predict artifact

README.md

@@ -22,3 +22,15 @@ Zadanie 2
 6. copyArtifacts z s444417-create-dataset
 7. parametr BRANCH do wyboru konkretnej gałęzi, buildselector do wybrania builda w Jenkins.eval
 8. powiadomenie mail wraz z metryką loss wysyłane w pliku Jenkinsfile.eval post emailext
+
+IUM_8 opis sposobu rozwiązania zadań i podpunktów
+---
+Zadanie 1
+1. lab8/trainScript.py log_param: epoch i learning_rate i log_metric final_loss
+2. lab8/MLproject
+
+Zadanie 2
+1. na końcu pliku lab8/trainScript.py, zawiera input_example, MLproject docker_env
+2. 
+3. zarejestronwany model np. http://tzietkiewicz.vm.wmi.amu.edu.pl/#/experiments/17/runs/811420769d2642b8be694693c75b3587/artifactPath/linear-model, model rejestruje w pliku lab8/trainScript.py
+4. [projekt](https://tzietkiewicz.vm.wmi.amu.edu.pl:8080/job/s444417-predict-s449288-from-registry/) realizuje predykcje skryptem lab8/predictMlflow.py i printuje ją w consoli builda,

lab8/Jenkinsfile.artifact

@@ -0,0 +1,23 @@
+pipeline {
+    agent {
+        dockerfile {
+            args '-v /mlruns:/mlruns'
+        }
+    }
+    parameters {
+        buildSelector(
+			defaultSelector: lastSuccessful(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+    }
+    stages {
+        stage('Stage') {
+            steps {
+                copyArtifacts filter: 'mlruns.tar.gz', projectName: 's449288-training/master', selector: buildParameter('BUILD_SELECTOR')
+                sh 'mkdir -p mlrunsArtifact && tar xzf mlruns.tar.gz -C mlrunsArtifact --strip-components 1'
+                sh "python lab8/predictArtifact.py"
+            }
+        }
+    }
+}

lab8/MLproject

@@ -11,3 +11,5 @@ entry_points:
       epochs: {type: float, default: 3}
       learning_rate: {type: float, default: 0.1}
     command: "python ./lab8/trainScript.py {epochs} {learning_rate}"
+  test:
+    command: "python ./src/evalScript.py"

lab8/predictArtifact.py

@@ -0,0 +1,14 @@
+import mlflow
+import numpy as np
+import json
+
+logged_model = 'mlrunsArtifact/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+with open(f'{logged_model}/input_example.json') as f:
+    input_example_data = json.load(f)
+
+input_example = np.array(input_example_data['inputs']).reshape(-1,)
+
+print(f'Input: {input_example}')
+print(f'Prediction: {loaded_model.predict(input_example)}')

lab8/predictMlflow.py

@@ -1,30 +1,14 @@
-# import mlflow
-# import numpy as np
-# import json
-# 
-# logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
-# 
-# # Load model as a PyFuncModel.
-# loaded_model = mlflow.pyfunc.load_model(logged_model)
-# 
-# with open(f'{loaded_model}/input_example.json') as f:
-#     input_example_data = json.load(f)
-# 
-#     # Predictions
-#     print(f'input: {input_example_data}')
-#     print(f'predictions: {loaded_model.predict(input_example_data)}')
-
 import mlflow
 import numpy as np
 import json
 
-registry_path = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
+logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
-model = mlflow.pyfunc.load_model(registry_path)
+loaded_model = mlflow.pyfunc.load_model(logged_model)
 
-with open(f'{registry_path}/input_example.json') as f:
+with open(f'{logged_model}/input_example.json') as f:
     input_example_data = json.load(f)
 
 input_example = np.array(input_example_data['inputs']).reshape(-1,)
 
 print(f'Input: {input_example}')
-print(f'Prediction: {model.predict(input_example)}')
+print(f'Prediction: {loaded_model.predict(input_example)}')