82 lines
2.8 KiB
Plaintext
82 lines
2.8 KiB
Plaintext
node {
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stage('Preparation') {
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properties([
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pipelineTriggers([
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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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string(
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defaultValue: '2',
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description: 'Batch size for training process',
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name: 'BATCH_SIZE',
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trim: false
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),
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string(
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defaultValue: '3',
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description: 'Number of training epochs',
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name: 'NUM_EPOCHS',
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trim: false
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),
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string(
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defaultValue: '1e-6',
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description: 'Learning rate',
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name: 'LR',
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trim: false
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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 ${env.WORKSPACE}"
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copyArtifacts fingerprintArtifacts: true, projectName: 's424714-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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def dockerImage = docker.build("s424714-model")
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dockerImage.inside {
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withEnv(["TRANSFORMERS_CACHE=./.cache"]) {
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stage("Docker: cloning artifacts"){
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sh 'mkdir -p ./data/dataset'
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sh 'mv -t ./data/dataset train.csv test.csv val.csv'
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sh 'mv -t ./data True.csv Fake.csv'
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}
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stage("Docker: Running training model")
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{
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sh 'mkdir -p ./.cache'
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sh 'mkdir -p ./sacred'
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// sh ""
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sh 'python ./src/main.py --train --lr=$LR --batch=$BATCH_SIZE --epochs=$NUM_EPOCHS'
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sh "cp ./results/model.pt ${WORKSPACE}"
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sh "cp ./results/sacred-artifacts.zip ${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: '*.pt, *.zip'
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}
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stage('Triggering eval job') {
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build job: "s424714-evaluation/master", wait: false
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}
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} |