handle coop model build
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@ -15,7 +15,7 @@ pipeline {
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}
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stage('training') {
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steps {
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sh 'rm -rf country_vaccination'
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sh 'rm -rf movies_imdb'
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sh 'chmod +x pred_from_model.py'
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sh 'python3 pred_from_model.py'
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}
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36
Jenkinsfile_mlflow_coop
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36
Jenkinsfile_mlflow_coop
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@ -0,0 +1,36 @@
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pipeline {
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agent {dockerfile true}
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parameters {
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'BUILD_SELECTOR'
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)
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}
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stages {
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stage('copyArtifacts') {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 's430705-training/master', selector: buildParameter('BUILD_SELECTOR')
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}
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}
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stage('training') {
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steps {
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sh 'rm -rf movies_imdb'
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sh 'chmod +x pred_from_mlflow_model.py'
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sh 'python3 pred_from_mlflow_model.py'
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}
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}
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stage('archiveArtifacts') {
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steps{
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archiveArtifacts 'movies_imdb/**'
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}
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}
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stage('sendMail') {
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steps{
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emailext body: currentBuild.result ?: 'MLFLOW PREDICT SUCCESS',
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subject: 's434804',
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to: '26ab8f35.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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@ -57,10 +57,8 @@ if __name__ == "__main__":
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mlf.set_experiment("s434804")
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tracking_url_type_store = urlparse(mlf.get_tracking_uri()).scheme
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if tracking_url_type_store != "file":
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print("no file")
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mlf.keras.log_model(model, "country_vaccination2", registered_model_name="s434804", signature=signature,
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input_example=input_example)
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else:
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print("file")
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mlf.keras.log_model(model, "vaccines_model", signature=signature, input_example=input_example)
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mlf.keras.save_model(model, "country_vaccination", signature=signature, input_example=input_example)
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