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搜索[install]，共10017篇文章

CentOS6,CentOS7官方镜像安装Oracle11G

文件,修改为以下内容 sh 复制代码 # 我的/home/oracle/rsp/db_install.rsp oracle.install.responseFileVersion=/oracle/install

2025-09-05

Dcoker安装(在线仓库)，最新的服务器搭配容器使用

安装有所区别 2.本文讲解的是Centos7的安装，所以注意您的操作系统 3.学习Docker之前的知识储备(Linux、Maven) 4.官方安装文档 https://docs.docker.com/install

2025-09-08

Hadoop Install

哈哈 # Install Hadoop Master # DownLoad Hadoop and Jdk packets One : stop all hosts Selinux and iptables

2017-11-14

Install WordPress on Centos

Install mariadb yum install mariadb-server mariadb -y systemctl start mariadb.service systemctl enable

2019-03-02

Maven 依赖 install

title: Maven 依赖 installdate: 2018-11-08 16:23:37 tags: 基础首先明确: mvn install 出来的 jar 包只会包含自己的 src 的 classes

2018-11-07

Install Storm on CentOS

[官网]http://storm-project.net/ 安装脚本安装daemontools-0.76.tar.gz的脚本: yum install patch sudo mkdir -p /package

2017-12-06

Mahout 是一个很强大的数据挖掘工具，是一个分布式机器学习算法的集合，包括被称为Taste的分布式协同过滤的实现、分类、聚类等。Mahout最大的优点就是基于hadoop实现，把很多以前运行于单机上的算法，转化为了MapReduce模式，这样大大提升了算法可处理的数据量和处理性能。 http://www.ha97.com/5803.html 1.下载解压[root@sht-sgmhadoopnn-01 hadoop]# wget http://apache.fayea.com/mahout/0.12.2/apache-mahout-distribution-0.12.2.tar.gz[root@sht-sgmhadoopnn-01 hadoop]# tar -xzvf apache-mahout-distribution-0.12.2.tar.gz [root@sht-sgmhadoopnn-01 hadoop]# ln -s /hadoop/apache-mahout-distribution-0.12.2 mahout 2.配置环境变量[root@sht-sgmhadoopnn-01 hadoop]# vi /etc/profile................export MAHOUT_HOME=/hadoop/mahoutexport MAHOUT_CONF_DIR=$MAHOUT_HOME/conf export PATH=$MAHOUT_HOME/bin:$PATH[root@sht-sgmhadoopnn-01 hadoop]# source /etc/profile 3.运行mahout 测试[root@sht-sgmhadoopnn-01 ~]# mahoutMAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.Running on hadoop, using /hadoop/hadoop-2.7.2/bin/hadoop and HADOOP_CONF_DIR=/hadoop/hadoop-2.7.2/etc/hadoopMAHOUT-JOB: /hadoop/mahout/mahout-examples-0.12.2-job.jarAn example program must be given as the first argument.Valid program names are: arff.vector: : Generate Vectors from an ARFF file or directory baumwelch: : Baum-Welch algorithm for unsupervised HMM training canopy: : Canopy clustering cat: : Print a file or resource as the logistic regression models would see it cleansvd: : Cleanup and verification of SVD output clusterdump: : Dump cluster output to text clusterpp: : Groups Clustering Output In Clusters cmdump: : Dump confusion matrix in HTML or text formats cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx) cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally. describe: : Describe the fields and target variable in a data set evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes fkmeans: : Fuzzy K-means clustering hmmpredict: : Generate random sequence of observations by given HMM itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering kmeans: : K-means clustering lucene.vector: : Generate Vectors from a Lucene index matrixdump: : Dump matrix in CSV format matrixmult: : Take the product of two matrices parallelALS: : ALS-WR factorization of a rating matrix qualcluster: : Runs clustering experiments and summarizes results in a CSV recommendfactorized: : Compute recommendations using the factorization of a rating matrix recommenditembased: : Compute recommendations using item-based collaborative filtering regexconverter: : Convert text files on a per line basis based on regular expressions resplit: : Splits a set of SequenceFiles into a number of equal splits rowid: : Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>} rowsimilarity: : Compute the pairwise similarities of the rows of a matrix runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model runlogistic: : Run a logistic regression model against CSV data seq2encoded: : Encoded Sparse Vector generation from Text sequence files seq2sparse: : Sparse Vector generation from Text sequence files seqdirectory: : Generate sequence files (of Text) from a directory seqdumper: : Generic Sequence File dumper seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives seqwiki: : Wikipedia xml dump to sequence file spectralkmeans: : Spectral k-means clustering split: : Split Input data into test and train sets splitDataset: : split a rating dataset into training and probe parts ssvd: : Stochastic SVD streamingkmeans: : Streaming k-means clustering svd: : Lanczos Singular Value Decomposition testnb: : Test the Vector-based Bayes classifier trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model trainlogistic: : Train a logistic regression using stochastic gradient descent trainnb: : Train the Vector-based Bayes classifier transpose: : Take the transpose of a matrix validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors vectordump: : Dump vectors from a sequence file to text viterbi: : Viterbi decoding of hidden states from given output states sequence[root@sht-sgmhadoopnn-01 ~]#

