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adding chart evam-0.7.2
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‎charts/evam-0.7.2/.helmignore

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# Patterns to ignore when building packages.
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# This supports shell glob matching, relative path matching, and
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# negation (prefixed with !). Only one pattern per line.
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.DS_Store
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# Common VCS dirs
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.git/
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.gitignore
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.bzr/
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.bzrignore
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.hg/
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.hgignore
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.svn/
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# Common backup files
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*.swp
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*.bak
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*.tmp
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*.orig
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*~
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# Various IDEs
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.project
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.idea/
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*.tmproj
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.vscode/

‎charts/evam-0.7.2/Chart.yaml

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apiVersion: v2
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appVersion: 0.7.2
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description: A Helm chart for deploying Edge Video Analytics Microservice and Mosquitto
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Server in k8s
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name: evam
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type: application
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version: 0.7.2

‎charts/evam-0.7.2/Jenkinsfile

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pipeline {
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agent {
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node {
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label 'aws-ec2'
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}
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}
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environment {
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MC_URL = 'https://esh-mc.intel.com' // API url to validate branch exists in mc
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HELMCHART_ID = '628cb03a254262002bf3d5e7' // ID associated with the Service Layer
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HELMCHART_NAME = 'Edge_Video_Analytics_Helm_Chart' // Name which goes to Service Layer
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HELMCHART_FILE_NAME = 'Edge_Video_Analytics_Helm_Chart' //Installation file name associated in the repo
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AWS_S3_BUCKET = 'eshs3bucket' // AWS S3 BUCKET NAME
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AWS_S3_REGION = 'us-west-2' // AWS REGION NAME
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AWS_S3_SOURCE = 'esh/jenkins' // AWS SOURCE Path for Jenkins upload
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AWS_S3_CRED = 'AWS_S3_Upload' // AWS Global Credentials in Jekins
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CURRENT_DATE = sh(script: 'date +%Y%m%d', returnStdout: true).trim()
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GITLAB_LOGIN = credentials('Intel-Gitlab')
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}
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stages {
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stage('Setup') {
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steps {
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sh '''
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echo "--------------------------------------------"
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echo " Agent setup "
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echo "--------------------------------------------"
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while pgrep apt > /dev/null; do
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sleep 11
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done
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sudo apt-get update
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echo "--------------------------------------------"
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'''
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}
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}
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stage('Install certs') {
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steps {
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sh '''
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wget https://gitlab.devtools.intel.com/dse-public/devops-scripts/-/raw/master/install-certs.sh --no-check-certificate
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chmod a+x install-certs.sh
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./install-certs.sh
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'''
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}
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}
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stage('Create Package') {
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steps {
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sh '''#!/bin/bash
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echo "--------------------------------------------"
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echo " Creating ${HELMCHART_NAME} tgz"
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echo "--------------------------------------------"
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echo "Helm Chart Name: ${HELMCHART_NAME}"
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echo "Helm Chart ID: ${HELMCHART_ID}"
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echo "Helm Chart File: ${HELMCHART_FILE_NAME}"
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rm -rf .git* build
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echo "-------------Copy dependencies--------------"
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rsync -av --exclude IntelSHA2RootChain-Base64 --exclude __MACOSX --exclude IntelSHA2RootChain-Base64.tgz --exclude IntelSHA2RootChain-Base64.zip \
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--exclude '*.crt' --exclude '*certs*' --exclude 'Jenkinsfile' ./ ${HELMCHART_ID}
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cd ${HELMCHART_ID}
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echo "---------------Create tgz-------------------"
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touch ${HELMCHART_ID}.tgz
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tar --exclude=${HELMCHART_ID}.tgz -zcvf ${HELMCHART_ID}.tgz .
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retval=$?
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if [ $retval -ne 0 ]; then
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echo "Failed create helm tgz"
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exit $retval
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fi
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echo "--------------------------------------------"
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'''
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}
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}
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stage('Push to S3'){
