"

# Set the domain name

domain_name="hpc.com"

# Set OpenLDAP domain name

lico_ldap_domain_name="dc=hpc,dc=com"

# set OpenLDAP domain component

lico_ldap_domain_component="hpc"

# original OS repository directory

repo_backup_dir="/install/custom/backup"

# OS image pathway

iso_path="/isos"

# Local repository directory for OS

os_repo_dir="/install/custom/server"

sdk_repo_dir="/install/custom/sdk"

# Local repository directory for confluent

confluent_repo_dir="/install/custom/confluent"

# link name of repository directory for Lenovo OpenHPC

link_ohpc_repo_dir="/install/custom/ohpc"

# link name of repository directory for LiCO

link_lico_repo_dir="/install/custom/lico"

# link name of repository directory for LiCO-dep

link_lico_dep_repo_dir="/install/custom/lico-dep"

# Local repository directory for Lenovo OpenHPC, please change it

# according to this version.

ohpc_repo_dir="/install/custom/ohpc-1.3.9"

# LiCO repository directory for LiCO, please change it according to this version.

lico_repo_dir="/install/custom/lico-7.0.0"

# LiCO repository directory for LiCO-dep, please change it according to this version.

lico_dep_repo_dir="/install/custom/lico-dep-7.0.0"

# icinga api listener port

icinga_api_port=5665

# If the confluence automatic discovery mode is enabled, skip the following configurations.

# Total compute nodes

num_computes="2"

# Prefix of compute node hostname.

# Change the configuration according to actual conditions.

compute_prefix="c"

# Compute node hostname list.

# Change the configuration according to actual conditions.

c_name[0]=c1

c_name[1]=c2

# Compute node IP list.

# Change the configuration according to actual conditions.

c_ip[0]=192.168.0.6

c_ip[1]=192.168.0.16

# Network interface card MAC address corresponding to the compute node IP.

# Change the configuration according to actual conditions.

c_mac[0]=fa:16:3e:73:ec:50

c_mac[1]=fa:16:3e:27:32:c6

# Compute node BMC address list.

c_bmc[0]=192.168.1.6

c_bmc[1]=192.168.1.16

# Total login nodes. If there is no login node in the cluster, or the management node

# and the login node is the same node, the number of logins must be "0".

# And the 'l_name', 'l_ip', 'l_mac', and 'l_bmc' lines need to be removed.

num_logins="1"

# Login node hostname list.

# Change the configuration according to actual conditions.

l_name[0]=l1

# Login node IP list.

# Change the configuration according to actual conditions.

l_ip[0]=192.168.0.15

# Network interface card MAC address corresponding to the login node IP.

# Change the configuration according to actual conditions.

l_mac[0]=fa:16:3e:2c:7a:47

# Login node BMC address list.

l_bmc[0]=192.168.1.15

步骤3. 将更改保存到lico_env.local,并重新加载环境变量:

chmod 600 lico_env.local

source lico_env.local

设置好集群环境后,在登录或管理节点上配置公共网络的IP 地址。这样即可从外部网络登录到LiCO Web 门户。

创建本地存储库

创建本地存储库以安装操作系统。

对于CentOS

步骤1. 运行以下命令以创建ISO 存储的目录:

mkdir -p ${iso_path}

步骤2. 从http://isoredirect.centos.org/centos/7/isos/x86_64/下载CentOS-7-x86_64-Everything-2009.iso 和sha256sum.txt 文件。

步骤3. 将文件拷贝到${iso_path}。

步骤4. 运行以下命令以获取该iso 文件的验证码,并确保此验证码与sha256sum.txt 中的验证

码相同。

cd ${iso_path}

sha256sum CentOS-7-x86_64-Everything-2009.iso

cd ~

步骤5. 运行以下命令以装载镜像:

mkdir -p ${os_repo_dir}

mount -o loop ${iso_path}/CentOS-7-x86_64-Everything-2009.iso ${os_repo_dir}

步骤6. 运行以下命令以配置本地存储库:

cat << eof > ${iso_path}/EL7-OS.repo

[EL7-OS]

name=el7-centos

enabled=1

gpgcheck=0

type=rpm-md

baseurl=file://${os_repo_dir}

eof

cp -a ${iso_path}/EL7-OS.repo /etc/yum.repos.d/

步骤7. 运行以下命令以关闭该存储库:

yum install --disablerepo=CentOS* -y yum-utils

yum-config-manager --disable CentOS\*

对于RHEL

步骤1. 运行以下命令以创建ISO 存储的目录:

mkdir -p ${iso_path}

步骤2. 将RHEL-7.9-20200917.0-Server-x86_64-dvd1.iso和RHEL-7.9-20200917.0-Server-x86_64-dvd1.iso.MD5SUM 文件拷贝到${iso_path} 目录。

步骤3. 运行以下命令以检查iso 文件的有效性:

cd ${iso_path}

md5sum -c RHEL-7.9-20200917.0-Server-x86_64-dvd1.iso.MD5SUM

cd ~

步骤4. 运行以下命令以装载镜像:

mkdir -p ${os_repo_dir}

mount -o loop ${iso_path}/RHEL-7.9-20200917.0-Server-x86_64-dvd1.iso ${os_repo_dir}

步骤5. 运行以下命令以配置本地存储库:

cat << eof > ${iso_path}/RHELS7-OS.repo

[RHELS7-OS]

name=RHELS7-OS

enabled=1

gpgcheck=0

type=rpm-md

baseurl=file://${os_repo_dir}

eof

cp -a ${iso_path}/RHELS7-OS.repo /etc/yum.repos.d/

安装Lenovo Confluent

步骤1. 下载以下软件包:

https://hpc.lenovo.com/downloads/22a/confluent-3.4.0-2-el7.tar.xz

步骤2. 将该软件包上传到/root 目录。

步骤3. 创建Confluent 本地存储库:

yum install -y bzip2 tar

mkdir -p $confluent_repo_dir

cd /root

tar -xvf confluent-3.4.0-2-el7.tar.xz -C $confluent_repo_dir

cd $confluent_repo_dir/lenovo-hpc-el7

./mklocalrepo.sh

cd ~

步骤4. 安装Lenovo Confluent:

yum install -y lenovo-confluent tftp-server

systemctl enable confluent --now

systemctl enable tftp.socket --now

systemctl disable firewalld --now

systemctl enable httpd --now

步骤5. 创建Confluent 帐户:

source /etc/profile.d/confluent_env.sh

confetty create /users/ password= role=admin

步骤6. 关闭SELinux:

sed -i 's/enforcing/disabled/' /etc/selinux/config

setenforce 0

通过Confluent 部署操作系统

注意:在Confluent 自动发现模式下部署集群时,请按照以下网站上的指导进行操作:

• https://hpc.lenovo.com/users/documentation/confluentdisco.html

• https://hpc.lenovo.com/users/documentation/confluentquickstart_el8.html

建议在Confluent 中创建名为“all”、“login”、“compute”等的组,并将节点与特定组绑定;否则,本指南其余章节中提到的命令可能会无法使用。

指定全局行为

注:在指定全局行为之前,请确保节点中的BMC 用户名和密码一致;如果不一致,则应进行修改。

在Confluent 中,大多数配置都是面向节点的,可从一个组派生出来。默认组“everything”会自动添加到每个节点中,该组可用于指示全局设置。

nodegroupattrib everything deployment.useinsecureprotocols=firmware \

console.method=ipmi dns.servers=$dns_server dns.domain=$domain_name \

net.ipv4_gateway=$ipv4_gateway net.ipv4_method="static"

deployment.useinsecureprotocols=firmware 会启用PXE 支持(“仅限HTTPS”的模式默认是唯一允许的模式),console.method=ipmi 可以被跳过,但如果指定它,则会指示Confluent 使用IPMI访问文本控制台以启用nodeconsole 命令。可以采用相同的方式指定密码和类似内容,并建议使用-p 参数来提示输入值,以避免它们出现在命令历史记录中。请注意,如果未指定,则默认的根用户密码行为是禁用基于密码的登录:

nodegroupattrib everything -p bmcuser bmcpass crypted.rootpassword

在Confluent 中定义节点

步骤1. 在lico_env.local 文件中定义要用于Confluent 的管理节点:

nodegroupdefine all

nodegroupdefine login

nodegroupdefine compute

nodedefine $sms_name

nodeattrib $sms_name net.hwaddr=$sms_mac

nodeattrib $sms_name net.ipv4_address=$sms_ip

nodeattrib $sms_name hardwaremanagement.manager=$sms_bmc

步骤2. 定义要用于Confluent 的计算节点配置:

for ((i=0; i<$num_computes; i++)); do

nodedefine ${c_name[$i]};

nodeattrib ${c_name[$i]} net.hwaddr=${c_mac[$i]};

nodeattrib ${c_name[$i]} net.ipv4_address=${c_ip[$i]};

nodeattrib ${c_name[$i]} hardwaremanagement.manager=${c_bmc[$i]};

nodedefine ${c_name[$i]} groups=all,compute;

done

步骤3. 定义要用于Confluent 的登录节点配置:

for ((i=0; i<$num_logins; i++)); do

nodedefine ${l_name[$i]};

nodeattrib ${l_name[$i]} net.hwaddr=${l_mac[$i]};

nodeattrib ${l_name[$i]} net.ipv4_address=${l_ip[$i]};

nodeattrib ${l_name[$i]} hardwaremanagement.manager=${l_bmc[$i]};

nodedefine ${l_name[$i]} groups=all,login;

done

准备名称解析

注:不强制要求使用特定的名称解析解决方案,但如果没有相应的策略,可通过以下步骤设定一个基本策略。

步骤1. 将节点信息附加到/etc/hosts:

for node_name in $(nodelist); do

noderun -n $node_name echo {net.ipv4_address} {node} {node}.{dns.domain} >> /etc/hosts

done

步骤2. 安装并启动到dnsmasq,创建可通过dns 使用的/etc/hosts:

yum install -y dnsmasq

systemctl enable dnsmasq --now

初始化Confluent 操作系统部署

用户可以通过osdeploy 命令的已初始化的子命令来设置操作系统部署要求。-i 参数用于交互式地提示可用的选项:

ssh-keygen -t ed25519

chown confluent /var/lib/confluent

osdeploy initialize -i

执行操作系统部署

对于Centos

步骤1. 导入安装介质:

osdeploy import ${iso_path}/CentOS-7-x86_64-Everything-2009.iso

步骤2. 开始部署:

nodedeploy all -n centos-7.9-x86_64-default

步骤3. (可选)查看部署进度:

nodedeploy all

对于RHEL

步骤1. 导入安装介质:

osdeploy import ${iso_path}/RHEL-7.9-20200917.0-Server-x86_64-dvd1.iso

步骤2. 开始部署:

nodedeploy all -n rhel-7.9-x86_64-default

步骤3. 检查部署过程:

nodedeploy all

为其他节点启用NGINX

注意:如果其他节点的操作系统是CentOS,请运行以下命令以关闭该存储库::

nodeshell all "yum install --disablerepo=CentOS* -y yum-utils"

nodeshell all "yum-config-manager --disable CentOS\*"

