06-training

Jenkinsfile

@@ -1,36 +1,37 @@
 pipeline {
     agent any
 
+    triggers {
+        upstream(upstreamProjects: 'z-s464937-create-dataset', threshold: hudson.model.Result.SUCCESS)
+    }
+
     parameters {
-        string(name: 'CUTOFF', defaultValue: '100', description: 'Ilość wierszy do odcięcia')
-	    string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
+        string(name: 'EPOCHS', defaultValue: '10', description: 'Epochs')
+        buildSelector(defaultSelector: lastSuccessful(), description: 'Build no', name: 'BUILD_SELECTOR')
     }
 
     stages {
-        stage('Clone repo') {
+        stage('Clone Repository') {
             steps {
-                git branch: "main", url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
+                git branch: 'training', url: "https://git.wmi.amu.edu.pl/s464937/ium_464937.git"
             }
         }
-
-        stage('Download and preprocess') {
-            environment {
-                    KAGGLE_USERNAME = "szymonbartanowicz"
-                    KAGGLE_KEY = "4692239eb65f20ec79f9a59ef30e67eb"
-                }
+        stage('Copy Artifacts') {
             steps {
-                withEnv([
-                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
-                    "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                ]) {
-                    sh "bash ./script1.sh ${params.CUTOFF}"
-                }
+                 copyArtifacts filter: 'openpowerlifting.csv', projectName: 'z-s464937-create-dataset', selector: buildParameter('BUILD_SELECTOR')
             }
         }
-        stage('Archive') {
+        stage("Run") {
+            agent {
+                dockerfile {
+                    filename 'Dockerfile'
+                    reuseNode true
+                }
+            }
             steps {
-                archiveArtifacts artifacts: 'data/*', onlyIfSuccessful: true
+                sh "chmod +x ./model.py"
+                sh "python3 ./model.py ${params.EPOCHS}"
+                archiveArtifacts artifacts: 'powerlifting_model.h5', onlyIfSuccessful: true
             }
         }
     }

model.py

@@ -1,3 +1,5 @@
+import sys
+
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
@@ -34,6 +36,6 @@ pipeline = Pipeline(steps=[
 
 pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])
 
-pipeline.fit(X_train, y_train, model__epochs=10, model__validation_split=0.1)
+pipeline.fit(X_train, y_train, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
 
 pipeline['model'].save('powerlifting_model.h5')