added model to registry, prediction on s434704

avocado-mlflow.py

@@ -14,6 +14,7 @@ from tensorflow.keras.models import Model
 from tensorflow.keras.callbacks import EarlyStopping
 from keras.models import Sequential
 import mlflow 
+from mlflow.tracking import MlflowClient
 
 
 
@@ -62,6 +63,11 @@ def my_main(epochs, batch_size):
 epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 15
 batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 16
 
+mlflow.set_tracking_uri("http://172.17.0.1:5000")
+mlflow.set_experiment("s434742")
+client = MlflowClient()
+
+
 
 with mlflow.start_run():
         
@@ -71,4 +77,6 @@ with mlflow.start_run():
     mlflow.log_param("batch_size", batch_size)
     mlflow.log_metric("rmse", rmse) 
     #mlflow.keras.log_model(model, 'avocado_model.h5')
-    mlflow.keras.log_model(keras_model=model, path='avocado_model', signature=infer_signature(X_train, y_train), input_example=X_train.iloc[0])
+    mlflow.keras.log_model(keras_model=model, path='avocado_model', registered_model_name="s434742", signature=infer_signature(X_train, y_train), input_example=X_train.iloc[0])
+    mlflow.keras.save_model(keras_model=model, path='avocado_model', signature=infer_signature(X_train, y_train), input_example=X_train.iloc[0])
+

predict-s434704.Jenkinsfile

@@ -0,0 +1,40 @@
+pipeline {
+    agent {
+        dockerfile true
+    }
+
+    parameters {
+        string(
+             defaultValue: 'input_example.json',
+             description: 'Input name',
+             name: 'INPUT_EXAMPLE',
+             trim: false
+        )
+
+        buildSelector(
+           defaultSelector: lastSuccessful(),
+           description: 'Which build to use for copying artifacts for predict',
+           name: 'BUILD_SELECTOR_s434704')
+        }
+
+    stages{
+
+         stage('copy artifacts from s434704') {
+             steps {
+                 copyArtifacts filter: 'movies_on_streaming_platforms_model/**/*', fingerprintArtifacts: false, projectName: 's434704-training/master', selector: buildParameter('BUILD_SELECTOR_s434704')
+             }
+         }
+ 
+         stage('predict data from s434704') {
+ 			steps {
+                 script {
+                         sh 'chmod +x predict-s434704.py'
+                         sh 'python3 predict-s434704.py $INPUT_EXAMPLE'
+                 }
+ 			}
+ 		}
+   
+ 	
+
+     }
+ }

predict-s434704.py

@@ -0,0 +1,13 @@
+import json
+import sys
+import mlflow
+
+filename = sys.argv[1]
+ 
+model = mlflow.keras.load_model("movies_on_streaming_platforms_model")
+path = 'movies_on_streaming_platforms_model/' + filename
+ 
+with open(path) as f:
+    data = json.load(f)
+
+model.predict(data['inputs'])