67 lines
2.2 KiB
Groovy
67 lines
2.2 KiB
Groovy
pipeline {
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agent any
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parameters {
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string(name: 'CUTOFF', defaultValue: '10000', description: 'Liczba wierszy do obcięcia ze zbioru danych')
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string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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}
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stages {
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stage('Clone Repository') {
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steps {
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git url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
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}
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}
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stage('Download dataset') {
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steps {
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withEnv(["KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", "KAGGLE_KEY=${env.KAGGLE_KEY}"]) {
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sh "kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate --unzip"
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}
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}
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}
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stage('Process and Split Dataset') {
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agent {
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dockerfile {
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filename 'Dockerfile'
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reuseNode true
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}
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}
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steps {
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sh "chmod +x ./IUM_05-split.py"
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sh "python3 ./IUM_05-split.py"
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archiveArtifacts artifacts: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', onlyIfSuccessful: true
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}
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}
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stage("Run") {
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agent {
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dockerfile {
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filename 'Dockerfile'
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reuseNode true
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}
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}
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steps {
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sh "chmod +x ./IUM_05-model.py"
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sh "chmod +x ./IUM_05-predict.py"
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sh "python3 ./IUM_05-model.py"
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sh "python3 ./IUM_05-predict.py"
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archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
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}
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}
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stage('Sacred') {
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steps {
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sh 'chmod +x sacred/sacred_training_model.py'
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sh 'python3 sacred/sacred_training_model.py'
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}
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}
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stage('Archive Artifacts from Experiments') {
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steps {
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archiveArtifacts artifacts: 'sacred_runs/**/*.*', onlyIfSuccessful: true
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}
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}
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}
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}
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}
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