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& Lt; Puppet centralized Configuration Management System & gt;

config. ppClass httpd: config {File {"/etc/httpd/conf/httpd. conf ":Ensure => present,Source => "puppet: // modules/httpd. conf ",# The actual path is in/etc/puppet/modules/httpd/files/httpd. conf.Require => Class ["httpd: install"],Policy => Class ["httpd: service"]}}Vi service. ppClass httpd: service {Service {"httpd ":Ensure => running,Require => Class ["httpd: install", "httpd: config"]}}Vi init. ppClass httpd {Include httpd: install, httpd: config, httpd: service}Vi nodes. ppNode 'server90

Basic Database knowledge Review

1. Data View and data model data abstraction have three layers: physical layer, logical layer, and view layer. There are two types of data models used in the logic layer: one is the conceptual data model, which is mainly used for database design. It can be understood by general users and is similar to the way people think. Such a model has an entity. 1. Data View and data model data abstraction have three layers: physical layer, logical layer, and view layer. There are two types of data models u

Net use command set ()

user name ERM to replace the access of the specified user Permission;/g username erm gives the specified user access permission; perm can be: N none, r read, W write, c Change (write), f full control; example: cacls D: \ test.txt/d pub settings D: \ test.txt rejects pub user access.Cacls file name to view the object access user permission listAdd annotation to the batch file for REM text contentNetsh

Reading Notes for statistical learning theory

dataset to normalise the data points to centre the mean into the origin, the Gaussian Distribu tion becomes a normal distribution. The term ' normal law ' is used. In my opinion, it's better to just highly the assumption of independence, since the assumption of independence was Mor E fundamental. The Gaussian distribution, is however, only a special situation of this condition. Regarding to the third point, the statement seems a little too absolute, as maximum likelihood estimation does not sta

The 1th Chapter: An Introduction to statistical learning methods

http://www.cnblogs.com/levone/p/3531054.html#28989841.4 Model Evaluation and model selectiongeneralization ability (generalization ability): the ability to predict unknown data with learning methods.over Fitting (over-fitting): the model selected in the study contains too many parameters, so that the model is expected to known quantity well,But the very bad image of the unknown is predicted.Experience risk minimization (empirical risk minimization, ERM

IBM Copy Service -- Flashcopy Introduction

IBM provides three advanced features of copy service1. flashcopy2. volumecopy3. Enhanced remote partitioning ing (ERM) This article describes flashcopy, volumecopy, and ERM.1) First, let's take a look at the description of flashcopy in redbook. A FlashCopy is a virtual logical drive that is a point-in-time (PiT) image of a real logical drive. the FlashCopy is the logical equivalent of its complete physical

Yisaitong and other encryption software deciphering the development history of Green Edition and free version of source code, and yisaitong encryption software

Yisaitong and other encryption software deciphering the development history of Green Edition and free version of source code, and yisaitong encryption software Encryption software deciphering software (fenqi technology) Green Edition is a software decryption tool that can be decrypted by the encryption software and restored to the pre-encryption status. It can mainly solve encryption software such as a file encryption system of yisaitong, an erm of

The concept learning of linear regression, logistic regression and various regression

(empirical Risk minimization) ERM, and the empirical risk is evaluated and calculated using the loss function.For classification problems, empirical risk, on training sample error rate.For function approximation, fitting problem, empirical risk, square training error.For the probability density estimation problem, ERM is the maximum likelihood estimation method.The least risk of experience is not necessari

Some cutting points in statistical learning-starting from basic concepts

1. Non-probability model vs Probability Model A non-probability model is a model represented by a decision function, and a probability model is a model represented by a conditional probability. 2. loss function (loss fun) VS risk function (cost fun) The loss function measures the quality of a model's prediction at a time. The risk function measures the quality of a model's prediction at an average value, which is assumed the expected loss of the model under the joint distribution. The risk

Recursive implementation of the full array (i)

IdeasThe following is a concrete example of the implementation of this idea, such as the realization of 123 of the full array of combinations.Requires 123 of the full arrangement, can be divided into the following situations:Case 1: Full arrangement of the No. 0 place for 1+23Case 2: Full arrangement of the No. 0 place for 2+13Case 3: Full arrangement of the No. 0 place for 3+32The above situation is implemented in code as follows://Situation 1//In order to be consistent with the following, plus

Linear regreesion Linear Regression

Newton Method to Solve the minimum value and determine the unknown parameters. Logistic/sigmoid regreesion mode: By using a specific function, the linear regression problem is transformed into a classification problem, that is, by using this function, the value of Y is within the range of 0-1. Expected Risk (real risk)It can be understood as the average loss degree of data or the error degree of "average" when the model function is fixed. The expected risk depends on the loss function and prob

