From fad30cde27c54090149956016c692cd9e6ceca38 Mon Sep 17 00:00:00 2001
From: Adam Wojdyla <adam.wojdyla@outlook.com>
Date: Mon, 16 May 2022 09:03:45 +0200
Subject: [PATCH] predict from registry

---
 Jenkinsfile_predict_registry   | 16 ++++++++++++++++
 lab08_deepLearining_mlflow.py  |  2 +-
 lab08_predict_from_registry.py | 14 ++++++++++++++
 3 files changed, 31 insertions(+), 1 deletion(-)
 create mode 100644 Jenkinsfile_predict_registry
 create mode 100644 lab08_predict_from_registry.py

diff --git a/Jenkinsfile_predict_registry b/Jenkinsfile_predict_registry
new file mode 100644
index 0000000..e827103
--- /dev/null
+++ b/Jenkinsfile_predict_registry
@@ -0,0 +1,16 @@
+pipeline {
+    agent {
+        docker {
+            image 's444507-create-dataset'
+            args '-v /mlruns:/mlruns'
+        }
+    }
+    
+    stages {
+        stage('Predict values using model from artifact') {
+            steps {
+                sh "python3 predict_from_registry.py"
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/lab08_deepLearining_mlflow.py b/lab08_deepLearining_mlflow.py
index 370f435..be84002 100644
--- a/lab08_deepLearining_mlflow.py
+++ b/lab08_deepLearining_mlflow.py
@@ -20,7 +20,7 @@ import mlflow.pytorch
 logging.basicConfig(level=logging.WARN)
 logger = logging.getLogger(__name__)
 
-# mlflow.set_tracking_uri("http://localhost:5000/")
+mlflow.set_tracking_uri("http://172.17.0.1:5000")
 mlflow.set_experiment("s444507")
 
 
diff --git a/lab08_predict_from_registry.py b/lab08_predict_from_registry.py
new file mode 100644
index 0000000..70214ee
--- /dev/null
+++ b/lab08_predict_from_registry.py
@@ -0,0 +1,14 @@
+import json
+import mlflow
+import numpy as np
+
+logged_model = '/mlruns/13/da5c6167bb45403fa35569849a1fbc13/artifacts/model'
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+
+with open(f'{logged_model}/input_example.json') as f:
+    data = json.load(f)
+    input_example = np.array([data['inputs'][0]], dtype=np.float64).reshape(-1, 2)
+
+
+print(f'Prediction: {loaded_model.predict(input_example)}')