add jk
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Jenkinsfile
vendored
68
Jenkinsfile
vendored
@ -1,29 +1,49 @@
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pipeline {
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agent any
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//Definijuemy parametry, które będzie można podać podczas wywoływania zadania
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parameters {
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string (
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defaultValue: 'Hello World!',
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description: 'Tekst, którym chcesz przywitać świat',
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name: 'INPUT_TEXT',
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trim: false
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parameters {
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string(
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defaultValue: 'szymonparafinski',
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description: 'Kaggle username',
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name: 'KAGGLE_USERNAME',
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trim: false
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)
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}
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password(
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defaultValue: '',
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description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
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name: 'KAGGLE_KEY'
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)
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string(
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defaultValue: '1',
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description: 'Cutoff lines',
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name: 'CUTOFF'
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)
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}
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environment {
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KAGGLE_USERNAME="$params.KAGGLE_USERNAME"
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KAGGLE_KEY="$params.KAGGLE_KEY"
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CUTOFF="$params.CUTOFF"
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}
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stages {
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stage('Hello') {
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steps {
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//Wypisz wartość parametru w konsoli (To nie jest polecenie bash, tylko groovy!)
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echo "INPUT_TEXT: ${INPUT_TEXT}"
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//Wywołaj w konsoli komendę "figlet", która generuje ASCI-art
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sh "figlet \"${INPUT_TEXT}\" | tee output.txt"
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}
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}
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stage('Goodbye!') {
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steps {
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echo 'Goodbye!'
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//Zarchiwizuj wynik
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archiveArtifacts 'output.txt'
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}
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}
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}
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stage('Checkout'){
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steps {
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's444018', url: 'https://git.wmi.amu.edu.pl/s444018/ium_444018.git']]])
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}
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}
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stage('Script'){
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steps {
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script {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
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sh 'echo KAGGLE_USERNAME: $KAGGLE_USERNAME'
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sh 'kaggle datasets list'
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}s
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}
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sh './download_dataset.sh $CUTOFF'
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archiveArtifacts artifacts: 'dataset.csv.dev, dataset.csv.test, dataset.csv.train', followSymlinks: false
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}
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}
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}
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}
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18
download_dataset.sh
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18
download_dataset.sh
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dataset_operation() {
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tail -n +2 imdb-dataset.csv | shuf > imdb-dataset.csv.s
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head -n $CUTOFF imdb-dataset.csv.s > ./imdb-dataset.csv.shuf
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len1=$(cat ./imdb-dataset.csv.shuf | wc -l)
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len2=$(($len1/10))
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len3=$(($len2*2))
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len4=$(($len3+1))
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head -n $len2 imdb-dataset.csv.shuf > imdb-dataset.csv.test
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head -n $len3 imdb-dataset.csv.shuf | tail -n $len2 > imdb-dataset.csv.dev
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tail -n +$len4 imdb-dataset.csv.shuf > imdb-dataset.csv.train
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rm imdb-dataset.csv.shuf
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wc -l imdb-dataset.csv.*
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}
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kaggle datasets download -d harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows
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unzip imdb-dataset-of-top-1000-movies-and-tv-shows.zip
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mv imdb-dataset-of-top-1000-movies-and-tv-shows.zip imdb-dataset.zip
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dataset_operation
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