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09d5694438
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@ -1,10 +1,10 @@
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pipeline {
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pipeline {
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agent {
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agent {
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dockerfile true
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docker {
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image 'agakul/ium:mlflow'
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args '-v /mlruns:/mlruns'
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}
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}
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}
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options {
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copyArtifactPermission('s444421-evaluation');
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}
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parameters {
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parameters {
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buildSelector(
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buildSelector(
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defaultSelector: lastSuccessful(),
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defaultSelector: lastSuccessful(),
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@ -23,27 +23,12 @@ pipeline {
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checkout([$class: 'GitSCM', branches: [[name: '*/training_and_evaluation']], extensions: [], userRemoteConfigs: [[credentialsId: 's444421', url: 'https://git.wmi.amu.edu.pl/s444421/ium_444421.git']]])
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checkout([$class: 'GitSCM', branches: [[name: '*/training_and_evaluation']], extensions: [], userRemoteConfigs: [[credentialsId: 's444421', url: 'https://git.wmi.amu.edu.pl/s444421/ium_444421.git']]])
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}
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}
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}
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}
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stage('Script') {
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stage('Training') {
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steps {
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steps {
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copyArtifacts filter: '*', projectName:'s444421-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts filter: '*', projectName:'s444421-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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sh 'ipython ./training_sacred.py with "epochs=$EPOCHS"'
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sh 'python training_mlflow.py $EPOCHS'
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archiveArtifacts artifacts: 'my_runs/1/*, model.pth, my_runs/_sources/*'
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archiveArtifacts artifacts: 'mlruns/**'
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}
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}
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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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success {
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emailext body: 'SUCCESS', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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build job: 's444421-evaluation/training_and_evaluation'
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}
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failure {
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emailext body: 'FAILURE', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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}
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aborted {
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emailext body: 'ABORTED', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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}
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changed {
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emailext body: 'CHANGED', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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}
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@ -118,9 +118,9 @@ def my_main(epochs):
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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if tracking_url_type_store != "file":
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if tracking_url_type_store != "file":
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mlflow.sklearn.log_model(model, "s444421", registered_model_name="s444421", signature=signature)
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mlflow.sklearn.log_model(model, "s444421", registered_model_name="s444421", signature=signature, input_example=X_test.numpy()[:5])
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else:
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else:
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mlflow.sklearn.log_model(model, "s444421", signature=signature)
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mlflow.sklearn.log_model(model, "s444421", signature=signature, input_example=X_test.numpy()[:5])
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# In[ ]:
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# In[ ]:
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