MLFlow other models inference init

jenkins/predict_s444356.Jenkinsfile

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+pipeline {
+    agent {
+        docker {
+            image 's478841-image:latest'
+        }
+    }
+    parameters {
+        string(
+            defaultValue: '{\\"inputs\\": [[0.76, 0.71], [0.6, 0.73], [0.75, 0.75], [0.91, 0.85]]}',
+            description: 'Input data',
+            name: 'INPUT',
+            trim: true
+        )
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Build used for artifacts copying',
+            name: 'BUILD_SELECTOR'
+        )
+    }
+
+    stages {
+        stage('Load artifacts') {
+            steps {
+                copyArtifacts projectName: 's444356-training/master', selector: buildParameter('BUILD_SELECTOR')
+            }
+        }
+        stage('Predict using artifact') {
+            steps {
+                sh "echo ${params.INPUT} > scripts/input_example.json"
+                sh 'python3 scripts/predict_s444356.py'
+            }
+        }
+    }
+}

jenkins/predict_s444356.registry.Jenkinsfile

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+pipeline {
+    agent {
+        docker {
+            image 's478841-image:latest'
+            args '-v /mlruns:/mlruns'
+        }
+    }
+
+    stages {
+        stage('Predict using artifacts') {
+            steps {
+                sh 'python3 scripts/predict_s444356_registry.py'
+            }
+        }
+    }
+}

scripts/predict_s444356.py

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+import mlflow
+import numpy as np
+import json
+
+
+model = mlflow.pyfunc.load_model(
+    'mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model')
+with open('input_example.json') as f:
+    data = np.array([json.load(f)['inputs'][0]], dtype=np.float32)
+print(f"Predicted values: {model.predict(data.reshape(-1, 2))}")

scripts/predict_s444356_registry.py

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+import mlflow
+import numpy as np
+import json
+
+
+model = mlflow.pyfunc.load_model(
+    'mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model')
+with open('mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model/input_example.json') as f:
+    data = np.array([json.load(f)['inputs'][0]], dtype=np.float32)
+print(f"Predicted values: {model.predict(data.reshape(-1, 2))}")