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steps{
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script {
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def res = "";
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withAWS(region:"${AWS_S3_REGION}",credentials:"${AWS_S3_CRED}") {
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def identity=awsIdentity();//Log AWS credentials
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echo "--------------------------------------------"
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echo " Pushing ${HELMCHART_NAME} to DAL"
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echo "--------------------------------------------"
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echo "${HELMCHART_NAME} : ${HELMCHART_ID}.tgz"
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def MC_VERSION = sh(script: "wget --server-response --method POST --timeout=0 --header 'Content-Type: application/json' \
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--ca-directory=/etc/ssl/certs/ --no-proxy --no-check-certificate \
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--body-data '{\"id\":\"'${HELMCHART_ID}'\",\"version\":\"'$GIT_BRANCH'\"}' \
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\${MC_URL}/helmchart/validateVersion 2>&1 | grep \"HTTP/\" | awk '{print \$2}'", returnStdout: true).trim();
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echo "ID: $HELMCHART_ID, version: $GIT_BRANCH"
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echo "----------------mc version is : ${}MC_VERSION ------------"
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if ( MC_VERSION != '200' ) {
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echo "-------Push to S3 skipped-------"
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echo "--------------------------------------------"
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sh(script: "exit 0")
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}
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def TGZ_FILE_PATH = pwd();
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echo TGZ_FILE_PATH;
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res = s3Upload(file:"${TGZ_FILE_PATH}/${HELMCHART_ID}/${HELMCHART_ID}.tgz", bucket:"${AWS_S3_BUCKET}", path:"${AWS_S3_SOURCE}/helm/${HELMCHART_ID}.tgz");
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}
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if ( res != "s3://${AWS_S3_BUCKET}/${AWS_S3_SOURCE}/helm/${HELMCHART_ID}.tgz" ) {
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error("AWS S3 Upload Failed.");
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}
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echo "-----------Generate MD5sum------------------"
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def MD5SUM = sh(script: "md5sum \${HELMCHART_ID}/\${HELMCHART_ID}.tgz | cut -d ' ' -f1",returnStdout:true).trim();;
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if ( !MD5SUM ){
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error("Failed to generate md5sum")
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}
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echo "original md5sum: ${MD5SUM}"
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}
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}
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}
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stage('Update MD5Sum'){
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steps{
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script {
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def TGZ_FILE_PATH = pwd();
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withAWS(region:"${AWS_S3_REGION}",credentials:"${AWS_S3_CRED}") {
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def identity=awsIdentity();//Log AWS credentials
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echo "--------------------------------------------"
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echo " Downloading ${HELMCHART_NAME} from S3"
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echo "--------------------------------------------"
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echo "${HELMCHART_NAME} : ${HELMCHART_ID}.tgz"
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sh(script: "sudo mkdir -p \${TGZ_FILE_PATH}/\${HELMCHART_ID}/download",returnStdout:true);
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res = s3Download(file:"${TGZ_FILE_PATH}/${HELMCHART_ID}/download/${HELMCHART_ID}.tgz", bucket:"${AWS_S3_BUCKET}", path:"${AWS_S3_SOURCE}/helm/${HELMCHART_ID}.tgz", force:true)
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}
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echo "-----------Generate MD5sum------------------"
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def MD5SUM = sh(script: "md5sum \${HELMCHART_ID}/download/\${HELMCHART_ID}.tgz | cut -d ' ' -f1",returnStdout:true).trim();
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if ( !MD5SUM ){
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error("Failed to generate md5sum")
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}
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echo "md5sum(s3): ${MD5SUM}"
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echo "--------------Post MD5sum-------------------"
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def RETVAL = sh(script: "wget --method POST --timeout=0 --header 'Content-Type: application/json' --body-data '{\"id\":\"'$HELMCHART_ID'\",\"hashValue\":\"'$MD5SUM'\"}' \${MC_URL}/helmchart/updateMD5Hash --ca-directory=/etc/ssl/certs/ --no-proxy --no-check-certificate",returnStatus:true);
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if ( RETVAL != 0 ) {
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error("Failed to post md5sum");
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}
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echo "--------------------------------------------"
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}
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}
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}
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}
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post {
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success {
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emailext(
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attachmentsPattern: "bandit_report.txt, pep8_output.txt",
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replyTo: '$DEFAULT_REPLYTO',
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subject: "Status of pipeline: ${currentBuild.fullDisplayName}",
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body: "${env.BUILD_URL} has result ${currentBuild.result}",
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recipientProviders: [[$class: 'DevelopersRecipientProvider']]
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)
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updateGitlabCommitStatus state: 'success'
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}
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failure {
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emailext(
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replyTo: '$DEFAULT_REPLYTO',
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subject: "Status of pipeline: ${currentBuild.fullDisplayName}",
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body: "${env.BUILD_URL} has result ${currentBuild.result}",
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recipientProviders: [[$class: 'DevelopersRecipientProvider']]
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)
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updateGitlabCommitStatus state: 'failed'
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}
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}
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}