为其他节点禁用防火墙

nodeshell all "systemctl disable firewalld --now"

nodeshell all "sed -i 's/enforcing/disabled/' /etc/selinux/config"

nodeshell all "setenforce 0"

检查点A

检查并确保安装已完成:

nodeshell all uptime

注:输出应如下所示:

c1: 05:03am up 0:02, 0 users, load average: 0.20, 0.13, 0.05

c2: 05:03am up 0:02, 0 users, load average: 0.20, 0.14, 0.06

l1: 05:03am up 0:02, 0 users, load average: 0.17, 0.13, 0.05

……

为节点安装基础结构软件

注:在安装节点列中,M 代表“管理节点”,L 代表“登录节点”,C 代表“计算节点”。

软件名称

组件名称

版本

服务名称

安装节点

备注

nfs

nfs-utils

1.3.0

nfs-server

M

/

chrony

chrony

3.4

chronyd

M、C、L

/

slurm

ohpc-slurm-server

1.3.8

mung、slurmctld

M

/

ohpc-slurm-client

1.3.8

mung、slurmd

C、L

/

icinga2

icinga2

2.13.5

icinga2

M、C、L

/

singularity

singularity-ohpc

3.7.1

/

M

mpi

openmpi3-gnu8-ohpc

3.1.4

/

M

至少需要安装一种类型的MPI

mpich-gnu8-ohpc

3.3.1

/

M

mvapich2-gnu8-ohpc

2.3

/

M

为安装程序定义共享目录

以下步骤以/install/installer 为例,说明了如何为安装程序定义共享目录:

步骤1. 管理节点共享/install/installer:

yum install -y nfs-utils

systemctl enable nfs-server --now

share_installer_dir="/install/installer"

mkdir -p $share_installer_dir

echo "/install/installer *(rw,async,no_subtree_check,no_root_squash)" >> /etc/exports

exportfs -a

步骤2. 分发/etc/hosts:

cp /etc/hosts $share_installer_dir

scp $share_installer_dir/hosts c1:/etc/ (注:c1为计算节点的hostname,如果存在多台计算节点,请将此文件拷贝到其它所有计算节点)

步骤3. 启用httpd 服务:

cat << eof > /etc/httpd/conf.d/installer.conf

Alias /install /install

AllowOverride None

Require all granted

Options +Indexes +FollowSymLinks

eof

systemctl restart httpd

注:/install is the basic directory for repository which configured in the lico_env.local

file.

步骤4.对于CentOS,运行以下命令:

cp /etc/yum.repos.d/EL7-OS.repo $share_installer_dir

sed -i '/^baseurl=/d' $share_installer_dir/EL7-OS.repo

echo "baseurl=http://${sms_name}${os_repo_dir}" >>$share_installer_dir/EL7-OS.repo

scp $share_installer_dir/EL7-OS.repo c1:/etc/yum.repos.d/ (注:c1为计算节点的hostname,如果存在多台计算节点,请将此文件拷贝到其它所有计算节点)

对于RHEL,运行以下命令:

cp /etc/yum.repos.d/RHELS7-OS.repo $share_installer_dir

sed -i '/^baseurl=/d' $share_installer_dir/RHELS7-OS.repo

echo "baseurl=http://${sms_name}${os_repo_dir}" >>$share_installer_dir/RHELS7-OS.repo

scp $share_installer_dir/RHELS7-OS.repo c1:/etc/yum.repos.d/ (注:c1为计算节点的hostname,如果存在多台计算节点,请将此文件拷贝到其它所有计算节点)

步骤5. 启用存储库:

nodeshell all yum clean all

nodeshell all yum makecache

步骤6. 为集群节点安装NFS:

nodeshell all yum install -y nfs-utils

步骤7. 为集群节点配置该共享目录:

nodeshell all mkdir -p $share_installer_dir

nodeshell all "echo '${sms_ip}:/install/installer /install/installer \

nfs nfsvers=4.0,nodev,nosuid,noatime 0 0' >> /etc/fstab"

步骤8. 装载共享目录:

nodeshell all mount /install/installer

为其他节点配置内存

步骤1. 运行以下命令:

cp /etc/security/limits.conf $share_installer_dir

nodeshell all cp $share_installer_dir/limits.conf /etc/security/limits.conf

nodeshell all reboot

步骤2. 检查并确保安装已完成:

nodeshell all uptime

为管理节点配置本地yum 存储库

步骤1. 从https://hpc.lenovo.com/lico/downloads/5.5/Lenovo-OpenHPC-1.3.9.CentOS_7.x86_64.tar 下载包。

步骤2. 将包上传到管理节点上的/root directory 目录。

步骤3. 运行以下命令以配置本地Lenovo OpenHPC 存储库:

mkdir -p $ohpc_repo_dir

cd /root

tar xvf Lenovo-OpenHPC-1.3.9.CentOS_7.x86_64.tar -C $ohpc_repo_dir

rm -rf $link_ohpc_repo_dir

ln -s $ohpc_repo_dir $link_ohpc_repo_dir

$link_ohpc_repo_dir/make_repo.sh

为登录和计算节点配置本地yum 存储库

步骤1. 运行以下命令以添加本地存储库:

cp /etc/yum.repos.d/Lenovo.OpenHPC.local.repo $share_installer_dir

sed -i '/^baseurl=/d' $share_installer_dir/Lenovo.OpenHPC.local.repo

sed -i '/^gpgkey=/d' $share_installer_dir/Lenovo.OpenHPC.local.repo

echo "baseurl=http://${sms_name}${link_ohpc_repo_dir}/CentOS_7" \

>> $share_installer_dir/Lenovo.OpenHPC.local.repo

echo "gpgkey=http://${sms_name}${link_ohpc_repo_dir}/CentOS_7\

/repodata/repomd.xml.key" >> $share_installer_dir/Lenovo.OpenHPC.local.repo

步骤2. 运行以下命令为其他节点分发文件:

nodeshell all cp $share_installer_dir/Lenovo.OpenHPC.local.repo \

/etc/yum.repos.d/

nodeshell all "echo -e %_excludedocs 1 >> ~/.rpmmacros"

配置LiCO 依赖项存储库

步骤1. 下载以下软件包:

https://hpc.lenovo.com/lico/downloads/7.0/lico-dep-7.0.0.el7.x86_64.tgz

步骤2. 将该软件包上传到/root 目录。

步骤3. 为管理节点配置存储库:

mkdir -p $lico_dep_repo_dir

cd /root

tar -xvf lico-dep-7.0.0.el7.x86_64.tgz -C $lico_dep_repo_dir

rm -rf $link_lico_dep_repo_dir

ln -s $lico_dep_repo_dir $link_lico_dep_repo_dir

$link_lico_dep_repo_dir/mklocalrepo.sh

注意:在运行这些命令之前,需确保已事先为上述操作和后续操作在管理节点中配置了本地操作系统存储库。

步骤4. (可选)如果集群已存在,请检查您的版本

步骤5. 为其他节点配置存储库:

cp /etc/yum.repos.d/lico-dep.repo $share_installer_dir

sed -i '/^baseurl=/d' $share_installer_dir/lico-dep.repo

sed -i '/^gpgkey=/d' $share_installer_dir/lico-dep.repo

sed -i "/name=lico-dep-local-library/a\baseurl=http://${sms_name}\

${link_lico_dep_repo_dir}/library/" $share_installer_dir/lico-dep.repo

sed -i "/name=lico-dep-local-library/a\gpgkey=http://${sms_name}\

${link_lico_dep_repo_dir}/RPM-GPG-KEY-LICO-DEP-EL7" $share_installer_dir/lico-dep.repo

sed -i "/name=lico-dep-local-standalone/a\baseurl=http://${sms_name}\

${link_lico_dep_repo_dir}/standalone/" $share_installer_dir/lico-dep.repo

sed -i "/name=lico-dep-local-standalone/a\gpgkey=http://${sms_name}\

${link_lico_dep_repo_dir}/RPM-GPG-KEY-LICO-DEP-EL7" $share_installer_dir/lico-dep.repo

nodeshell all cp $share_installer_dir/lico-dep.repo /etc/yum.repos.d

获取LiCO 安装包

步骤1. 请与联想销售或支持人员联系获取EL7 的LiCO 7.0.0 发布包lico-release-7.0.0.el7.x86_64.tar.gz 和LiCO许可证文件。

步骤2. 将该发布包上传到管理节点。

为LiCO 配置本地存储库

步骤1. 为管理节点配置本地存储库:

mkdir -p $lico_repo_dir

tar zxvf lico-release-7.0.0.el7.x86_64.tar.gz -C $lico_repo_dir --strip-components 1

rm -rf $link_lico_repo_dir

ln -s $lico_repo_dir $link_lico_repo_dir

$link_lico_repo_dir/mklocalrepo.sh

步骤2. 为其他节点配置本地yum 存储库:

cp /etc/yum.repos.d/lico-release.repo $share_installer_dir

sed -i '/baseurl=/d' $share_installer_dir/lico-release.repo

sed -i "/name=lico-release-host/a\baseurl=http://${sms_name}\

${link_lico_repo_dir}/host/" $share_installer_dir/lico-release.repo

sed -i "/name=lico-release-public/a\baseurl=http://${sms_name}\

${link_lico_repo_dir}/public/" $share_installer_dir/lico-release.repo

步骤3. 分发存储库文件:

nodeshell all cp $share_installer_dir/lico-release.repo /etc/yum.repos.d/

配置Confluent 本地存储库

步骤1. 为其他节点配置本地存储库:

cp /etc/yum.repos.d/lenovo-hpc.repo $share_installer_dir

sed -i '/^baseurl=/d' $share_installer_dir/lenovo-hpc.repo

sed -i '/^gpgkey=/d' $share_installer_dir/lenovo-hpc.repo

echo "baseurl=http://${sms_name}${confluent_repo_dir}/lenovo-hpc-el7" \

>> $share_installer_dir/lenovo-hpc.repo

echo "gpgkey=http://${sms_name}${confluent_repo_dir}/lenovo-hpc-el7\

/lenovohpckey.pub" >> $share_installer_dir/lenovo-hpc.repo

步骤2. 分发存储库文件:

nodeshell all cp $share_installer_dir/lenovo-hpc.repo /etc/yum.repos.d/

安装Slurm

步骤1. 安装基础包:

yum install -y lenovo-ohpc-base

步骤2. 安装Slurm:

yum install -y ohpc-slurm-server

步骤3. 安装Slurm 客户端:

nodeshell all yum install -y ohpc-base-compute ohpc-slurm-client lmod-ohpc

步骤4. (可选)防止以非根用户身份登录到计算节点:

nodeshell compute "echo 'account required pam_slurm.so' >> /etc/pam.d/sshd"

注:To allow non-root logins to the compute nodes regardless of whether a Slurm

job is running on these nodes, skip this step. If this step is performed, the non-root

logins to the compute nodes will only be allowed when a Slurm job is running on

these nodes under a particular username. In this case, non-root ssh logins will work

for that particular username in this process.

