MLFlow other models inference init
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34
jenkins/predict_s444356.Jenkinsfile
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34
jenkins/predict_s444356.Jenkinsfile
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pipeline {
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agent {
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docker {
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image 's478841-image:latest'
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}
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}
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parameters {
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string(
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defaultValue: '{\\"inputs\\": [[0.76, 0.71], [0.6, 0.73], [0.75, 0.75], [0.91, 0.85]]}',
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description: 'Input data',
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name: 'INPUT',
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trim: true
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)
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Build used for artifacts copying',
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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('Load artifacts') {
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steps {
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copyArtifacts projectName: 's444356-training/master', selector: buildParameter('BUILD_SELECTOR')
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}
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}
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stage('Predict using artifact') {
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steps {
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sh "echo ${params.INPUT} > scripts/input_example.json"
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sh 'python3 scripts/predict_s444356.py'
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}
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}
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}
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}
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16
jenkins/predict_s444356.registry.Jenkinsfile
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jenkins/predict_s444356.registry.Jenkinsfile
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pipeline {
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agent {
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docker {
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image 's478841-image:latest'
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args '-v /mlruns:/mlruns'
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}
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}
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stages {
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stage('Predict using artifacts') {
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steps {
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sh 'python3 scripts/predict_s444356_registry.py'
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}
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}
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}
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}
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scripts/predict_s444356.py
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scripts/predict_s444356.py
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import mlflow
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import numpy as np
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import json
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model = mlflow.pyfunc.load_model(
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'mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model')
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with open('input_example.json') as f:
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data = np.array([json.load(f)['inputs'][0]], dtype=np.float32)
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print(f"Predicted values: {model.predict(data.reshape(-1, 2))}")
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10
scripts/predict_s444356_registry.py
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scripts/predict_s444356_registry.py
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import mlflow
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import numpy as np
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import json
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model = mlflow.pyfunc.load_model(
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'mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model')
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with open('mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model/input_example.json') as f:
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data = np.array([json.load(f)['inputs'][0]], dtype=np.float32)
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print(f"Predicted values: {model.predict(data.reshape(-1, 2))}")
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