2016-10-12

Install Node.js

选择对应的安装包下载之后，是如下图的一个文件： image.png 双击打开它，会进入安装向导，一直next就可以了，途中可以自定义安装的路径，最后点击Install即可，下图为安装过程的一个截图： image.png

2018-07-08

第 1 章 install

/configure --prefix=/usr/local/python2.x make make install ln -s /usr/local/python/bin/python2.5 /usr

2018-01-10

20.2. yum install

# yum install memcached # chkconfig memcached on # chkconfig --list memcached # cat /etc/sysconfig/memcached

2018-01-08

Source Install

requires MySQL, PHP, RRDTool, net-snmp, and a webserver that supports PHP such as Apache. sudo apt-get install

2017-12-31

Spark2.0.0 Install And Examples

1.Scala 2.11.8 下载解压[root@sht-sgmhadoopnn-01 hadoop]# wget http://downloads.lightbend.com/scala/2.11.8/scala-2.11.8.tgz[root@sht-sgmhadoopnn-01 hadoop]# tar xzvf scala-2.11.8.tgz[root@sht-sgmhadoopnn-01 hadoop]# mv scala-2.11.8 scala[root@sht-sgmhadoopnn-01 hadoop]# 2.将scala文件夹同步到集群其他机器[root@sht-sgmhadoopnn-01 hadoop]# scp -r scala root@sht-sgmhadoopnn-02:/hadoop/[root@sht-sgmhadoopnn-01 hadoop]# scp -r scala root@sht-sgmhadoopdn-01:/hadoop/[root@sht-sgmhadoopnn-01 hadoop]# scp -r scala root@sht-sgmhadoopdn-02:/hadoop/[root@sht-sgmhadoopnn-01 hadoop]# scp -r scala root@sht-sgmhadoopdn-03:/hadoop/ 3.在集群的每台机器配置环境变量,生效###在文件末尾添加两行[root@sht-sgmhadoopnn-01 hadoop]# vi /etc/profileexport SCALA_HOME=/hadoop/scalaexport PATH=$SCALA_HOME/bin:$PATH [root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopnn-02:/etc/profile[root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopdn-01:/etc/profile[root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopdn-02:/etc/profile[root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopdn-03:/etc/profile [root@sht-sgmhadoopnn-01 hadoop]# source /etc/profile[root@sht-sgmhadoopnn-02 hadoop]# source /etc/profile[root@sht-sgmhadoopdn-01 hadoop]# source /etc/profile[root@sht-sgmhadoopdn-02 hadoop]# source /etc/profile[root@sht-sgmhadoopdn-03 hadoop]# source /etc/profile ---------------------------------------------------------------------------------------------------------------------1.Spark2.0.0下载解压[root@sht-sgmhadoopnn-01 hadoop]# wget http://apache.website-solution.net/spark/spark-2.0.0/spark-2.0.0-bin-hadoop2.7.tgz[root@sht-sgmhadoopnn-01 hadoop]# tar xzvf spark-2.0.0-bin-hadoop2.7.tgz[root@sht-sgmhadoopnn-01 hadoop]# mv spark-2.0.0-bin-hadoop2.7 spark 2.配置spark-env.sh[root@sht-sgmhadoopnn-01 conf]# pwd/hadoop/spark/conf[root@sht-sgmhadoopnn-01 conf]# cp spark-env.sh.template spark-env.sh[root@sht-sgmhadoopnn-01 conf]# ###添加以下5行[root@sht-sgmhadoopnn-01 conf]# vi spark-env.sh export SCALA_HOME=/hadoop/scalaexport JAVA_HOME=/usr/java/jdk1.7.0_67-clouderaexport SPARK_MASTER_IP=172.16.101.55export SPARK_WORKER_MEMORY=1gexport SPARK_PID_DIR=/hadoop/pidexport HADOOP_CONF_DIR=/hadoop/hadoop/etc/hadoop 3.配置slaves文件[root@sht-sgmhadoopnn-01 conf]# cp slaves.template slaves[root@sht-sgmhadoopnn-01 conf]# vi slaves sht-sgmhadoopdn-01sht-sgmhadoopdn-02sht-sgmhadoopdn-03 4.将spark文件夹copy到配置slaves文件的机器上[root@sht-sgmhadoopnn-01 hadoop]# scp -r spark root@sht-sgmhadoopdn-01:/hadoop/[root@sht-sgmhadoopnn-01 hadoop]# scp -r spark root@sht-sgmhadoopdn-02:/hadoop/[root@sht-sgmhadoopnn-01 hadoop]# scp -r spark root@sht-sgmhadoopdn-03:/hadoop/ 5.在集群的每台机器配置环境变量,生效[root@sht-sgmhadoopnn-01 hadoop]# vi /etc/profileexport SPARK_HOME=/hadoop/scalaexport PATH=$SPARK_HOME/bin:$PATH [root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopnn-02:/etc/profile[root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopdn-01:/etc/profile[root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopdn-02:/etc/profile[root@sht-sgmhadoopnn-01 hadoop]# scp -r /etc/profile root@sht-sgmhadoopdn-03:/etc/profile [root@sht-sgmhadoopnn-01 hadoop]# source /etc/profile[root@sht-sgmhadoopnn-02 hadoop]# source /etc/profile[root@sht-sgmhadoopdn-01 hadoop]# source /etc/profile[root@sht-sgmhadoopdn-02 hadoop]# source /etc/profile[root@sht-sgmhadoopdn-03 hadoop]# source /etc/profile 6.