68 lines
2.1 KiB
Plaintext
68 lines
2.1 KiB
Plaintext
pipeline {
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agent any
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parameters{
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string(
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defaultValue: 'mattkrawl',
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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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password(
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defaultValue: '8e6132e627c5c176f7e19d880270d22e',
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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: '100',
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description: 'CUTOFF',
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name: 'CUTOFF',
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trim: false
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)
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}
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stages {
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stage('clear_all') {
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steps {
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sh 'rm -rf *'
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}
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}
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stage('Build'){
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steps{
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//cloning github
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sh 'git clone https://git.wmi.amu.edu.pl/s458023/ium_458023'
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//running Build
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
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// downloading and unzipping kaggle dataset
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sh 'kaggle datasets download -d arnabchaki/data-science-salaries-2023'
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sh 'unzip data-science-salaries-2023.zip -d ./ium_458023'
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// removing zip file
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sh 'rm data-science-salaries-2023.zip'
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sh 'ls -a'
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sh 'ls -a ./ium_458023'
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}
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}
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}
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stage('Docker'){
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agent{
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dockerfile{
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filename 'Dockerfile'
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dir 'ium_458023'
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reuseNode true
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}
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}
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steps{
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sh 'ls -a'
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sh 'python .ium_458023/create-dataset.py'
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archiveArtifacts 'salary_test.csv'
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archiveArtifacts 'salary_dev.csv'
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archiveArtifacts 'salary_train.csv'
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}
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}
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}
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} |