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Agata 2022-05-17 21:21:31 +02:00
parent 09d5694438
commit 53320fe8a3
2 changed files with 9 additions and 24 deletions
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29
training.Jenkinsfile
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@ -1,10 +1,10 @@
pipeline { pipeline {
agent { agent {
dockerfile true docker {
image 'agakul/ium:mlflow'
args '-v /mlruns:/mlruns'
}
} }
options {
copyArtifactPermission('s444421-evaluation');
}
parameters { parameters {
buildSelector( buildSelector(
defaultSelector: lastSuccessful(), defaultSelector: lastSuccessful(),
@ -23,27 +23,12 @@ pipeline {
checkout([$class: 'GitSCM', branches: [[name: '*/training_and_evaluation']], extensions: [], userRemoteConfigs: [[credentialsId: 's444421', url: 'https://git.wmi.amu.edu.pl/s444421/ium_444421.git']]]) checkout([$class: 'GitSCM', branches: [[name: '*/training_and_evaluation']], extensions: [], userRemoteConfigs: [[credentialsId: 's444421', url: 'https://git.wmi.amu.edu.pl/s444421/ium_444421.git']]])
} }
} }
stage('Script') { stage('Training') {
steps { steps {
copyArtifacts filter: '*', projectName:'s444421-create-dataset', selector: buildParameter('BUILD_SELECTOR') copyArtifacts filter: '*', projectName:'s444421-create-dataset', selector: buildParameter('BUILD_SELECTOR')
sh 'ipython ./training_sacred.py with "epochs=$EPOCHS"' sh 'python training_mlflow.py $EPOCHS'
archiveArtifacts artifacts: 'my_runs/1/*, model.pth, my_runs/_sources/*' archiveArtifacts artifacts: 'mlruns/**'
} }
} }
} }
post {
success {
emailext body: 'SUCCESS', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
build job: 's444421-evaluation/training_and_evaluation'
}
failure {
emailext body: 'FAILURE', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
aborted {
emailext body: 'ABORTED', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
changed {
emailext body: 'CHANGED', subject: 's444421-training status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
}
}
} }

4
training_mlflow.py
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@ -118,9 +118,9 @@ def my_main(epochs):
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
if tracking_url_type_store != "file": if tracking_url_type_store != "file":
mlflow.sklearn.log_model(model, "s444421", registered_model_name="s444421", signature=signature) mlflow.sklearn.log_model(model, "s444421", registered_model_name="s444421", signature=signature, input_example=X_test.numpy()[:5])
else: else:
mlflow.sklearn.log_model(model, "s444421", signature=signature) mlflow.sklearn.log_model(model, "s444421", signature=signature, input_example=X_test.numpy()[:5])
# In[ ]: # In[ ]: