ium_464979/Jenkinsfile

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