启动spark[root@sht-sgmhadoopnn-01 sbin]# ./start-all.shstarting org.apache.spark.deploy.master.Master, logging to /hadoop/spark/logs/spark-root-org.apache.spark.deploy.master.Master-1-sht-sgmhadoopnn-01.outsht-sgmhadoopdn-01: starting org.apache.spark.deploy.worker.Worker, logging to /hadoop/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-sht-sgmhadoopdn-01.telenav.cn.outsht-sgmhadoopdn-02: starting org.apache.spark.deploy.worker.Worker, logging to /hadoop/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-sht-sgmhadoopdn-02.telenav.cn.outsht-sgmhadoopdn-03: starting org.apache.spark.deploy.worker.Worker, logging to /hadoop/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-sht-sgmhadoopdn-03.telenav.cn.out[root@sht-sgmhadoopnn-01 sbin]# 7.web查看http://sht-sgmhadoopnn-01:8080/ [root@sht-sgmhadoopnn-01 sbin]# jps27169 HMaster26233 NameNode26641 ResourceManager2312 Jps26542 DFSZKFailoverController2092 Master27303 RunJar26989 JobHistoryServer [root@sht-sgmhadoopdn-01 ~]# jps19907 Worker2086 jar17265 DataNode17486 NodeManager20055 Jps17377 JournalNode17697 HRegionServer3671 QuorumPeerMain 8.运行WordCount案例[root@sht-sgmhadoopnn-01 hadoop]# vi wordcount.txthello abc 123abc hadoop hello hdfsspark yarn123 abc hello hdfs spark wjp wjp abc hello [root@sht-sgmhadoopnn-01 bin]# spark-shellscala> scala> val textfile = sc.textFile("file:///hadoop/wordcount.txt")textfile: org.apache.spark.rdd.RDD[String] = file:///hadoop/wordcount.txt MapPartitionsRDD[1] at textFile at :24 scala> val count=textfile.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)count: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at :26 scala> count.collect()res0: Array[(String, Int)] = Array((hello,4), (123,2), (yarn,1), (abc,4), (wjp,2), (spark,2), (hadoop,1), (hdfs,2)) scala> ###val file=sc.textFile("hadoop fs -ls hdfs://172.16.101.56:8020/wordcount.txt") val file = sc.textFile("hdfs://namenode:8020/path/to/input")val counts = file.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _)counts.saveAsTextFile("hdfs://namenode:8020/output") --------------------------------------------------------------------------------------------------------------------------------------------------------a.本地模式两线程运行#[root@sht-sgmhadoopdn-01 ~]# ./bin/run-example SparkPi 2>&1 | grep "Pi is roughly" [root@sht-sgmhadoopnn-01 spark]# ./bin/run-example SparkPi 10 --master local[2] b.Spark Standalone 集群模式运行[root@sht-sgmhadoopnn-01 spark]# ./bin/spark-submit \ --class org.apache.spark.examples.SparkPi \--master spark://sht-sgmhadoopnn-01:7077 \examples/jars/spark-examples_2.11-2.0.0.jar \100 c.注意 Spark on YARN 支持两种运行模式，分别为yarn-cluster和yarn-client，具体的区别可以看这篇博文，从广义上讲，yarn-cluster适用于生产环境；而yarn-client适用于交互和调试，也就是希望快速地看到application的输出。 #Spark on YARN 集群上 yarn-cluster 模式运行[root@sht-sgmhadoopnn-01 spark]# ./bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master yarn-cluster ./examples/jars/spark-examples_2.11-2.0.0.jar \10 spark-submit \--class org.apache.spark.examples.SparkPi \--master yarn-cluster \--num-executors 3 \--driver-memory 4g \--executor-memory 2g \--executor-cores 1 \$SPARK_HOME/examples/jars/spark-examples_2.11-2.0.0.jar \10

2016-07-31

一文了解npm install -g和npm install --save-dev的关系

本文分享自华为云社区《npm install -g 和 npm install --save-dev 的关系》，作者： SHQ5785。一、npm install 本地安装将安装包放在 .

