Log model.py as artifact

Jenkinsfile-train

@@ -36,12 +36,13 @@ pipeline {
         stage('Train model') {
             steps {
                 sh "python train_model.py -e ${params.EPOCHS} -b ${params.BATCHSIZE}"
-            }
-        }
-        stage('Archive model and evaluate it') {
-            steps {
                 archiveArtifacts artifacts: 'model_out', onlyIfSuccessful: true
                 archiveArtifacts artifacts: 'mlruns/**', onlyIfSuccessful: true
+                sh 'rm -r mlruns'
+            }
+        }
+        stage('Evaluate model') {
+            steps {
                build job: "s444409-evaluation/${params.BRANCH}/", parameters: [string(name: 'BRANCH', value: "${params.BRANCH}")]
             }
         }

train_model.py

@@ -1,10 +1,11 @@
-import torch
-import mlflow
 import argparse
+from urllib.parse import urlparse
+
+import mlflow
 import numpy as np
+import torch
 from torch import nn
 from torch.utils.data import DataLoader
-from urllib.parse import urlparse
 
 from model import PlantsDataset, MLP, train, test
 
@@ -13,7 +14,7 @@ default_epochs = 5
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-mlflow.set_tracking_uri("http://172.17.0.1:5000")
+# mlflow.set_tracking_uri("http://172.17.0.1:5000")
 mlflow.set_experiment("s444409")
 
 
@@ -72,12 +73,15 @@ if __name__ == "__main__":
             signature = mlflow.models.signature.infer_signature(plant_test.x_train.numpy(), preds.numpy())
         tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
         if tracking_url_type_store != "file":
+            mlflow.log_artifact('model.py')
             mlflow.pytorch.log_model(
                 model,
                 "s444409",
                 registered_model_name="s444409",
                 signature=signature,
-                input_example=input_example
+                input_example=input_example,
+                extra_files=['model.py']
             )
         else:
-            mlflow.pytorch.log_model(model, "s444409", signature=signature, input_example=input_example)
+            mlflow.pytorch.log_model(model, "s444409", signature=signature, input_example=input_example,
+                                     extra_files=['model.py'])