步骤5. (可选)要保存以前的作业信息并使用内存计费功能,请参考以下信息来安装和配置slurm计费功能:

https://slurm.schedmd.com/accounting.html

配置NFS

配置用户共享目录

以下步骤以/home 为例说明了如何创建用户共享目录。

步骤1. 管理节点共享/home:

echo "/home *(rw,async,no_subtree_check,no_root_squash)" >> /etc/exports

exportfs -a

步骤2. 卸载已装载的/home:

nodeshell all "sed -i '/ \/home /d' /etc/fstab"

nodeshell all umount /home

步骤3. 为集群节点配置该共享目录:

nodeshell all "echo '${sms_ip}:/home /home nfs nfsvers=4.0,nodev,nosuid,noatime \

0 0' >> /etc/fstab"

步骤4. 装载共享目录:

nodeshell all mount /home

为OpenHPC 配置共享目录

步骤1. 管理用于OpenHPC 的节点共享/opt/ohpc/pub:

echo "/opt/ohpc/pub *(ro,no_subtree_check,fsid=11)" >> /etc/exports

exportfs -a

步骤2. 为集群节点配置共享目录:

nodeshell all mkdir -p /opt/ohpc/pub

nodeshell all "echo '${sms_ip}:/opt/ohpc/pub /opt/ohpc/pub nfs \

nfsvers=4.0,nodev,noatime 0 0' >> /etc/fstab"

步骤3. 装载共享目录:

nodeshell all mount /opt/ohpc/pub

注意:This directory is mandatory. If you have shared this directory from the

management node and mounted it on all other nodes, skip this step.

为监控配置监控目录

Step 1. 管理节点共享 /opt/lico/pub:

mkdir -p /opt/lico/pub

echo "/opt/lico/pub *(ro,sync,no_subtree_check,no_root_squash)" >> /etc/exports

exportfs -a

Step 2. 为集群节点配置共享目录:

nodeshell all mkdir -p /opt/lico/pub

nodeshell all "echo '${sms_ip}:/opt/lico/pub /opt/lico/pub nfs nfsvers=4.0,nodev,noatime \

0 0' >> /etc/fstab"

Step 3. 挂载共享目录:

nodeshell all mount /opt/lico/pub

配置Chrony

注:如果已为集群中的节点配置Chrony 服务,请跳过本节。

步骤1. 安装Chrony:

yum install -y chrony

步骤2. 集群时间不同步可能会导致意外问题。请参考以下信息来配置chronyd 服务:

https://chrony.tuxfamily.org/documentation.html

安装GPU 驱动程序

应在每个GPU 计算节点上安装GPU 驱动程序。如果只有部分节点安装了GPU,请将nodeshell

命令中的compute 参数替换为GPU 节点对应的节点范围。

禁用Nouveau 驱动程序

要安装显示驱动程序,请先禁用Nouveau 驱动程序。

步骤1. 将操作系统配置为在文本控制台上启动,然后重新启动系统:

注:仅当操作系统配置为在图形桌面上启动时才需要执行此步骤。

nodeshell compute systemctl set-default multi-user.target

步骤2. 添加配置文件:

cat << eof > $share_installer_dir/blacklist-nouveau.conf

blacklist nouveau

options nouveau modeset=0

eof

步骤3. 分发配置文件:

nodeshell compute cp $share_installer_dir/blacklist-nouveau.conf \

/usr/lib/modprobe.d/blacklist-nouveau.conf

步骤4. 重新生成内核initramfs:

nodeshell compute dracut --force

步骤5. 使配置生效:

nodeshell compute reboot

安装GPU 驱动程序

步骤1. 从https://us.download.nvidia.com/tesla/520.61.07/NVIDIA-Linux-x86_64-520.61.07.run,下载NVIDIA 驱动程序,并将其复制到共享目录$share_installer_dir。

步骤2. 运行以下命令:

yum install -y tar bzip2 make automake gcc gcc-c++ pciutils \

elfutils-libelf-devel libglvnd-devel

yum install -y kernel-devel-$(uname -r) kernel-headers-$(uname -r)

chmod +x $share_installer_dir/NVIDIA-Linux-x86_64-520.61.07.run

$share_installer_dir/NVIDIA-Linux-x86_64-520.61.07.run --add-this-kernel -s

nodeshell compute $share_installer_dir/NVIDIA-Linux-x86_64-520.61.07-custom.run -s

步骤3. 在GPU 节点上运行以下命令来确定是否可以识别到GPU:

nodeshell compute nvidia-smi

注:如果运行该命令无法识别GPU 信息,请重启所有GPU 节点。然后重新运行该命令。

nodeshell compute reboot

为GPU 驱动程序配置自动启动

步骤1. 添加配置文件:

cat << eof > $share_installer_dir/nvidia-persistenced.service

[Unit]

Description=NVIDIA Persistence Daemon

After=syslog.target

[Service]

Type=forking

PIDFile=/var/run/nvidia-persistenced/nvidia-persistenced.pid

Restart=always

ExecStart=/usr/bin/nvidia-persistenced --verbose

ExecStopPost=/bin/rm -rf /var/run/nvidia-persistenced/*

TimeoutSec=300

[Install]

WantedBy=multi-user.target

eof

cat << eof > $share_installer_dir/nvidia-modprobe-loader.service

[Unit]

Description=NVIDIA ModProbe Service

After=syslog.target

Before=slurmd.service

[Service]

Type=oneshot

ExecStart=/usr/bin/nvidia-modprobe -u -c=0

RemainAfterExit=yes

[Install]

WantedBy=multi-user.target

eof

步骤2. 分发配置文件:

nodeshell compute cp $share_installer_dir/nvidia-persistenced.service \

/usr/lib/systemd/system/nvidia-persistenced.service

nodeshell compute cp $share_installer_dir/nvidia-modprobe-loader.service \

/usr/lib/systemd/system/nvidia-modprobe-loader.service

nodeshell compute mkdir -p /var/run/nvidia-persistenced

步骤3. 重新启动服务:

nodeshell compute systemctl daemon-reload

nodeshell compute systemctl enable nvidia-persistenced --now

nodeshell compute systemctl enable nvidia-modprobe-loader.service --now

配置Slurm

步骤1. 从以下网站下载slurm.conf:

https://hpc.lenovo.com/lico/downloads/7.0/examples/conf/

步骤2. 将slurm.conf 上传到$share_installer_dir,并根据配置“slurm.conf”中的说明修改此文件。

步骤3. 从以下网站下载cgroup.conf:

https://hpc.lenovo.com/lico/downloads/7.0/examples/conf/

步骤4. 将cgroup.conf 上传到$share_installer_dir。

步骤5. 分发配置:

cp $share_installer_dir/slurm.conf /etc/slurm/slurm.conf

nodeshell all cp $share_installer_dir/slurm.conf /etc/slurm/slurm.conf

cp $share_installer_dir/cgroup.conf /etc/slurm/cgroup.conf

nodeshell all cp $share_installer_dir/cgroup.conf /etc/slurm/cgroup.conf

cp /etc/munge/munge.key $share_installer_dir

nodeshell all cp $share_installer_dir/munge.key /etc/munge/munge.key

步骤6. (可选)仅适用于GPU 节点:

• 如果在GPU 节点中启用了Nvidia MIG,请配置GPU 节点。有关更多信息,请参阅:https://gitlab.com/nvidia/hpc/slurm-mig-discovery

• 如果在GPU 节点中禁用或不支持Nvidia MIG,请从https://hpc.lenovo.com/lico/downloads/7.0/examples/conf/ 下载示例文件gres.conf,根据您的需要编辑该示例文件,然后将其上传到GPU 节点上的/etc/slurm 中。

步骤7. 启动服务:

systemctl enable munge

systemctl enable slurmctld

systemctl restart munge

systemctl restart slurmctld

步骤8. 启动其他节点服务:

nodeshell all systemctl enable munge

nodeshell all systemctl restart munge

nodeshell all systemctl enable slurmd

nodeshell all systemctl restart slurmd

配置slurm.conf

需要配置的典型字段如下:

• 集群名称:

ClusterName=mycluster

• 管理节点名称:

SlurmctldHost=c031

• GPU 调度:

GresTypes=gpu

注:在集群中,仅当包含GPU 节点时才需要使用此条目。如果集群不包含GPU 节点,请删除此条目。

• 集群节点定义:

NodeName=c031 Gres=gpu:4 CPUs=28 RealMemory=200000 State=UNKNOWN

NodeName=c032 Gres=gpu:4 CPUs=28 RealMemory=200000 State=UNKNOWN

– Gres:GPU 数量

– CPUs:节点上的CPU 数量。

– RealMemory:节点的内存大小(单位:M)。

• 分区定义:

PartitionName=compute Nodes=c0[31-32] Default=YES MaxTime=INFINITE State=UP

PartitionName=compute1 Nodes=c0[31-32] Default=NO MaxTime=INFINITE State=UP

注:

– Default:标识此分区是否为默认分区。提交作业时,可以选择一个分区。如果不选择分区,则会使用默认分区。

– Nodes : NodeName 列表。如果NodeName 无规律, 允许Nodes=[nodename1,nodename2,...]