‎charts/evam-0.7.2/LICENSE

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Copyright (C) 2022 Intel Corporation
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SPDX-License-Identifier: MIT
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎charts/evam-0.7.2/README.md

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# Edge Video Analytics Helm chart
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This chart can be used to deploy [Edge Video Analyics Microservice](https://www.intel.com/content/www/us/en/developer/articles/technical/video-analytics-service.html) on Kubernetes cluster using Helm CLI.
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## Usage
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### Pre-requisite.
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1. Clone this [repo](https://github.com/intel/edge-video-analytics-microservice).
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2. Run the following command to make the following files executable:
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```sh
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chmod +x tools/model_downloader/model_downloader.sh docker/run.sh
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```
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3. Download the required models. From the cloned repo, run the following command:
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```sh
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./tools/model_downloader/model_downloader.sh --model-list <Path to model-list.yml>
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```
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4. After downloading the models, you will have the `models` directory in the base folder. Refer to the following:
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```json
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models/
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├── action_recognition
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├── audio_detection
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├── emotion_recognition
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├── face_detection_retail
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├── object_classification
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└── object_detection
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```
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5. Copy the `models` , `pipelines` and `resources` folder to `/opt/intel/evam/` on the kubernetes worker nodes.
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6. Install a [Kubernetes](https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/) cluster.
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7. Install [Helm](https://helm.sh) CLI. Please refer to Helm's [documentation](https://helm.sh/docs/) to get started.
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### Deployment
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- Once the prerequisites are completed properly, install the chart as follows:
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```console
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helm install evam ./chart/
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```
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- Check the status of deployed application:
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Command:-
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```console
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helm ls
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```
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Output:-
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```console
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NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION
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evam default 1 2022-05-16 16:30:09.118680429 +0530 IST deployed evam-0.7.2 0.7.2
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```
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Command:-
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```console
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kubectl get pods
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```
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Output:-
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```console
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NAME READY STATUS RESTARTS AGE
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evam-deployment-79b95cdc98-rdcdd 1/1 Running 0 3m2s
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mqtt-deployment-5b87d54794-8mlr6 1/1 Running 0 3m2s
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```
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### Sending Pipeline Server Requests
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- Get the IP addresses of the evam and mqtt pods running in the cluster.
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```console
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kubectl get pods -o wide
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```
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Sample Output:-
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```console
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NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
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evam-deployment-79b95cdc98-rdcdd 1/1 Running 0 6m11s 10.144.2.45 kubernetes-worker1 <none> <none>
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mqtt-deployment-5b87d54794-8mlr6 1/1 Running 0 6m11s 10.144.1.43 kubernetes-worker2 <none> <none>
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```
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- From the above IPs trigger the pipeline request as below :-
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```console
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curl --location -X POST 'http://10.144.2.45:8080/pipelines/object_detection/person_vehicle_bike' \
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--header 'Content-Type: application/json' \
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--data-raw '{
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"source": {
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"uri": "https://github.com/intel-iot-devkit/sample-videos/raw/master/person-bicycle-car-detection.mp4?raw=true",
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"type": "uri"
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},
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"destination": {
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"metadata": {
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"type": "mqtt",
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"host": "10.144.1.43:1883",
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"topic": "vaserving"
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}
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}
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}'
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```
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- View Pipeline Results via MQTT
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```console
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docker run -it --entrypoint mosquitto_sub eclipse-mosquitto:1.6 --topic vaserving -p 1883 -h 10.144.1.43
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```
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```console
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{"objects":[{"detection":{"bounding_box":{"x_max":0.0878419540822506,"x_min":0.0017804615199565887,"y_max":0.3632156252861023,"y_min":0.12948647141456604},"confidence":0.6258460283279419,"label":"vehicle","label_id":2},"h":101,"roi_type":"vehicle","w":66,"x":1,"y":56}],"resolution":{"height":432,"width":768},"source":"https://github.com/intel-iot-devkit/sample-videos/raw/master/person-bicycle-car-detection.mp4?raw=true","timestamp":49000000000}
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{"objects":[{"detection":{"bounding_box":{"x_max":0.0731869749724865,"x_min":0.004130329936742783,"y_max":0.35117650032043457,"y_min":0.14347273111343384},"confidence":0.6314214468002319,"label":"vehicle","label_id":2},"h":90,"roi_type":"vehicle","w":53,"x":3,"y":62}],"resolution":{"height":432,"width":768},"source":"https://github.com/intel-iot-devkit/sample-videos/raw/master/person-bicycle-car-detection.mp4?raw=true","timestamp":49083333333}
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{"objects":[{"detection":{"bounding_box":{"x_max":0.0626695342361927,"x_min":0.0,"y_max":0.34957605600357056,"y_min":0.14383465051651},"confidence":0.6896529197692871,"label":"vehicle","label_id":2},"h":89,"roi_type":"vehicle","w":48,"x":0,"y":62}],"resolution":{"height":432,"width":768},"source":"https://github.com/intel-iot-devkit/sample-videos/raw/master/person-bicycle-car-detection.mp4?raw=true","timestamp":49166666666}
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```
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## License
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[MIT License](../LICENSE)

‎charts/evam-0.7.2/evam-0.7.2.tgz

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‎charts/evam-0.7.2/templates/NOTES.txt

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1. Get the application URLs by running these commands:
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kubectl get pods -o wide
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2. Run a sample pipline on EVAM pods using the commmand below:
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curl --location -X POST 'http://<EVAM_POD_IP>:8080/pipelines/object_detection/person_vehicle_bike' \
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--header 'Content-Type: application/json' \
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--data-raw '{
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"source": {
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"uri": "https://github.com/intel-iot-devkit/sample-videos/raw/master/person-bicycle-car-detection.mp4?raw=true",
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"type": "uri"
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},
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"destination": {
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"metadata": {
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"type": "mqtt",
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"host": "<MQTT_POD_IP>:1883",
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"topic": "vaserving"
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}
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}
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}'
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3. Get the inference output on MQTT subscriber.
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docker run -it --entrypoint mosquitto_sub eclipse-mosquitto:1.6 --topic vaserving -p 1883 -h <MQTT_POD_IP>
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