ium_z487174/Jenkinsfile_create_dataset

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pipeline {
agent any
//Definijuemy parametry, które będzie można podać podczas wywoływania zadania
parameters {
string (
defaultValue: 'kaggletests',
description: 'Kaggle username',
name: 'KAGGLE_USERNAME',
trim: false
)
password(
defaultValue: '',
description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
name: 'KAGGLE_KEY'
)
string(
defaultValue: '0',
description: 'CUTOFF',
name: 'CUTOFF',
trim: false
)
}
stages {
stage('clear_all') {
steps {
//Wypisz wartość parametru w konsoli (To nie jest polecenie bash, tylko groovy!)
sh 'rm -rf *'
}
}
stage('checkout') {
steps {
//Wypisz wartość parametru w konsoli (To nie jest polecenie bash, tylko groovy!)
sh 'git clone https://git.wmi.amu.edu.pl/s487174/ium_z487174'
}
}
stage('Build') {
steps {
// Run the maven build
withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
"KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
print 'DEBUG: parameter isFoo = ' + params.KAGGLE_KEY
sh "export KAGGLE_USERNAME=${params.KAGGLE_USERNAME}"
sh "export KAGGLE_KEY=${params.KAGGLE_KEY}"
// sh 'kaggle datasets download -d bartoszpieniak/poland-cars-for-sale-dataset'
// sh 'unzip archive.zip -d ./ium_z487174'
}
}
}
stage('BuildDocker') {
steps {
//Wypisz wartość parametru w konsoli (To nie jest polecenie bash, tylko groovy!)
sh 'mkdir ./results'
sh 'echo $KAGGLE_KEY'
sh 'docker build -t datasets:1.0 ./ium_z487174'
// sh 'pip3 install -r ./ium_z487174/requirements.txt'
// sh 'python3 ./ium_z487174/dataset.py'
}
}
stage('RunDocker') {
steps {
sh "docker run -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v /var/lib/jenkins/workspace/z-s487174-create-dataset/results:/app/results datasets:1.0"
}
}
stage('Goodbye!') {
steps {
echo 'Goodbye!'
//Zarchiwizuj wynik
//archiveArtifacts 'output.txt'
archiveArtifacts 'results/train_data.csv, results/dev_data.csv, results/test_data.csv'
}
}
}
}