84 lines
2.7 KiB
Plaintext
84 lines
2.7 KiB
Plaintext
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node {
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stage('Preparation') {
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properties([
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pipelineTriggers([
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// TODO change auto run after train
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upstream(
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threshold: hudson.model.Result.SUCCESS,
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upstreamProjects: 's424714-create-dataset'
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)
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]),
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copyArtifactPermission('*'),
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parameters([
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'BUILD_SELECTOR'
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),
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])
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])
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}
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stage('Git clone') {
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//cloning git repo
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's424714', url: 'https://git.wmi.amu.edu.pl/s424714/ium_424714']]])
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}
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stage('Dockerfile build') {
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sh "chmod +x -R ${env.WORKSPACE}"
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copyArtifacts fingerprintArtifacts: true, projectName: 's424714-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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//TODO from train
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// copyArtifacts fingerprintArtifacts: true, projectName: 's424714-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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def dockerImage = docker.build("s424714-model")
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dockerImage.inside {
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withEnv(["TRANSFORMERS_CACHE=./.cache"]) {
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stage("Docker: cloning artifacts"){
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sh 'mkdir -p ./data/dataset'
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sh 'mv -t ./data/dataset train.csv test.csv val.csv'
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sh 'mv -t ./data True.csv Fake.csv'
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sh 'mv model.pt ./results/model.pt'
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}
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stage("Docker: Running training model")
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{
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sh 'mkdir -p ./.cache'
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// sh ""
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sh 'python ./src/main.py --test '
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sh "cp ./results/*.csv ${WORKSPACE}"
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}
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}
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}
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}
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stage('Saving artefacts') {
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echo 'Goodbye!'
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sh 'ls'
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archiveArtifacts artifacts: '*.csv'
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}
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stage('Drawing plot') {
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plot csvFileName: 'plot-accuracy.csv',
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csvSeries: [[
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file: 'acc.csv',
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exclusionValues: '',
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displayTableFlag: false,
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inclusionFlag: 'OFF',
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url: '']],
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group: 'Plot Group',
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title: 'Accuracy',
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style: 'line',
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exclZero: false,
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keepRecords: false,
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logarithmic: false,
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numBuilds: '',
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useDescr: false,
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yaxis: '',
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yaxisMaximum: '',
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yaxisMinimum: ''
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}
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}
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