update dockerfile, add jenkinsfile_registry, registry.py
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@ -38,6 +38,7 @@ COPY Biblioteka_DL/dllib-sacred.py .
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COPY Biblioteka_DL/evaluate.py .
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COPY Biblioteka_DL/dllib-mlflow.py .
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COPY predict.py .
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COPY registry.py .
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COPY Biblioteka_DL/all_games.csv .
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RUN ./download.sh $CUTOFF
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22
Jenkinsfile_registry
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22
Jenkinsfile_registry
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@ -0,0 +1,22 @@
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pipeline {
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agent {
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dockerfile {
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additionalBuildArgs "--build-arg KAGGLE_USERNAME=${params.KAGGLE_USERNAME} --build-arg KAGGLE_KEY=${params.KAGGLE_KEY} --build-arg CUTOFF=${params.CUTOFF} -t maciejczajka"
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}
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}
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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 for predict',
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name: 'BUILD_SELECTOR')
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}
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stages {
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stage('Script') {
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steps {
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copyArtifacts projectName: 's444409-training/main', selector: buildParameter('BUILD_SELECTOR')
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sh 'python3 ./registry.py'
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}
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}
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}
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}
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9
registry.py
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9
registry.py
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@ -0,0 +1,9 @@
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import mlflow
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logged_model = '/mlruns/12/1c2b9737c0204b0ca825811c35fb6c64/artifacts/s444409'
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# Load model as a PyFuncModel.
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loaded_model = mlflow.pyfunc.load_model(logged_model)
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# Predict on a Pandas DataFrame.
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import pandas as pd
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print(f'Prediction: {loaded_model.predict(pd.DataFrame(data))}')
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