• 强制实施分区限制定义:

EnforcePartLimits=ALL

注意:如果要在作业请求的资源超过集群资源量时提交直接错误响应,请使用此配置。否则,作业将保留在队列中。有关如何配置slurm.conf 的更多详细信息,请参阅Slurm 官方网站:

https://slurm.schedmd.com/slurm.conf.html

gres.conf

该配置文件描述了安装在GPU 节点上的GPU 和GPU 内存。此文件的内容可能因GPU 节点而异。

请修改以下内容:

Name=gpu File=/dev/nvidia[0-3]

注:应该将/dev/nvidia[0-3] 中的[0–3] 更改为您的实际GPU 配置。例如,/dev/nvidia0 表示一张GPU 卡,而/dev/nvidia[0-1] 则表示两张GPU 卡。

(可选)安装Icinga2

注:如果没有使用LiCO 来监控集群,请跳过本节。

如果已准备好IB 设备,并且需要安装IB 驱动程序,请在安装Icinga2 之前参考LeSI 22A_SI 最佳配置在操作系统中安装IB 驱动程序。USB 网卡会影响MPI 调用的IB 网卡。因此,建议在开机过程中添加“rmmod cdc_ether”,以删除USB 网卡。

步骤1. 安装icinga2:

yum install -y icinga2

nodeshell all yum install -y icinga2

步骤2. 安装LiCO icinga2 插件:

yum install -y nagios-plugins-ping lico-icinga-plugin-slurm

步骤3. 打开API 功能:

icinga2 api setup

步骤4. 配置icinga2:

icinga2 node setup --master --disable-confd

echo -e "LANG=en_US.UTF-8" >> /etc/sysconfig/icinga2

systemctl restart icinga2

步骤5. 为其他节点配置icinga2 代理:

nodeshell all icinga2 pki save-cert --trustedcert \

/var/lib/icinga2/certs/trusted-parent.crt --host ${sms_name}

for ((i=0;i<$num_computes;i++));do

ticket=`icinga2 pki ticket --cn ${c_name[${i}]}`

nodeshell ${c_name[${i}]} icinga2 node setup --ticket ${ticket} --cn ${c_name[${i}]} \

--endpoint ${sms_name} --zone ${c_name[${i}]} --parent_zone master --parent_host \

${sms_name} --trustedcert /var/lib/icinga2/certs/trusted-parent.crt \

--accept-commands --accept-config --disable-confd

done

for ((i=0;i<$num_logins;i++));do

ticket=`icinga2 pki ticket --cn ${l_name[${i}]}`

nodeshell ${l_name[${i}]} icinga2 node setup --ticket ${ticket} --cn ${l_name[${i}]} \

--endpoint ${sms_name} --zone ${l_name[${i}]} --parent_zone master --parent_host \

${sms_name} --trustedcert /var/lib/icinga2/certs/trusted-parent.crt \

--accept-commands --accept-config --disable-confd

done

nodeshell all "echo -e 'LANG=en_US.UTF-8' >> /etc/sysconfig/icinga2"

nodeshell all systemctl restart icinga2

步骤6. 在管理节点上配置全局模板文件:

mkdir -p /etc/icinga2/zones.d/global-templates

echo -e "object CheckCommand \"lico_monitor\" {\n command = [ \"/opt/lico/pub/monitor/\

lico_icinga_plugin/lico-icinga-plugin\" ]\n}" > /etc/icinga2/zones.d/global-templates/commands.conf

echo -e "object CheckCommand \"lico_job_monitor\" {\n command = [\"/opt/lico/pub/monitor/\

lico_icinga_plugin/lico-job-icinga-plugin\" ]\n}" >> /etc/icinga2/zones.d/global-\

templates/commands.conf

echo -e "object CheckCommand \"lico_check_procs\" {\n command =[ \"/opt/lico/pub/monitor\

/lico_icinga_plugin/lico-process-icinga-plugin\" ]\n}" >>/etc/icinga2/zones.d/global-\

templates/commands.conf

echo -e "object CheckCommand \"lico_vnc_monitor\" {\n command =[ \"/opt/lico/pub/monitor/\

lico_icinga_plugin/lico-vnc-icinga-plugin\" ]\n}" >> /etc/icinga2/zones.d/global-\

templates/commands.conf

chown -R icinga:icinga /etc/icinga2/zones.d/global-templates

步骤7. 定义区域文件:

mkdir -p /etc/icinga2/zones.d/master

echo -e "object Host \"${sms_name}\" {\n check_command = \"hostalive\"\n \

address = \"${sms_ip}\"\n vars.agent_endpoint = name\n}\n" >> \

/etc/icinga2/zones.d/master/hosts.conf

for ((i=0;i<$num_computes;i++));do

echo -e "object Endpoint \"${c_name[${i}]}\" {\n host = \"${c_name[${i}]}\"\n \

port = \"${icinga_api_port}\"\n log_duration = 0\n}\nobject \

Zone \"${c_name[${i}]}\" {\n endpoints = [ \"${c_name[${i}]}\" ]\n \

parent = \"master\"\n}\n" >> /etc/icinga2/zones.d/master/agent.conf

echo -e "object Host \"${c_name[${i}]}\" {\n check_command = \"hostalive\"\n \

address = \"${c_ip[${i}]}\"\n vars.agent_endpoint = name\n}\n" >> \

/etc/icinga2/zones.d/master/hosts.conf

done

for ((i=0;i<$num_logins;i++));do

echo -e "object Endpoint \"${l_name[${i}]}\" {\n host = \"${l_name[${i}]}\"\n \

port = \"${icinga_api_port}\"\n log_duration = 0\n}\nobject \

Zone \"${l_name[${i}]}\" {\n endpoints = [ \"${l_name[${i}]}\" ]\n \

parent = \"master\"\n}\n" >> /etc/icinga2/zones.d/master/agent.conf

echo -e "object Host \"${l_name[${i}]}\" {\n check_command = \"hostalive\"\n \

address = \"${l_ip[${i}]}\"\n vars.agent_endpoint = name\n}\n" >> \

/etc/icinga2/zones.d/master/hosts.conf

done

echo -e "apply Service \"lico\" {\n check_command = \"lico_monitor\"\n \

max_check_attempts = 5\n check_interval = 1m\n retry_interval = 30s\n assign \

where host.name == \"${sms_name}\"\n assign where host.vars.agent_endpoint\n \

command_endpoint = host.vars.agent_endpoint\n}\n" > \

/etc/icinga2/zones.d/master/service.conf

echo -e "apply Service \"lico-procs-service\" {\n check_command = \"lico_\

check_procs\"\n enable_active_checks = false\n assign where \

host.name == \"${sms_name}\"\n assign where host.vars.agent_endpoint\n \

command_endpoint = host.vars.agent_endpoint\n}\n" >> \

/etc/icinga2/zones.d/master/service.conf

echo -e "apply Service \"lico-job-service\" {\n check_command = \"lico_job_monitor\"\n \

max_check_attempts = 5\n check_interval = 1m\n retry_interval = 30s\n assign \

where host.name == \"${sms_name}\"\n assign where host.vars.agent_endpoint\n \

command_endpoint = host.vars.agent_endpoint\n}\n" >> \

/etc/icinga2/zones.d/master/service.conf

echo -e "apply Service \"lico-vnc-service\" {\n check_command = \"lico_vnc_monitor\"\n \

max_check_attempts = 5\n check_interval = 15s\n retry_interval = 30s\n assign \

where host.name == \"${sms_name}\"\n assign where host.vars.agent_endpoint\n \

command_endpoint = host.vars.agent_endpoint\n}\n" >> \

/etc/icinga2/zones.d/master/service.conf

chown -R icinga:icinga /etc/icinga2/zones.d/master

chmod u+s /opt/lico/pub/monitor/lico_icinga_plugin/lico-vnc-icinga-plugin

systemctl restart icinga2

步骤8. 启用服务:

nodeshell all modprobe ipmi_devintf

nodeshell all systemctl enable icinga2

modprobe ipmi_devintf

systemctl enable icinga2

步骤9. (可选)检查配置:

icinga2 daemon -C

安装MPI

步骤1. 运行以下命令以将三个模块(OpenMPI、MPICH 和MVAPICH)安装到系统:

yum install -y openmpi3-gnu8-ohpc mpich-gnu8-ohpc mvapich2-gnu8-ohpc

步骤2. 设置默认模块。

运行以下命令以将OpenMPI 模块设置为默认模块:

yum install -y lmod-defaults-gnu8-openmpi3-ohpc

运行以下命令以将MPICH 模块设置为默认模块:

yum install -y lmod-defaults-gnu8-mpich-ohpc

运行以下命令以将MVAPICH 模块设置为默认模块:

yum install -y lmod-defaults-gnu8-mvapich2-ohpc

注:MVAPICH 要求Infiniband 或OPA 存在且工作正常。应安装以下包以支持Infiniband

或OPA:

yum list installed libibmad5 librdmacm1 rdma infinipath-psm dapl-devel \

dapl-utils libibverbs-utils

MPI 类型之间的依赖关系

安装MPI 时,请遵循以下依赖关系:

• 要使用MVAPICH2(psm2),请安装mvapich2-psm2-gnu8-ohpc。

• 要使用OpenMPI(PMIx),请安装openmpi3-pmix-slurm-gnu8-ohpc。

• openmpi3-gnu8-ohpc 与openmpi3-pmix-slurm-gnu8-ohpc 不兼容。

• mvapich2-psm2-gnu8-ohpc 与mvapich2-gnu8-ohpc 不兼容。

安装Singularity

Singularity 是一个面向HPC 的轻型容器框架。

步骤1. 运行以下命令安装Singularity:

yum install -y singularity-ohpc

步骤2. 通过将以下内容添加到module try-add 区块的末尾以编辑

/opt/ohpc/pub/modulefiles/ohpc:

module try-add singularity

步骤3. 在module del 区块中,添加以下内容作为第一行:

module del singularity

步骤4. 运行以下命令:

source /etc/profile.d/lmod.sh

如果默认模块随着lmod-defaults* 包的安装发生了更改,对/opt/ohpc/pub/modulefiles/ohpc

所做的更改可能会丢失。此情况下, 请再次修改/opt/ohpc/pub/modulefiles/ohpc , 或在

/etc/profile.d/lmod.sh 的末尾添加module try-add singularity。

检查点B

步骤1. 运行以下命令以测试Slurm 是否已正确安装:

sinfo

注:

• 输出应如下所示:

PARTITION AVAIL TIMELIMIT NODES STATE NODELIST

normal* up 1-00:00:00 2 idle c[1-2]

……

• 所有节点的状态应为idle;不接受idle*。

步骤2. 运行以下命令以添加test 帐户:

useradd test -m --uid 65530

nodeshell all useradd test -m --uid 65530

步骤3. 使用测试帐户和Slurm 分发的测试程序登录到计算节点:

su - test

mpicc -O3 /opt/ohpc/pub/examples/mpi/hello.c

srun -n 8 -N 1 -w -p --pty /bin/bash

prun ./a.out

注:输出应如下所示:

Master compute host = c1

Resource manager = slurm

Launch cmd = mpiexec.hydra -bootstrap slurm ./a.out

Hello, world (8 procs total)

