ium_424714/Jenkinsfile-training

node {
    stage('Preparation') { 
        properties([
            pipelineTriggers([
                upstream(
                    threshold: hudson.model.Result.SUCCESS,
                    upstreamProjects: 's424714-create-dataset'
                    )
            ]),
            copyArtifactPermission('*'),
            parameters([
                buildSelector(
                    defaultSelector: lastSuccessful(),
                    description: 'Which build to use for copying artifacts',
                    name: 'BUILD_SELECTOR'
                ),
                string(
                    defaultValue: '2',
                    description: 'Batch size for training process',
                    name: 'BATCH_SIZE',
                    trim: false
                ),
                string(
                    defaultValue: '3',
                    description: 'Number of training epochs',
                    name: 'NUM_EPOCHS',
                    trim: false
                ),
                string(
                    defaultValue: '1e-6',
                    description: 'Learning rate',
                    name: 'LR',
                    trim: false
                ),
            ])
        ])
    }

    stage('Git clone') {
        //cloning git repo
        checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's424714', url: 'https://git.wmi.amu.edu.pl/s424714/ium_424714']]])
    }

    stage('Dockerfile build') {


            sh "chmod +x -R ${env.WORKSPACE}"
                copyArtifacts fingerprintArtifacts: true, projectName: 's424714-create-dataset', selector: buildParameter('BUILD_SELECTOR')

            def dockerImage = docker.build("s424714-model")

            dockerImage.inside {
            withEnv(["TRANSFORMERS_CACHE=./.cache"]) {
                stage("Docker: cloning artifacts"){
                    sh 'mkdir -p ./data/dataset'
                    sh 'mv -t ./data/dataset train.csv test.csv val.csv'
                    sh 'mv -t ./data True.csv Fake.csv'
                }
                stage("Docker: Running training model")
                {
                    sh 'mkdir -p ./.cache'
                    sh 'mkdir -p ./sacred'
                    // sh ""
                    sh 'python ./src/main.py --train --lr=$LR --batch=$BATCH_SIZE --epochs=$NUM_EPOCHS'
                    sh "cp ./results/model.pt ${WORKSPACE}" 
                    sh "cp ./results/sacred-artifacts.zip ${WORKSPACE}" 
                }

                }
            }

    }

    stage('Saving artefacts') {
        echo 'Goodbye!'
        sh 'ls'
        archiveArtifacts artifacts: '*.pt, *.zip' 
    }
    stage('Triggering eval job') {
        build job: "s424714-evaluation/master", wait: false
    }
}