ium_444452/Jenkins/Jenkinsfile.training

node {
    checkout scm
    try {
        docker.image('s444452/ium:1.3').inside {
        stage('Preparation') {
            properties([
                pipelineTriggers([upstream(threshold: hudson.model.Result.SUCCESS, upstreamProjects: "s444452-create-dataset")]),
                parameters([
                string(
                 defaultValue: ".,14000,1,50,100",
                 description: 'Train params: data_path,num_words,epochs,batch_size,pad_length',
                 name: 'TRAIN_PARAMS'
                )
                ])
            ])
        }
        stage('Copy artifacts') {
            copyArtifacts filter: 'train_data.csv', fingerprintArtifacts: true, projectName: 's444452-create-dataset'
            copyArtifacts filter: 'test_data.csv', fingerprintArtifacts: true, projectName: 's444452-create-dataset'
        }
        stage('Run script') {
            withEnv(["TRAIN_PARAMS=${params.TRAIN_PARAMS}"]) {
                sh "python3 Scripts/train_neural_network.py $TRAIN_PARAMS"
            }
        }
        stage('Archive artifacts') {
            archiveArtifacts "model/neural_net"
        }
        }
    } catch (e) {
        currentBuild.result = "FAILED"
        throw e
    } finally {
        notifyBuild(currentBuild.result)
    }
}
def notifyBuild(String buildStatus = 'STARTED') {
    buildStatus = buildStatus ?: 'SUCCESS'

    def subject = "Job: ${env.JOB_NAME}"
    def details = "Build nr: ${env.BUILD_NUMBER}, status: ${buildStatus} \n url: ${env.BUILD_URL} \n build params: ${params.TRAIN_PARAMS}"

    emailext (
        subject: subject,
        body: details,
        to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
    )
}