--> Process # 0 of 8 is alive. -> c1

--> Process # 4 of 8 is alive. -> c2

--> Process # 1 of 8 is alive. -> c1

--> Process # 5 of 8 is alive. -> c2

--> Process # 2 of 8 is alive. -> c1

--> Process # 6 of 8 is alive. -> c2

--> Process # 3 of 8 is alive. -> c1

--> Process # 7 of 8 is alive. -> c2

步骤4. 结束测试:

exit

注:要离开“test”用户会话,请再次输入“exit”。

步骤5. 删除该test 用户:

nodeshell all userdel test

userdel test -r

命令执行完毕后,您将切换为管理节点的根用户。

第3 章安装LiCO 依赖项

集群检查

检查环境变量smsn​ame、{lico_ldap_domain_name} 和${lico_repo_dir}

echo $sms_name;echo $lico_repo_dir;echo $lico_ldap_domain_name

注:

• 输出应如下所示:

head

/install/custom/lico-7.0.0

dc=hpc,dc=com

检查安装程序的共享目录

检查共享目录$share_installer_dir:

echo $share_installer_dir

注:

• 输出应如下所示:

/install/installer

检查LiCO 依赖项存储库:

yum repolist | grep lico-dep-local

注:

• 输出应如下所示:

lico-dep-local-library lico-dep-local-library 234

lico-dep-local-standalone lico-dep-local-standalone 108

检查LiCO 存储库:

yum repolist | grep lico-release

lico-release-host lico-release-host 82

lico-release-public lico-release-public 42

检查NFS

注:如果集群不使用NFS 作为分布式文件系统,请跳过本节。

检查NFS 服务:

systemctl status nfs-server | grep Active && exportfs -v | grep -E '/home|/opt/ohpc/pub'

注:

• 输出应如下所示:

Active: active (exited) since Sat 2019-10-12 16:04:21 CST; 2 days ago

/opt/ohpc/pub (sync,wdelay,hide,no_subtree_check,sec=sys,ro,secure,root_squash,no_all_

squash)

/home (async,wdelay,hide,no_subtree_check,sec=sys,rw,secure,no_root_squash,no_all_squash)

请检查所有其他节点上的装载点:

nodeshell all "df | grep -E '/home | /opt/ohpc/pub'"

注:

• 输出应如下所示:

c1: 10.1.1.31:/home 485642240 111060992 374581248 23% /home

c1: 10.1.1.31:/opt/ohpc/pub 485642240 111060992 374581248 23% /opt/ohpc/pub

检查Slurm

检查slurmctld:

systemctl status slurmctld | grep Active

注:

• 输出应如下所示:

Active: active (running) since Tue 2018-07-24 19:02:49 CST; 1 months 20 days ago

检查计算节点上的slurmd:

nodeshell compute "systemctl status slurmd | grep Active"

注:

• 输出应如下所示:

c1: Active: active (running) since Tue 2018-07-24 19:02:49 CST; 1 months 20 days ago

c2: Active: active (running) since Sat 2018-07-21 17:16:59 CST; 1 months 23 days ago

检查MPI 和Singularity

module list

注:

• 输出应如下所示:

Currently Loaded Modules:

1) prun/1.3 2) gnu8/8.3.0 3) openmpi3/3.1.4 4) singularity/3.7.1 5) ohpc

安装LiCO 依赖项

注:在安装节点列中,M 代表“管理节点”,L 代表“登录节点”,C 代表“计算节点”。

软件

组件

版本

服务

安装节点

备注

rabbitmq

rabbitmq-server

3.9.10

rabbitmq-server

M

mariadb

mariadb-server

10.3.32

mariadb-server

M

influxdb

influxdb

1.8.10

influxdb

M

confluent

confluent

3.4.0

confluent

M

libuser

libuser

0.62

M

python3-libuser

0.62

M

安装RabbitMQ

LiCO 使用RabbitMQ 作为消息代理。

步骤1. 安装RabbitMQ:

yum install -y rabbitmq-server

步骤2. 启动RabbitMQ 服务:

systemctl enable rabbitmq-server --now

安装MariaDB

LiCO 使用MariaDB 作为数据存储的对象相关数据库。

步骤1. 安装MariaDB:

yum install -y mariadb-server mariadb-devel

步骤2. 启动MariaDB 服务:

systemctl enable mariadb --now

步骤3. 为LiCO 配置MariaDB:

注:安装lico-passwd-tool 时将使用此处的用户名和密码。因此,请在安装MariaDB 时

记录这些信息。

mysql

create database lico character set utf8 collate utf8_bin;

create user ''@'%' identified by '';

grant ALL on lico.* to ''@'%';

exit

步骤4. 配置MariaDB 限制:

sed -i "/\[mysqld\]/a\max-connections=1024" /etc/my.cnf.d/mariadb-server.cnf

mkdir /usr/lib/systemd/system/mariadb.service.d

cat << eof > /usr/lib/systemd/system/mariadb.service.d/limits.conf

[Service]

LimitNOFILE=10000

eof

systemctl daemon-reload

systemctl restart mariadb

安装InfluxDB

LiCO 使用InfluxDB 作为执行存储监控的时间序列数据库。

步骤1. 安装InfluxDB:

yum install -y influxdb

systemctl enable influxdb --now

步骤2. 创建InfluxDB 用户:

• 进入InfluxDB shell:

influx

• 创建数据库:

create database lico

• 使用数据库:

use lico

• 创建管理员用户时,请确保使用字符串密码:

create user with password '' with all privileges

• 退出InfluxDB shell:

exit

• 进行配置:

sed -i '/# auth-enabled = false/a\ auth-enabled = true' /etc/influxdb/config.toml

• 重新启动InfluxDB:

systemctl restart influxdb

配置用户认证

安装OpenLDAP

注:如果配置了OpenLDAP 或已在集群中使用其他认证服务,请跳过本节。OpenLDAP 是轻量级目录访问协议的开源版本。建议使用OpenLDAP 来管理用户。但是,LiCO也支持与Linux-PAM 兼容的其他认证服务。

步骤1. 安装OpenLDAP:

yum install -y slapd-ssl-config openldap-servers

步骤2. 修改配置文件:

sed -i "s/dc=hpc,dc=com/${lico_ldap_domain_name}/" /usr/share/openldap-servers/lico.ldif

sed -i "/dc:/s/hpc/${lico_ldap_domain_component}/" /usr/share/openldap-servers/lico.ldif

sed -i "s/dc=hpc,dc=com/${lico_ldap_domain_name}/" /etc/openldap/slapd.conf

slapadd -v -l /usr/share/openldap-servers/lico.ldif -f /etc/openldap/slapd.conf -b \

${lico_ldap_domain_name}

步骤3. 获取OpenLDAP 密钥:

slappasswd

步骤4. 编辑/etc/openldap/slapd.conf,将根用户密码设置为获得的密钥。

rootpw

步骤5. 更改配置文件的所属用户:

chown -R ldap:ldap /var/lib/ldap

chown ldap:ldap /etc/openldap/slapd.conf

步骤6. 编辑/etc/sysconfig/slapd 配置,并确保和如下配置一致。

SLAPD_URLS="ldapi:/// ldap:/// ldaps:///"

SLAPD_OPTIONS="-f /etc/openldap/slapd.conf"

步骤7. 启动OpenLDAP 服务:

systemctl enable slapd --now

步骤8. 验证该服务是否已启动:

systemctl status slapd

安装libuser

libuser 模块是OpenLDAP 的推荐工具包。该模块的安装是可选的。

步骤1. 安装libuser:

yum install -y libuser python3-libuser

步骤2. 从https://hpc.lenovo.com/lico/downloads/7.0/examples/conf/ 将libuser.conf 下载到管理节点上的/etc 中,然后按照文件中的说明修改此文件。

安装OpenLDAP-client

echo "TLS_REQCERT never" >> /etc/openldap/ldap.conf

cp /etc/openldap/ldap.conf $share_installer_dir

nodeshell all cp $share_installer_dir/ldap.conf /etc/openldap/ldap.conf

安装nss-pam-ldapd

nss-pam-ldapd 是一个名称服务交换模块和可插拔认证模块。LiCO 使用此模块来认证用户。

步骤1. 运行以下命令以在管理节点上安装nss-pam-ldapd:

yum install -y nss-pam-ldapd authconfig

authconfig --useshadow --usemd5 --enablemkhomedir --disablecache \

--enablelocauthorize --disablesssd --disablesssdauth \

--enableforcelegacy --enableldap --enableldapauth --disableldaptls \

--ldapbasedn=${lico_ldap_domain_name} --ldapserver="ldap://${sms_name}" --updateall

echo "rootpwmoddn uid=admin,${lico_ldap_domain_name}" >> /etc/nslcd.conf

systemctl enable nslcd

systemctl start nslcd

步骤2. 运行以下命令以在其他节点上安装nss-pam-ldapd:

nodeshell all yum install -y nss-pam-ldapd authconfig

nodeshell all authconfig --useshadow --usemd5 --enablemkhomedir \

--disablecache --enablelocauthorize --disablesssd \

--disablesssdauth --enableforcelegacy --enableldap --enableldapauth \

--disableldaptls --ldapbasedn="${lico_ldap_domain_name}" \

--ldapserver="ldap://${sms_name}" --updateall

nodeshell all echo "\""rootpwmoddn uid=admin,${lico_ldap_domain_name}"\"" \>\> /etc/nslcd.conf

nodeshell all systemctl enable nslcd

nodeshell all systemctl start nslcd

注:默认情况下,xCAT 部署操作系统将禁用SElinux。因此,运行authconfig 命令将输出一条参考消息,如“getsebool: SELinux is disabled”。您可以忽略此消息,其并不影响功能。

第4 章 安装LiCO

安装LiCO Core

注:在安装节点列中,M 代表“管理节点”,L 代表“登录节点”,C 代表“计算节点”。

表7. 要安装的LiCO 组件列表

组件

软件

版本

服务

安装节点

备注

lico-core

lico-core

7.0.0

lico

M

lico-portal

lico-portal

7.0.0

L

lico-core-extend

lico-confluent-proxy

1.2.1

M

lico-vnc-proxy

1.3.0

lico-vnc-proxy

M

lico-env

lico-ai-scripts

1.3.0

M

lico monitor

lico-icinga-mond

1.5.0

lico-icinga-mond

M

lico-icinga-plugin-slurm

1.5.0

M

lico alarm notification

lico-sms-agent

1.2.7

lico-sms-agent

M

如果需要通过短信发送警报,则是必需的

lico-wechat-agent

1.2.7

lico-wechat-agent

M

如果需要通过微信发送警报,则是必需的

lico-mail-agent

1.3.8

lico-mail-agent

M

如果需要通过电子邮件发送警报,则是必需的

lico manager

lico-file-manager

2.2.2

lico-file-manager

M

基本组件

lico-task

lico-async-task

1.1.2

lico-async-task、lico-async-task-proxy

M、L

步骤1. 根据需要执行以下操作之一:

• 要使用LiCO 进行集群监控,请按以下方式安装LiCO 模块:

对于Centos,安装lico-async-task之前需要配置源:

cat << eof >>${iso_path}/EL7-OS.repo

[EL7-extras]

name=el7-extras

enabled=1

baseurl=http://mirror.centos.org/centos/7/extras/x86_64/

eof

cp -a ${iso_path}/EL7-OS.repo /etc/yum.repos.d/

(可选)对于REHL,安装lico-async-task之前需要配置源:

cat << eof >>${iso_path}/RHELS7-OS.repo

[EL7-extras]

name=el7-extras

enabled=1

baseurl=http://mirror.centos.org/centos/7/extras/x86_64/

eof

cp -a ${iso_path}/RHELS7-OS.repo /etc/yum.repos.d/

步骤2.开始安装lico组件

yum clean all

yum makecache

yum install -y python3-cffi

yum install -y lico-core lico-file-manager lico-confluent-proxy \

lico-vnc-proxy lico-icinga-mond lico-async-task lico-service-tool \

lico-ai-scripts

步骤3. (可选)提供电子邮件、短息和微信服务:

yum install -y lico-mail-agent

yum install -y lico-sms-agent

yum install -y lico-wechat-agent

步骤4. 重新启动服务:

systemctl restart confluent

在登录节点上安装LiCO 模块:

nodeshell login yum install -y lico-workspace-skeleton lico-portal lico-service-tool

第5 章 配置LiCO

配置服务帐户

注:

• 本指南中配置了MariaDB、InfluxDB、Confluent 和LDAP 的用户名或密码。

• 从/etc/icinga2/conf.d/api-users.conf 文件中获取icinga2 的用户名和密码。

在管理节点上,使用工具lico-password-tool。

按照以下提示输入MariaDB、InfluxDB、Confluent、Icinga2 和LDAP 的用户名或密码:

lico-password-tool

为其他节点配置服务帐户:

nodeshell login mkdir -p /var/lib/lico/tool

cp /var/lib/lico/tool/.db $share_installer_dir

nodeshell login cp $share_installer_dir/.db /var/lib/lico/tool

配置集群节点

步骤1. 将集群信息导入系统:

cp /etc/lico/nodes.csv.example /etc/lico/nodes.csv

步骤2. 编辑集群信息文件:

vi /etc/lico/nodes.csv

注:建议您将此文件下载到本地计算机,并使用Excel 或其他表格编辑软件编辑该文件。之后,您可以将其上传到管理节点并覆盖原始文件。

机房信息

以下是机房信息表的示例。

表8. 机房信息表

room

name

location_description

Shanghai Solution Room

Shanghai Zhangjiang

在name 和location_description 字段中输入一个信息条目。

逻辑组信息

管理人员可以使用逻辑组将集群中的节点划分为不同的组。逻辑组不会影响对计算机资源的使用或

权限配置。

以下是逻辑组信息表的示例。

表9. 逻辑组信息表

group

name

login

您需要在name 字段中输入至少一个逻辑组名称。

机房行信息

机房行是指机房内的机架顺序。输入集群节点所在机架行的信息。

以下是机房行信息表的示例。

表10. 机房行信息表

row

name

index

belonging_room

row1

1

Shanghai Solution Room

请在以下字段中输入至少一个行信息条目:

• name:行名称(在同一个机房内必须是唯一的)

• index:行顺序(必须是正整数,并且在同一个机房内必须是唯一的)

• belonging_room:行所在机房的名称

注:请将此信息添加到机房信息表中。

机架信息

以下是机架信息表的示例。

表11. 机架信息表

rack

name

column

belonging_row

rack1

1

row1

请在以下字段中输入至少一个机架信息条目:

• name:机架名称(在同一个机房内必须是唯一的)

• column:机架位置列,也称为机架编号(必须是正整数,并且在同一行中必须是唯一的)

• belonging_row:机架所在行的名称

注:请将此信息添加到行信息表中。

机箱信息

如果集群中有机箱,请输入机箱信息。

以下是机箱信息表的示例。

表12. 机箱信息表

chassis

name

belonging_rack

location_u_in_rack

location_u_in_rack

chassis1

rack1

7

7X20

该表中的字段说明如下:

• name:机箱名称(在同一个机房内必须是唯一的)

• belonging_rack:机架位置名称(必须使用机架信息表中配置的名称)

• location_u_in_rack:机箱基座在机架中的位置(单位:U)。在标准机柜中,该值应该在1 到

42 之间。例如,机箱基座位于5U。

• machine_type:机箱类型

节点信息

在节点信息表中输入集群中所有节点的信息。由于宽度原因,示例节点信息表拆分为两个部分进

行显示。

表13. 节点信息表(第1 部分)

node

name

node-type

immip

hostip

machine_type

ipmi_user

head

head

10.240.212.13

127.0.0.1

7X58

表14. 节点信息表(第2 部分)

ipmi_pwd

belonging_rack

belonging_chassis

location_u

groups

rack1

2

login

字段的描述如下:

• name:节点主机名(不需要域名)

• nodetype:head 表示管理节点;login 表示登录节点;compute 表示计算节点。

• immip:节点BMC 系统的IP 地址

• hostip:主机网络上的节点的IP 地址

• machine_type:节点的产品名称

• ipmi_user:节点的XCC(BMC)帐户

• ipmi_pwd:节点的XCC(BMC)密码

• belonging_rack:节点所在机架的名称(需要将配置的名称添加到机架信息表中)。如果节点属于机箱,请将此字段留空。

• belonging_chassis:节点所在机箱的名称(需要将配置的名称添加到机箱信息表中)。如果节点属于机架,请将此字段留空。

• location_u:节点位置。如果节点位于机箱中,请输入节点所在的机箱插槽。如果节点位于机架

中,请输入节点基座在机架中的位置(单位:U)。

• groups:节点位置逻辑组的名称。一个节点可以属于多个逻辑组。组名应该用“;”分隔。请在逻辑组信息表中配置逻辑组名称。

配置通用资源

该模块仅在调度器为slurm 时执行。执行以下操作之一来配置通用资源:

• 如果默认没有配置通用资源,而GPU 资源位于集群中且需要计费,请执行如下命令:

cp /etc/lico/gres.csv.example /etc/lico/gres.csv

• 如果为Slurm 配置了其他通用资源,并且需要对这些资源进行计费,请执行以下命令:

vi /etc/lico/gres.csv

注:为确保历史计费信息的准确性,已从gres.csv 中删除的通用资源仍会保留在系统数据库中。

通用资源信息

以下是通用资源信息表的示例:

code

display_name

unit

gpu

GPU

card

在以下字段中输入至少一项通用资源信息:

• code:此代码应与调度器中定义的通用资源类型一致。如果已经按照本文档安装了LiCO,则

可以根据slurm.conf 中的GresTypes 配置填写该代码。

• display_name:LiCO 系统中显示的通用资源名称。建议使用有意义的显示名称。

• unit:资源单位。

集群服务列表

注:在安装节点列中,M 代表“管理节点”,L 代表“登录节点”,C 代表“计算节点”。

表15. 集群服务列表

软件

组件

服务

默认端口

安装节点

lico

lico-core

lico

18080/tcp

M

lico-confluent-proxy

18081/tcp

M

lico-vnc-proxy

lico-vnc-proxy

18082/tcp、18083/tcp

M

lico-vnc-mond

lico-vnc-mond

C

lico-sms-agent

lico-sms-agent

18092/tcp

M

lico-wechat-agent

lico-wechat-agent

18090/tcp

M

lico-mail-agent

lico-mail-agent

18091/tcp

M

lico-file-manager

lico-file-manager

18085/tcp

M

lico-async-task

lico-async-task-proxy

18086/tcp

M、L

lico-async-task

18084/tcp

M、L

lico依赖项

nginx

nginx

80/tcp、443/tcp

L、M

rabbitmq

rabbitmq-server

5762/tcp

M

mariadb

mariadb

3306/tcp

confluent

confluent

4005/tcp、13001/tcp

M

influxdb

influxdb

8086/tcp、8088/tcp

M

ldap

slapd

389/tcp、636/tcp

M

nslcd

M、C、L

集群

nfs

nfs

111/tcp、111/udp、2049/tcp、2049/udp

M

chrony

chronyd

M

slurm

munge

M、C

slurmctld

6817/tcp

M

slurmd

6818/tcp

C

icinga2

icinga2

5665/tcp、5665/udp

M、C、L

dns

named

53/udp

M

dhcp

dhcpd

67/udp

M

配置LiCO 组件

有关配置LiCO 的更多信息,请参阅:

https://hpc.lenovo.com/lico/downloads/7.0/configuration/host/configuration.html

lico-portal

为了防止https 和NGINX Web 服务器之间的冲突,可能需要修改安装了lico-portal 模块(该模

块为外部Web 服务提供不同的端口)的节点的一些路径文件。

/etc/nginx/nginx.conf

可编辑/etc/nginx/nginx.conf,将端口更改为8080:

listen 8080 default_server;

listen [::]:8080 default_server;

要隐藏服务器版本信息,请修改/etc/nginx/nginx.conf,将server_tokens 关闭:

http{

......

sendfile on;

server_tokens off;

……

}

/etc/nginx/conf.d/https.conf

可编辑/etc/nginx/conf.d/https.conf,将默认https 端口443 更改为另一个端口:

listen ssl http2;

注:请确保该端口未被其他应用程序使用且未被防火墙阻止。

/etc/nginx/conf.d/sites-available/lico.conf

可编辑/etc/nginx/conf.d/sites-available/lico.conf,将第一行替换为以下内容:

set $lico_host 127.0.0.1;

注:如果lico-portal 无法运行,可将127.0.0.1 更改为管理节点的IP 地址。

/etc/lico/portal.conf

可编辑/etc/lico/portal.conf,添加自定义快捷链接。请参阅/etc/lico/portal.conf.example 以了解配置格式。

初始化系统

初始化LiCO:

lico init

初始化云工具

初始化云工具:

lico cloudtool import -n 'CVAT' -c \

cvat -t cvat -p job_queue,cores_per_node,username,password,ram_size,share_dir

lico cloudtool import -n 'Jupyter Notebook' -c jupyter -t jupyter -p \

image_path,jupyter_cmd,password,job_queue,cores_per_node,gpu_per_node,check_timeout,run_time

lico cloudtool import -n 'RStudio Server' -c \

rstudio -t rstudio -p job_queue,cores_per_node,gpu_per_node,password,run_time

lico cloudtool import -n 'TigerVNC' -c \

tigervnc -t tigervnc -p job_queue,cores_per_node,gpu_per_node,runtime_id,password,run_time

初始化用户

完成以下步骤以初始化LiCO 用户:

步骤1. (可选)要使用LDAP 管理用户,请在LiCO 配置文件/etc/lico/lico.ini.d/user.ini 中找

到以下配置并将值更改为“true”:

USE_LIBUSER = false

步骤2. (可选)向LDAP 添加具有管理员权限的新用户:

luseradd -P

nodeshell all "su - -c whoami"

步骤3. 将用户导入LiCO:

lico import_user -u -r admin

第6 章启动并登录到LiCO

启动LiCO

步骤1. 在登录节点上启动LiCO 相关服务:

nodeshell login lico-service-tool enable

nodeshell login lico-service-tool start

步骤2. 在管理节点上启动LiCO 相关服务:

lico-service-tool enable

lico-service-tool start

-1.4

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联想集群超算LICO初次使用踩坑说明(遇到的错误,一些使用方法和singularity新建容器方法)_lenovo lico-CSDN博客

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联想集群超算LICO初次使用踩坑说明(遇到的错误,一些使用方法和singularity新建容器方法)_lenovo lico-CSDN博客

联想集群超算LICO初次使用踩坑说明(遇到的错误,一些使用方法和singularity新建容器方法)

最新推荐文章于 2022-05-18 10:27:11 发布

瞲_大河弯弯

最新推荐文章于 2022-05-18 10:27:11 发布

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联想集群超算LICO初次使用踩坑说明(遇到的错误,一些使用方法和singularity新建容器方法)

说明

本文主要写一些我这几天初次使用LICO的过程中遇到的一点问题和解决的办法,还有一些模块的规范使用方式,和一点自己的小经验,以后如果在使用过程中踩到其他的坑和一些模块的使用方式会慢慢进行更新。。希望对你有所帮助。 环境:LICO5.5 浏览器:Google浏览器 登录方式:英文登陆(有时中文登陆会遇到问题在进入singularity镜像时) 主要参考内容:《LiCO 5.5.0用户指南》

帮助:可以直接ctrl+F开启浏览器的查找关键字,来快速定位到想要的内容

建议新人前俩个章节一定要看下。

目录

遇到的错误(重要)联想标准AI中我使用的模块遇到的错误标准AI中我使用的模块遇到的错误专家模式下遇到的错误

通用的一些说明(重要)slurm命令简介Singularity创建镜像第一步 创建Singularity运行环境第二步 下载系统镜像并新建自己的镜像第三步 进入专家模式,并进入Singularity镜像交互界面总结

Lenovo Accelerated AI部分模块使用图象分类训练模块使用说明新建任务查看输出

图象分类预测模块使用说明新建任务查看输出

标准AI部分模块使用TensorFlow 单节点作业新建任务查看输出

TensorFlow 多节点作业Pytorch单节点作业新建任务查看输出

总结

遇到的错误(重要)

联想标准AI中我使用的模块遇到的错误

这个就不多写了,, 只要按照步骤来, 只要按照步骤来, 只要按照步骤来, 怎么都不会有错误

标准AI中我使用的模块遇到的错误

关于错误的详情,可以百度搜索csdn,但是解决错误的方法,如果你在这个博客内搜索,并有,建议按照这个写的方法解决,如果识别的话再搜别的,因为这里面会有我的一点点经验,并且有的错误解决方法我给出了我当时解决时的参考博客的连接。

ModuleNotFoundError: No module named ‘matplotlib’ 或 ‘xxxx’

解决办法: 方法1 最简单的,看代码中,如果improt的这个库没有用,那就直接不引用。 方法2 这个是我的方法,换一个包含这个库的镜像,可以自己制作,参看通用的一些说明(重要)章节或者联系管理员让管理员在系统镜像中安装该库(当然这个库要有通用性,,) 方法3 自己进入专家模式然后pip一下试试,反正我这里不行,没有读写权限是第一,第二识别不了pip指令。 SyntaxError: Non-ASCII character ‘\xe4’ in file“xxxx”,but no encoding declared; see"xxxx" 解决办法: 在需要运行的所有代码文件中的第一行加入:

#coding=utf-8

上面的是.py文件内我加入这一行就行了,其他文件要不要改变形式不知道。。 ModuleNotFoundError: No module named ‘torchvision.ops’

AssertionError: The NVIDIA driver on your system is too old (found version 10010).

解决办法: 注意这个要对镜像操作,参考通用的一些说明(重要)章节 cuda版本与pytorch版本不匹配的问题,详细解决点击下面参考链接,给出查看CUDA版本的方式,在镜像交互界面内完成:

对于我的CUDA我把以前的torch卸载了,然后安装下面的方法安装的:

pip install torch==1.2.0 torchvision==0.4.0 -f https://download.pytorch.org/whl/torch_stable.html

参考链接1 参考链接2

ValueError: invalid literal for int() with base 8: ‘build_te’ RuntimeError: model_data/yolo4_weights.pth is a zip archive (did you mean to use torch.jit.load()?) tarfile.ReadError: invalid header 解决办法: 这几个error是一起出现的,因为我的与训练的权重是在torch1.6训练的,但是在LiCo是用1.2版本跑的,所以就会这样可以改一行保存权重时的代码:

主要是改变这个参数:_use_new_zipfile_serialization=False

torch.save(model.state_dict(), model_cp,_use_new_zipfile_serialization=False)

参考链接

专家模式下遇到的错误

ERROR: Could not install packages due to an EnvironmentError: [Errno 30] Read-only file system:

解决办法: 权限不够,例如 pip install cv2,则改写为:

pip install cv2 --user

参考链接

通用的一些说明(重要)

写在前面

请运行的浏览器按照要求最好,不然会出现下面的错误: 这个图就是在QQ浏览器下,文件管理看不到文件,在提交作业的时候也就这个,看不到文件夹 多看.out输出文件 多看.out输出文件 多看.out输出文件 里面会有成功或者错误的类型发现集群的首页不能显示实时的CPU GPU运转信息,并且作业状态也一直是排队等待(queuing)的状态,这个时候可以进入专家模式输入squeue来查看作业真实的状态(下面有介绍slurm指令)或者看.out文件的内容是否作业完成,如果完成那这个作业是可以取消掉的,因为跑完了,这个是我使用的集群的一点小问题吧。专家模式的操作需要具有一定的LINUX知识可以在作业状态中点击ID查看Job Information看信息在作业运行过程中,可以直接去目录里查看.out文件,或者点击作业名称:

这样显示的好处是会自动刷新,直接打开.out文件则不会;当然作业运行完的话还是直接去目录打开方便。

slurm命令简介

想了解详情,请参考下面的链接,这里主要给出常用的slurm命令的名称,然后大家可以点击参考链接ctrl+f定位到具体的地方看命令的详细说明 参考链接

命令名称命令说明squeue显示排队和运行中的作业的作业状态scontrol show job id显示作业id的详细信息.如果不输入id会默认打印当前正在工作的作业

好吧,我就常用上面俩个现在哈哈哈哈 效果如下:

下图表示作业ID为137的正在执行

具体信息请进入参考链接查看(如果失效请百度slurm命令说明之类的)

Singularity创建镜像

说实话我不懂Singularity,但是我瞎猫碰到死耗子成功的制作了自己的Singularity镜像,现把我的步骤记录如下,先给出参考链接: Singularity入门之通过沙盒创建镜像 Singularity安装和使用 联想Lico+singularity容器制作+anaconda3安装教程 以下步骤全是我的经验操作,仅供参考。读者在借鉴过程中注意操作,如有不当操作或错误,欢迎大家在评论区指出

第一步 创建Singularity运行环境

然后验证运行查看是否成功

第二步 下载系统镜像并新建自己的镜像

以pytorch镜像为例下载并保存到一个文件夹内,这里新建并保存到了image文件夹 下载保存后,可以新建自己的镜像,并且指向该镜像: 标签描述自己定义。

第三步 进入专家模式,并进入Singularity镜像交互界面

首先用cd命令到包含镜像的文件夹内,然后输入下面语句: pytorch.image 是镜像的名称

singularity shell pytorch.image

然后可以查看python等信息了: 这个时候可以使用pip命令查看已经安装的库文件和其他,然后根据自己的需要进行下载库(下载慢的话可以切换为清华源),注意在pip安装时,要: 加入–user 不然会报错(第一章有提到过)

pip install cv2 --user

应该也可以进行其他的镜像操作,但是我这里安装python库对我已经够用的,所以就没有探索了,感兴趣可以探索一下。 最后,操作完成后退出singularity镜像:

exit n

然后在新建作业的时候选择自己的镜像就行了;

总结

这个新建Singularity镜像的原理我真的不是太懂,凑合着用吧。。如果有大佬可以详细说明,不胜感激。管理员可以对新建一些配置好很多常用库的py文件或者其他,例如Jupyter Notebook 什么的 的镜像,这样对于大多数的小白都可以很好的使用上面的新建例子只是对pytorch镜像进行了一个改进(安装了一些我要使用的库)成为自己的私有库,其他镜像应该有相同的修改方法吧以上是个人探索过程的经验帖,仅供参考。

Lenovo Accelerated AI部分模块使用

图象分类训练模块使用说明

新建任务

《LiCO 5.5.0用户指南》31页

要注意数据集目录的选取和数据目录内存放的文件格式,可以下载数据集样例先测试一下: 下载数据集样例后把压缩包上传到云端的文件管理内找个合适的地方解压,然后要注意选取dataset_dir文件夹作为数据集目录,参考这个文件夹内的图片和csv文件的形式去建立自己的数据集进行训练: 网络结构要选择和图片的大小相符的网络,例如样例中的图片是32 * 32的,这里测试使用的lenet网络也是要求的32 * 32的输入 查看输出

查看输出

在工作空间的文件加下,有.out格式的文件,打开后就可以看到本次训练的结果输出(截取了一部分):

图象分类预测模块使用说明

新建任务

《LiCO 5.5.0用户指南》39页

需要注意的和分类训练任务的基本相同,注意输出目录将存放结果,训练目录选择的是图象分类训练模块的输出的train_dir文件夹,再选择相对应的网络拓扑结构即可:

查看输出

还可以从.out文件中查看本次预测的详细信息,在工作空间目录下,不过我看不出来有有用的信息: 在输出目录中所选的文件夹内,将出现两个文件: 打开accuracy.txt(仅截取一部分): 为啥全是0我不是很明白。。。。。。 打开predict_result.txt(仅截取一部分): 可以看到对应图片的预测结果,发现有的结果预测失败,有的成功

标准AI部分模块使用

TensorFlow 单节点作业

新建任务

《LiCO 5.5.0用户指南》79页

把自己写的代码和数据文件夹都解压上传到My_Folder的合适位置,我这里是TesyPy文件内,TesyPy文件夹内应包含的是你在电脑上能跑通代码的所有文件: run_data文件夹下内容,很多其实是没有用的,对于我的代码,只保留.npy文件和txt标签文件就行了,请根据自己的代码和使用进行保留在电脑上能跑通代码的所有文件: 然后新建任务:

查看输出

部分.out文件截图: 可以直接下载保存checkpoint文件在电脑加载训练权重

对于可视化loss曲线的方法 参考 《LiCO 5.5.0用户指南》80页

不过我一直在加载,,没显示出来 过程中遇到错误请参考 遇到的错误(重要)章节 中会有,或者直接ctrl+F开启浏览器的查找关键字,来快速定位到想要的内容,应该能找到

TensorFlow 多节点作业

操作方法同上

Pytorch单节点作业

新建任务

把自己写的代码和数据文件夹都解压上传到My_Folder的合适位置,我这里是YoLov4_task/YoLoNet文件内,YoLoNet文件夹内应包含的是你在电脑上能跑通代码的所有文件:

注意上图中我框出来的容器镜像,这里我选择的是我制作的一个镜像,因为现在在我们使用的系统的PyTorch中的镜像里,有很多python库(例如cv2 pillow等)没有包含,大家可以先测试使用系统的PyTorch镜像看能跑不能,不行的话联系管理员去为系统的PyTorch镜像安装,也可以参考 通用的一些说明(重要)章节去自己新建一个满足自己需要的镜像库。 初次使用过程中可能会遇到很多奇怪的问题。。。。。如果代码没泡成一定要多看.out文件!!!!看错误类型(例如): 这就是一个没有包含库文件的错误

查看输出

如果代码跑成的话,在.out文件中查看输出信息(截取部分): 然后就可以下载自己保存的.pth文件到电脑上来预测了

过程中遇到错误请参考 遇到的错误(重要)章节中会有,或者直接ctrl+F开启浏览器的查找关键字,来快速定位到想要的内容,应该能找到

总结

以上是个人经验贴,主要记录了一点在第一次使用LICO时遇到的一点问题和探索的方法,上面提到的一些经验和使用教程供我个人使用好久了可以,但是应该只开发了LICO的一点点功能吧,,希望大家不要受到我的这个的限制,按照自己的需求进行参考和探索以后如果自己又用到别的功能会继续更新。

如有不当操作或错误,疑问等,欢迎大家在评论区指出

今天是2020年10月24日,祝所有程序员节日快乐:D

2020 - 996 = 1024

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联想集群超算LICO初次使用踩坑说明(遇到的错误,一些使用方法和singularity新建容器方法)

联想集群超算LICO初次使用踩坑说明(遇到的错误,一些使用方法和singularity新建容器方法)说明本文主要写一些我这几天初次使用LICO的过程中遇到的一点问题和解决的办法,还有一些模块的规范使用方式,和一点自己的小经验,以后如果在使用过程中踩到其他的坑和一些模块的使用方式会慢慢进行更新。。希望对你有所帮助。环境:LICO5.5主要参考内容:《LiCO 5.5.0用户指南》帮助:可以直接ctrl+F开启浏览器的查找关键字,来快速定位到想要的内容目录遇到的错误(重要)Lenovo Accel.

复制链接

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专栏目录

集群系统用户手册

12-18

1.1 硬件环境 1

1.1.1 登录节点 1

1.1.2 计算节点 1

1.1.3 互连系统 1

1.2系统环境 2

1.3 编译环境 2

1.3.1 Intel 编译器 2

1.3.2 gcc编译器 3

1.3.3 mpi编译环境 3

1.3.4 CUDA编译环境 4

2. 系统使用方式 4

2.1 基本条件 4

2.2.3 文件传输 5

2.1 环境变量设置 6

2.2 用户帐号密码修改 7

2.3 退出系统 7

3. 作业提交 7

3.1使用限制 8

3.2状态查看命令 9

4. Cluster应用软件 9

附录A 常用Unix命令 11

附录B 常用vi命令 13

附录C GDB常用命令 15

LICO 6.4.0 配置自己代码的运行的docker

最新发布

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例如:Pytorch + cuda +ubuntu的镜像集合 https://github.com/cnstark/pytorch-docker。例如你找到是docker pull cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04,在cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04前面加上docker://就行了。在终端中,先cd到YOUR_NAME.sif的目录下,例如:安装一些依赖包。

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学校以后要扩建这个平台,现在就7个P4和两个P100

未来有更多显卡会加入(手动微笑)

这个平台不像自己电脑那样有可视化界面

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pycharm配置IdeaVim

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pycharm配置IdeaVim

不知道为啥,找了一圈都没找到配置教程,,,还有的博文直接就讲不能配置???迷惑行为。

配置教程原文:

https://www.jetbrains.com/help/pycharm/using-product-as-the-vim-editor.html

这个是官方文档。

本文也是就做到翻译一下吧,我对vim配置也不太懂。

第一步 安装IdeaVim

然后重启pycharm

这个时候打开idealvim,在Tools > Vim Emulato

这个时候打开ideal

from shapely.geometry import Polygon报错shapely库报错

qq1406433326

12-17

3084

from shapely.geometry import Polygon报错

错误信息如下:

明明安装shapely了,但却出现这个错误,我看了很多回答,都没有解决我的这个问题,有的博主说下载whl,但是官网都404了,并且他们提供的我安装也是失败,到最后我发现我的别的环境里有一个版本的Shapely可以使用:

然后安装这个版本的就解决:

pip install Shapely==1.6.4.post2

...

机器学习经验笔记

qq1406433326

03-14

1932

根据实验楼:机器学习开放基础课程整理,非常好的课!

第一步:读取数据(Pandas 和 Numpy)

1.初步了解数据

首先读取数据

df = pd.read_csv(

'https://labfile.oss.aliyuncs.com/courses/1283/telecom_churn.csv')

df.head() # 查看前五行的数据

为了稍微详细的了解数据的维度,特征,可以用df.shape和df.columns两个方法分别查看,还可以用df.info来输出DataFrame的总体信息。

自动化专业英语单词(人工智能+图像处理+概率论+线代)保研/复试面试使用

qq1406433326

07-14

1703

专业英语单词

英语名称

中文名称

Artificial intelligence (AI)

人工智能

Deep learning (DL)

深度学习

deep neural network (DNN)

深度神经网络

deep Q-Network (DQN)

深度Q学习

End to end

端到端

K-Means Clustering

K聚类算法

Feedforward Neural Networks

前馈神经网络

Linear Regression

线性回归

联想 lico5,5使用手册

07-17

### 回答1:

联想lico5.5使用手册是一份详细的指南,旨在帮助用户熟悉该设备的功能和操作方法。手册提供了关于设备的基本信息,包括硬件规格、屏幕大小、处理器类型等。

手册首先介绍了设备的外观和各个部件的功能,如电源键、音量控制键等。然后,它详细解释了如何打开和关闭设备,以及如何连接到无线网络。此外,手册还提供了一些有关如何保护设备安全的提示,例如设置屏幕锁定密码和启用指纹识别功能。

手册的进一步内容涵盖了设备的各种功能和应用程序的使用方法。例如,它解释了如何使用设备上的摄像头拍照和录制视频,以及如何编辑和共享这些媒体文件。此外,手册还讲解了如何使用设备上的浏览器进行网页浏览,以及如何下载和安装应用程序。

手册还提供了有关设备设置和个性化选项的说明。例如,它解释了如何更改设备的壁纸和主题,以及如何调整各种设置,如通知和声音设置、语言和输入法设置等。

最后,手册还包含了一些故障排除和常见问题解答。它指导用户如何解决可能遇到的一些常见问题,如应用程序崩溃、无法连接到无线网络等。

总之,联想lico5.5使用手册为用户提供了全面的指导,帮助他们更好地了解和使用该设备。无论是初次使用还是已经熟悉该设备,用户都可以在手册中找到有关各种功能和操作的详细说明。

### 回答2:

联想 lico5,5 是一款智能手机,它提供了详细的使用手册来帮助用户更好地了解和操作手机。以下是有关联想 lico5,5使用手册的回答:

1. 开始使用:使用手册首先介绍了联想 lico5,5的外观和基本操作,包括如何打开手机、插入SIM卡和扩展存储卡,以及如何充电和启动手机。

2. 主要功能:手册介绍了联想 lico5,5的主要功能,包括拨打电话、发送短信、浏览互联网、拍照、录制视频等。手册详细介绍了每个功能的操作步骤和注意事项。

3. 设置和调整:手册提供了关于手机设置和调整的详细说明,包括如何调整屏幕亮度、音量和振动设置,如何连接Wi-Fi和蓝牙设备,以及如何设置锁屏密码和指纹识别等。

4. 应用程序和功能扩展:手册介绍了联想 lico5,5的内置应用程序,如短信、电话簿、相册等,并提供了如何下载和安装其他应用程序的说明。手册还介绍了如何使用手机的GPS导航功能和支付功能等。

5. 常见问题和故障排除:在手册的最后部分,提供了一些常见问题和故障排除的解决方法,如手机无法开机、无法连接互联网等。这些解决方案可以帮助用户快速解决一些常见问题。

总的来说,联想 lico5,5的使用手册提供了全面而详细的指导,帮助用户了解和操作手机的各种功能。它是用户使用这款手机时的重要参考资料,可以帮助用户充分发挥手机的功能,并解决遇到的问题。用户只需仔细阅读手册,按照手册提供的步骤进行操作,就能轻松地使用联想 lico5,5手机。

### 回答3:

联想Lico 5.5是一款智能手机,使用手册提供了用户在使用该手机时的详细指导和说明。下面是对该使用手册的回答。

联想Lico 5.5使用手册对手机的各个方面进行了全面介绍。首先,手册中包含了关于手机的基本信息,例如尺寸、重量、屏幕大小等,这有助于用户了解手机的外观和规格。此外,手册详细介绍了手机的主要功能,如打电话、发送短信、拍照等。对于新手用户来说,这些介绍非常有用,可以帮助他们快速了解和掌握手机的基本操作。

除了基本功能,手册还介绍了手机的高级功能和设置选项。例如,手册中可能会详细介绍如何使用手机上的指纹识别功能、如何设置手机上的各种通知和提醒、如何连接和使用蓝牙设备等。这对于想要更深入了解并充分利用手机功能的用户来说非常有帮助。

此外,手册可能还包含了一些实用技巧和贴士,以帮助用户更好地使用手机。它可以包括如何省电、如何管理手机中的应用程序、如何优化手机的性能等。这些技巧可以帮助用户更好地使用手机,提高其使用体验。

总之,联想Lico 5.5使用手册是用户了解、掌握和充分利用手机功能的重要工具。它提供了对手机各个方面的详细介绍和说明,能够满足用户对手机使用的各种需求。无论是新手还是有经验的用户,使用手册都能为他们提供便利和指导,帮助他们更好地使用联想Lico 5.5智能手机。

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