Update Jenkinsfile

Jenkinsfile

@@ -1,9 +1,15 @@
 pipeline {
     agent any
+    triggers {
+        upstream(upstreamProjects: 'z-s464914-create-dataset', threshold: hudson.model.Result.SUCCESS)
+    }
     parameters {
-    string(name: 'KAGGLE_USERNAME', defaultValue: 'alicjaszulecka', description: 'Kaggle username')
+            buildSelector (
-    password(name: 'KAGGLE_KEY', defaultValue:'', description: 'Kaggle Key')
+                defaultSelector: lastSuccessful(),
-    string(name: 'CUTOFF', defaultValue: '100', description: 'cut off number')
+                description: 'Build for copying artifacts',
+                name: 'BUILD_SELECTOR'
+            )
+            string(name: 'EPOCHS', defaultValue: '10', description: 'epochs')
         }
     stages {
         stage('Git Checkout') {
@@ -11,38 +17,17 @@ pipeline {
                checkout scm
             }
         }
-         stage('Download dataset') {
+     stage('Copy Artifacts') {
             steps {
-        withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
+               copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464914-create-dataset', selector: buildParameter('BUILD_SELECTOR')
-          sh 'pip install kaggle'
-          sh 'kaggle datasets download -d uciml/forest-cover-type-dataset'
-          sh 'unzip -o forest-cover-type-dataset.zip'
-          sh 'rm forest-cover-type-dataset.zip'
             }
         }
-    }
+         stage('Train') {
-     stage('Build') {
-         steps {
-            script {
-                 withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
-                          "KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
-                    def customImage = docker.build("custom-image")
-                    customImage.inside {
-                        sh 'python3 ./IUM_2.py'
-                        archiveArtifacts artifacts: 'covtype.csv, forest_train.csv, forest_test.csv, forest_val.csv', onlyIfSuccessful: true
-                    }
-                 }
-             }
-         }
-         }
-
-         stage('Train and Predict') {
             steps {
               script {
                def customImage = docker.build("custom-image")
                     customImage.inside {
-                        sh 'python3 ./model.py'
+                        sh 'python3 ./model.py ' + params.EPOCHS
-                        sh 'python3 ./prediction.py'
                         archiveArtifacts artifacts: 'model.pth, predictions.txt', onlyIfSuccessful: true
                     }
               }

model.py

@@ -6,6 +6,7 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 import torch.nn.functional as F
+import sys
  
 
 device = (
@@ -30,6 +31,9 @@ class Model(nn.Module):
         return x
 
 def main():
+    epochs = int(sys.argv[1])
+    print(epochs)
+
     forest_train = pd.read_csv('forest_train.csv')
     forest_val = pd.read_csv('forest_val.csv')
 
@@ -59,7 +63,6 @@ def main():
     val_loader = DataLoader(list(zip(X_val, y_val)), batch_size=64)
 
     # Training loop
-    epochs = 10
     for epoch in range(epochs):
         model.train()  # Set model to training mode
         running_loss = 0.0