mlflow attempt task 2 prediction registry
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28
Jenkinsfile_pred_reg
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28
Jenkinsfile_pred_reg
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pipeline {
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agent {
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dockerfile true
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}
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parameters {
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string(
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defaultValue: 'input_example.json',
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description: 'Input file name',
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name: 'INPUT_FILE_NAME',
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trim: false
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)
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}
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stages {
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stage('Stage 1') {
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steps {
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echo 'Hello world!'
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}
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}
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stage('Prediction') {
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steps {
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sh 'python3 predict_registry.py $INPUT_FILE_NAME'
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}
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}
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}
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}
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32
predict_registry.py
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predict_registry.py
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import json
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import mlflow
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import numpy as np
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import sys
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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client =mlflow.tracking .MlflowClient()
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model_version = 14
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model_name = "s449288"
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experiment = client.get_latest_versions(model_name, stages=None)
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print(experiment)
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print(experiment[0].source)
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model = mlflow.pyfunc.load_model(
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model_uri=f"models:/{model_name}/{model_version}"
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)
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with open(f'{experiment[0].source}/{(sys.argv[1:])[0]}', 'r') as file:
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json_data = json.load(file)
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print(f"Prediction: {model.predict(np.array([json_data['inputs']]))}")
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'''
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PATH = "mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model"
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model = mlflow.pyfunc.load_model(PATH)
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with open(f'{PATH}/{(sys.argv[1:])[0]}', 'r') as file:
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json_data = json.load(file)
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print(f"Prediction: {model.predict(np.array([json_data['inputs']]))}")
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'''
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