run prediction

Jenkinsfile_predict

@@ -0,0 +1,38 @@
+pipeline {
+    agent {
+        docker { image 's444507_create_dataset_image:latest' }
+    }
+    parameters {
+        buildSelector(
+          defaultSelector: lastSuccessful(),
+          description: 'Which build to use for copying artifacts for predict',
+          name: 'BUILD_SELECTOR')
+          string(
+            defaultValue: '{\\"inputs\\": [[0.51, 0.86], [0.79, 0.79], [0.74, 0.77], [0.66, 0.73]]}',
+            description: 'Input file',
+            name: 'INPUT',
+            trim: true
+          )
+    }
+    stages {
+        stage('Copy arifacts') {
+            steps {
+                copyArtifacts projectName: 's444356-training/master', selector: buildParameter('BUILD_SELECTOR')
+                sh "echo ${params.INPUT} > input_example.json"
+            }
+        }
+        stage('Run prediction on model') {
+            steps {
+                sh "python3 lab08_predict.py $epoch"
+            }
+        }
+    }
+    post {
+        success {
+            archiveArtifacts artifacts: 'CarPrices_pytorch_model.pkl, mlruns/**, my_model/**', followSymlinks: false
+        }
+        always {
+            emailext body: "${currentBuild.currentResult}", subject: 's444507-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+    }
+}

lab08_deepLearining_mlflow.py

@@ -15,7 +15,7 @@ from sklearn import preprocessing
 import sys
 import logging
 import mlflow
-import mlflow.sklearn
+import mlflow.pytorch
 
 logging.basicConfig(level=logging.WARN)
 logger = logging.getLogger(__name__)
@@ -32,9 +32,9 @@ class Model(nn.Module):
         self.layer3 = nn.Linear(60, 5)
 
     def forward(self, x):
-        x = F.relu(self.layer1(x))
-        x = F.relu(self.layer2(x))
-        x = F.softmax(self.layer3(x))  # To check with the loss function
+        x = F.relu(self.layer1(x.float()))
+        x = F.relu(self.layer2(x.float()))
+        x = F.softmax(self.layer3(x.float()))  # To check with the loss function
         return x
 
 

lab08_predict.py

@@ -0,0 +1,15 @@
+import mlflow
+import mlflow.sklearn
+import pandas as pd
+import numpy as np
+import json
+
+logged_model = 'mlruns/1/4b83e774512444188fb587288818c298/artifacts/model'
+model = mlflow.pyfunc.load_model(logged_model)
+
+with open('my_model/input_example.json') as f:
+    data = json.load(f)
+    input_example = np.array([data['inputs'][0]]).reshape(-1,4)
+
+print(f'Prediction: {model.predict(input_example)}')
+