ium_444452/Jenkins/Jenkinsfile.training

node {
    checkout scm
    try {
        docker.image('s444452/ium:1.4').inside('-v /mlruns:/mlruns') {
        stage('Preparation') {
            properties([
                pipelineTriggers([upstream(threshold: hudson.model.Result.SUCCESS, upstreamProjects: "s444452-create-dataset")]),
                parameters([
                string(
                 defaultValue: ".",
                 description: 'Data path',
                 name: 'DATA_PATH'
                ),
                string(
                 defaultValue: "1",
                 description: 'EPOCHS',
                 name: 'EPOCHS'
                ),
                string(
                 defaultValue: "20000",
                 description: 'Num words',
                 name: 'NUM_WORDS'
                ),
                string(
                 defaultValue: "150",
                 description: 'Batch size',
                 name: 'BATCH_SIZE'
                ),
                string(
                 defaultValue: "300",
                 description: 'Pad length',
                 name: 'PAD_LENGTH'
                )
                ])
            ])
        }
        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(["DATA_PATH=${params.DATA_PATH}","EPOCHS=${params.EPOCHS}","NUM_WORDS=${params.NUM_WORDS}",
                "BATCH_SIZE=${params.BATCH_SIZE}","PAD_LENGTH=${params.PAD_LENGTH}"]) {
                sh "chmod u+x Scripts/train_neural_network.py"
                sh "python3 Scripts/train_neural_network.py $DATA_PATH $EPOCHS $NUM_WORDS $BATCH_SIZE $PAD_LENGTH"
            }
        }
        stage('Archive artifacts') {
            archiveArtifacts "model/neural_net"
            archiveArtifacts "my_runs/**"
            archiveArtifacts "mlruns/**"
        }
        }
    } 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 build_params = "Path: ${params.DATA_PATH}, Epochs: ${params.EPOCHS}, Num_words: ${params.NUM_WORDS}, Batch_size: ${params.BATCH_SIZE}, Pad_length: ${params.PAD_LENGTH}"
    def details = "Build nr: ${env.BUILD_NUMBER}, status: ${buildStatus} \n url: ${env.BUILD_URL} \n build params: ${build_params}"

    if (buildStatus == 'SUCCESS') {
        build (
            job: "s444452-evaluation/${env.BRANCH_NAME}",
            parameters: [
                gitParameter(name: "BRANCH", value: "${env.BRANCH_NAME}"),
                string(name: "BUILD_NR", value: "${env.BUILD_NUMBER}"),
                string(name: "DATA_PATH",  value: "${params.DATA_PATH}"),
                string(name: "EPOCHS",  value: "${params.EPOCHS}"),
                string(name: "NUM_WORDS",  value: "${params.NUM_WORDS}"),
                string(name: "BATCH_SIZE",  value: "${params.BATCH_SIZE}"),
                string(name: "PAD_LENGTH",  value: "${params.PAD_LENGTH}")
            ],
            wait: false
        )
    }

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