Registry?

Jenkinsfile_predict_r

@@ -0,0 +1,48 @@
+pipeline {
+    agent {docker { image 'snowycocoon/ium_434788:4'}}
+	parameters{
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'WHICH_BUILD'
+        )
+    }
+
+    stages {
+		stage('copy artifacts')
+            {
+                steps
+                {
+                    sh 'rm -r my_model'
+                    sh 'rm -r model'
+                    copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-training/train', selector: buildParameter('WHICH_BUILD'))
+                }
+            }
+            stage('predict')
+            {
+                steps
+                {
+                    catchError {
+                        sh 'python3.8 Zadanie_09_MLflow_Predict_Registry.py ${BATCH_SIZE} ${EPOCHS}'
+                    }
+                }
+            }
+	}
+
+    post {
+        success {
+            mail body: 'SUCCESS',
+                subject: 's434788 mlflow predict from registry',
+                to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            mail body: 'UNSTABLE', subject: 's434788 mlflow predict from registry', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            mail body: 'FAILURE', subject: 's434788 mlflow predict from registry', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            mail body: 'CHANGED', subject: 's434788 mlflow predict from registry', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+    }
+}

Jenkinsfile_train

@@ -32,6 +32,7 @@ pipeline {
                 {
                     catchError {
                         sh 'rm -r my_model'
+                        sh 'rm -r model'
                         sh 'python3.8 Zadanie_08_and_09_MLflow.py ${BATCH_SIZE} ${EPOCHS}'
                     }
                 }

Zadanie_09_MLflow_Predict_Registry.py

@@ -0,0 +1,21 @@
+
+from mlflow.tracking import MlflowClient
+import mlflow
+import pandas as pd
+
+mlflow.set_tracking_uri("http://172.17.0.1:5000")
+client = MlflowClient()
+version = 0
+model_name = "s434695"
+for mv in client.search_model_versions(f"name='{model_name}'"):
+    if int(mv.version) > version:
+        version = int(mv.version)
+
+model = mlflow.pytorch.load_model(
+    model_uri=f"models:/{model_name}/{version}"
+)
+
+data = pd.read_json('my_model/input_example.json', orient='split')
+
+print(data)
+print(model.predict(data))