2023-06-28 22:39:02 +02:00
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pipeline {
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agent any
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parameters {
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string(
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defaultValue: 'wojciechbatruszewicz',
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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: '',
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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: '30',
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description: 'dataset 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('Download dataset') {
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steps {
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checkout scm
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2023-04-21 16:10:02 +02:00
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sh 'ls -l'
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2023-06-28 22:39:02 +02:00
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
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sh 'kaggle datasets download -d elakiricoder/gender-classification-dataset'
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sh 'unzip -o gender-classification-dataset.zip'
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}
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}
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}
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stage('Docker') {
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steps {
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script {
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def dockerImage = docker.build("docker-image", "./")
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dockerImage.inside {
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sh 'ls -l'
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sh 'ls -l'
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sh 'python3 createDataset.py'
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archiveArtifacts 'gender_classification_train.csv'
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archiveArtifacts 'gender_classification_test.csv'
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archiveArtifacts 'gender_classification_val.csv'
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sh 'ls -l'
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}
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}
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}
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}
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}
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post {
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success {
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build job: 'x1-training/main', wait: false
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2023-04-21 15:03:48 +02:00
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}
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}
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2023-06-28 22:39:02 +02:00
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}
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