52 lines
1.8 KiB
Plaintext
52 lines
1.8 KiB
Plaintext
node {
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checkout scm
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try {
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docker.image('s444452/ium:1.3').inside {
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stage('Preparation') {
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properties([
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pipelineTriggers([upstream(threshold: hudson.model.Result.SUCCESS, upstreamProjects: "s444452-create-dataset")]),
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parameters([
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string(
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defaultValue: ".,14000,1,50,100",
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description: 'Train params: data_path,num_words,epochs,batch_size,pad_length',
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name: 'TRAIN_PARAMS'
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)
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])
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])
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}
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stage('Copy artifacts') {
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copyArtifacts filter: 'train_data.csv', fingerprintArtifacts: true, projectName: 's444452-create-dataset'
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copyArtifacts filter: 'test_data.csv', fingerprintArtifacts: true, projectName: 's444452-create-dataset'
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}
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stage('Run script') {
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withEnv(["TRAIN_PARAMS=${params.TRAIN_PARAMS}"]) {
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sh "python3 Scripts/train_neural_network.py $TRAIN_PARAMS"
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}
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}
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stage('Archive artifacts') {
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archiveArtifacts "model/neural_net"
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}
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}
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} catch (e) {
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currentBuild.result = "FAILED"
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throw e
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} finally {
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notifyBuild(currentBuild.result)
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}
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}
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def notifyBuild(String buildStatus = 'STARTED') {
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buildStatus = buildStatus ?: 'SUCCESS'
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def subject = "Job: ${env.JOB_NAME}"
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def details = "Build nr: ${env.BUILD_NUMBER}, status: ${buildStatus} \n url: ${env.BUILD_URL} \n build params: ${params.TRAIN_PARAMS}"
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if (buildStatus == 'SUCCESS') {
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build job: 's444452-evaluation/master', parameters: [string(name: "TEST_PARAMS", value: "${params.TRAIN_PARAMS}")], wait: false
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}
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emailext (
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subject: subject,
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body: details,
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to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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)
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} |