ECLIPSE Idea Tuning 1

Tuning the JVM for the IDE you useLog output configuration is performed firstEclipse Modify eclipse.ini idea Modify idea.exe.vmoptionsIncrease the configuration parameters of the print log -xx: +printgctimestamps-xx: +printgcdetails-verbose: GC-XLOGGC: Cc_gc.log After you start the IDE, view the cc_gc.log file 9.818: [GC 9.818: [defnew:139776k->17472k (157248K), 0.1119870 secs] 139776k->18556k (506816K), 0.1122030 secs] [Ti mes:user=0.09 sys=0.02, real=0.1

NET use command collection detailed () _dos/bat

instead of the Cmd.exeThe file name of the path path \ Executable file sets a path for the executable file.CMD starts a win2k command interpretation window. Parameters:/eff,/en off, open command extensions;See the detailed description of CMD/?REGEDIT/S registry File name Import registry, parameter/s refers to quiet mode import, without any hint;regedit/e registry File name Export Registrycacls filename parameter to show or modify file access control List (ACL)--For NTFS format。 Parameters:/d us

"Database Foundation" of computer soft test notes

model 1 Basic knowledge In a relational database management system (RDBMS), data is represented by a collection of relationships. Characteristics of relationships in an RDBMS: name, attribute, tuple 2 operation of the relationship (1) Structured Query Language (SQL): A descriptive (not procedural) language (2) 9 Kinds of operation ① Insertion ② Delete ③ Update ④ selection ⑤ Photography ⑥ Connection ⑦ and ⑧ Sex ⑨ Poor (3) Combination of statements The SQL language allows us to combine the statem

How to Use Datadog to monitor Nginx (part 3)

How to Use Datadog to monitor Nginx (part 3) If you have read the previous sections on how to monitor NGINX, you should know how much information you can get from several metrics of your network environment. You can also see how easy it is to collect metrics from a specific NGINX base. However, to achieve comprehensive and continuous monitoring of NGINX, you need a powerful monitoring system to store and visualize metrics. You can be reminded when exceptions occur. In this article, we will show

Business Intelligence: plan your first Microsoft Bi Solution

part to embed a pivot table and a pivot table.Moss dashboard page. A dashboard is a special SharePoint content type that allows you to use a variety of WebThe widget displays data from multiple sources on a page. You can even add a filter to the dashboard page and connect the filter to some or all webWidget to dynamically change the content on the page based on

Puppet Detailed Deployment Guide

/tmp/helloworld.txtHello, worldIII. Dashboard Tools for installing puppet on the service side1. Installation settings MySQLYum install MySQL mysql-devel mysql-server-yIn/etc/my.cnf [mysqld], add max_allowed_packet = 32M/etc/init.d/mysqld startChkconfig mysqld onmysqladmin-u root password ' 123456 'Cat Create_dashboard.sql #创建数据库CREATE DATABASE dashboard CHARACTER SET UTF8;CREATE USER '

WordPress Removes unnecessary information

1. Removing unnecessary header information// Remove unnecessary head information // Removal of version information remove_action (' Wp_head ', ' Wlwmanifest_link ' ); Remove_action (' wp_head ', ' rsd_link '); // clears the Offline editor interface remove_action (' Wp_head ', ' adjacent_posts_rel_link_wp_head ', 10, 0); // clear the contextual information remove_action (' Wp_head ', ' feed_links ', 2 ); Remove_action (' wp_head ', ' Feed_links_extra ', 3); // Clear Feed information remove_action

Puppet3 installation under the CentOS6.5 cluster

Environment: 3 main units,IP is 10.211.55.11, 12, 13, respectivelyPuppet Master installed in 10.211.55.11Puppet agent installed in 10.211.55.11, 12, 131, install the Epel library behind the installation puppet dashboard needYum Install YUM-PRIORITIESRPM-IVH https://dl.fedoraproject.org/pub/epel/epel-release-latest-6.noarch.rpm rpm- Import https://dl.fedoraproject.org/pub/epel/RPM-GPG-KEY-EPEL-6Where the source can be replaced with a domestic mirrorhtt

Create a k8s standalone environment using minikube

kubernetes Create the following script: VI auto_pull_images.sh Add the following content to the script: #! /Bin/bashimages = (kube-proxy-amd64: v1.12.1 kube-scheduler-amd64: v1.12.1 kube-controller-manager-amd64: v1.12.1 kube-apiserver-amd64: v1.12.1 etcd-amd64: 3.2.24 pause-amd64: 3.1 kubernetes-dashboard-amd64: v1.10.0 k8s-dns-sidecar-amd64: 1.14.8 k8s-dns-kube-dns-amd64: 1.14.8k8s-dns-dnsmasq-nanny-amd64: 1.14.8) for imagename in $ {images [@]};

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