2024-05-21

Install Tensorflow 2.0 in Conda on Windows 10

正常通过CONDA INSTALL是无法安装TF2.0的，所以只能通过PIP安装，如果你安装后没有任何问题，那么恭喜你，因为我没这么幸运。

2019-10-18

How to Install and Deploy Kubernetes on Ubuntu 16.04

By Hitesh Jethva, Alibaba Cloud Tech Share Author. Tech Share is Alibaba Cloud’s incentive program to encourage the sharing of technical knowledge and best practices within the cloud community. Kubernetes is an open-source container management system that is available for free. It provides a platform for automating deployment, scaling, and operations of application con

2018-05-13

How to Install and Use Ansible on Ubuntu 16.04

By Hitesh Jethva, Alibaba Cloud Tech Share Author Ansible is a free and open source configuration management system that allows you to automatically deploy and centrally manage your applications. You can easily manage and control large numbers of servers from central location using Ansible. Ansible is a great alternative solution of Chef and Puppet because it has a muc

2018-04-18

How to Install and Use Docker on Ubuntu 16.04

By Hitesh Jethva, Alibaba Cloud Tech Share Author Introduction Docker is an Open Source project (container management service) for developers and system administrators to build, ship, and run distributed applications on laptops, data center VMs, cloud and ship them into containers which can then be deployed anywhere. Docker provides an automation of operating system lev

2018-02-12

mac OSX 上 brew install hive

本文介绍brew install hive并修改默认的metastore存储方案，改Derby数据库为mysql的方法以及可能遇到的问题的解决方案。 1.

2017-11-24

yum install报错“Transaction check erro"

在yum install 安装软件的过程中，报如下错误： Transaction check error: file /usr/bin/docker from install of docker-ce

2017-11-11

Openstack组建部署 — Glance Install

目录目录前文列表 Image service overview Openstack Image service包含的组件 Install and configure Prerequisites 先决条件

2016-06-20

关于mvn install命令执行报错问题

比如修改了test-entity,没有及时执行mvn install，会导致test-web启动报错。这种报错，通常及时修改并mvn install是可以避免的。

2018-07-28

OpenStack 系列之Manila install and using the Virtualenv

--adminurlhttp: //X .X.X.X:8786 /v1/ %$tenant_id$s\ --regionRegionOne\ share 3.安装依赖. yum-y install

2017-11-13

资源下载

更多资源

Mario

马里奥是站在游戏界顶峰的超人气多面角色。马里奥靠吃蘑菇成长，特征是大鼻子、头戴帽子、身穿背带裤，还留着胡子。与他的双胞胎兄弟路易基一起，长年担任任天堂的招牌角色。

Nacos

Nacos /nɑ:kəʊs/ 是 Dynamic Naming and Configuration Service 的首字母简称，一个易于构建 AI Agent 应用的动态服务发现、配置管理和AI智能体管理平台。Nacos 致力于帮助您发现、配置和管理微服务及AI智能体应用。Nacos 提供了一组简单易用的特性集，帮助您快速实现动态服务发现、服务配置、服务元数据、流量管理。Nacos 帮助您更敏捷和容易地构建、交付和管理微服务平台。

Rocky Linux

Rocky Linux（中文名：洛基）是由Gregory Kurtzer于2020年12月发起的企业级Linux发行版，作为CentOS稳定版停止维护后与RHEL（Red Hat Enterprise Linux）完全兼容的开源替代方案，由社区拥有并管理，支持x86_64、aarch64等架构。其通过重新编译RHEL源代码提供长期稳定性，采用模块化包装和SELinux安全架构，默认包含GNOME桌面环境及XFS文件系统，支持十年生命周期更新。

WebStorm

WebStorm 是jetbrains公司旗下一款JavaScript 开发工具。目前已经被广大中国JS开发者誉为“Web前端开发神器”、“最强大的HTML5编辑器”、“最智能的JavaScript IDE”等。与IntelliJ IDEA同源，继承了IntelliJ IDEA强大的JS部分的功能。