prediction registry

lab8/Jenkinsfile.mlflow

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+pipeline {
+    agent {
+        docker {
+            image 'mikolajk/ium:latest'
+            args '-v /mlruns:/mlruns'
+        }
+    }    
+    stages {
+        stage('Stage') {
+            steps {
+                sh 'cd lab8'
+                sh "python predictMlflow.py"
+            }
+        }
+    }
+}

lab8/predictMlflow.py

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+import mlflow
+import numpy as np
+import json
+
+logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
+
+# Load model as a PyFuncModel.
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+with open(f'{loaded_model}/input_example.json') as f:
+    input_example_data = json.load(f)
+
+    # Predictions
+    print(f'input: {input_example_data}')
+    print(f'predictions: {loaded_model.predict(input_example_data)}')