update dockerfile, add jenkinsfile_registry, registry.py

Dockerfile

@@ -38,6 +38,7 @@ COPY Biblioteka_DL/dllib-sacred.py .
 COPY Biblioteka_DL/evaluate.py .
 COPY Biblioteka_DL/dllib-mlflow.py .
 COPY predict.py .
+COPY registry.py .
 COPY Biblioteka_DL/all_games.csv .
 
 RUN ./download.sh $CUTOFF

Jenkinsfile_registry

@@ -0,0 +1,22 @@
+pipeline {
+    agent {
+       dockerfile {
+			 additionalBuildArgs "--build-arg KAGGLE_USERNAME=${params.KAGGLE_USERNAME} --build-arg KAGGLE_KEY=${params.KAGGLE_KEY} --build-arg CUTOFF=${params.CUTOFF} -t maciejczajka"
+		}
+    }
+ parameters {
+        buildSelector(
+          defaultSelector: lastSuccessful(),
+          description: 'Which build to use for copying artifacts for predict',
+          name: 'BUILD_SELECTOR')
+    }
+    
+    stages {
+        stage('Script') {
+            steps {
+                copyArtifacts projectName: 's444409-training/main', selector: buildParameter('BUILD_SELECTOR')
+		sh 'python3 ./registry.py'
+            }
+        }
+    }
+}

registry.py

@@ -0,0 +1,9 @@
+import mlflow
+logged_model = '/mlruns/12/1c2b9737c0204b0ca825811c35fb6c64/artifacts/s444409'
+
+# Load model as a PyFuncModel.
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+# Predict on a Pandas DataFrame.
+import pandas as pd
+print(f'Prediction: {loaded_model.predict(pd.DataFrame(data))}')