update dockerfile, add predict.py, jenkinsfile_predict
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@ -37,6 +37,7 @@ COPY Biblioteka_DL/dllib.py .
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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 Biblioteka_DL/all_games.csv .
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RUN ./download.sh $CUTOFF
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Jenkinsfile_predict
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16
Jenkinsfile_predict
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pipeline {
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agent {
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dockerfile {
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additionalBuildArgs "--build-arg KAGGLE_USERNAME=${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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stages {
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stage('Script') {
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steps {
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copyArtifacts filter: '*', projectName: 's444409-training/master', selector: lastSuccessful()
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sh "python predict.py"
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}
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}
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}
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}
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predict.py
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predict.py
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import json
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import mlflow
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import pandas as pd
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#logged_model = 'mlruns/1/d5b6f9c1784a4d2dbb8592cd4ad364d7/artifacts/model'
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logged_model = 'mlruns/1/fa3e620f03e64d888c364827907fb6f5/artifacts/s444409'
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loaded_model = mlflow.pyfunc.load_model(logged_model)
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with open(f'{logged_model}/input_example.json') as f:
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data = json.load(f)
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input_example = pd.DataFrame(data['inputs'][0])
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loaded_model.predict(input_example)
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