2022-05-01 18:34:51 +02:00
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pipeline {
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agent {
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2022-05-16 09:14:35 +02:00
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docker {
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image 's444507_create_dataset_image:latest'
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args '-v /mlruns:/mlruns'
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}
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2022-05-01 18:34:51 +02:00
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}
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2022-05-01 20:55:08 +02:00
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parameters {
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2022-05-16 09:11:55 +02:00
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string(name: 'epoch', defaultValue: '100', description: 'Number of epochs to train model.')
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2022-05-01 20:55:08 +02:00
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}
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2022-05-01 18:34:51 +02:00
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stages {
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2022-05-16 09:26:07 +02:00
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stage('Get artifacts') {
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2022-05-01 18:34:51 +02:00
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 's444507-create-dataset', selector: lastSuccessful()
|
2022-05-16 09:26:07 +02:00
|
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|
}
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}
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stage('Run mlflow script and save artifacts') {
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steps {
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sh "python3 lab08_deepLearining_mlflow.py $epoch"
|
2022-05-16 09:40:16 +02:00
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|
2022-05-01 18:34:51 +02:00
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}
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|
|
}
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|
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}
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post {
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always {
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emailext body: "${currentBuild.currentResult}", subject: 's444507-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
|
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|
}
|
|
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}
|
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}
|