revert e99d986335

revert Update Jenkinsfile and Dockerfile

Dockerfile

@@ -1,14 +1,19 @@
 FROM ubuntu:latest
 
-RUN apt-get update && apt-get install -y python3-pip unzip coreutils
+RUN apt-get update && apt-get install -y \
+    python3-pip \
+    unzip \
+    coreutils
 
-RUN pip install --user kaggle pandas scikit-learn tensorflow
+RUN pip3 install kaggle pandas scikit-learn tensorflow
 
 WORKDIR /app
 
 COPY ./data_processing.sh ./
+COPY ./model.py ./
 COPY ./OrangeQualityData.csv ./
-COPY ./orange_quality_model_tf.h5 ./
-COPY ./predictions_tf.json ./
+COPY ./requirements.txt ./
 
-CMD ["python3", "data_processing.sh"]
+RUN pip3 install -r requirements.txt
+
+CMD ["python3", "model.py"]

Jenkinsfile

@@ -6,8 +6,18 @@ pipeline {
       parameters {
         string(
 			defaultValue: '10000',
-			name: 'CUTOFF',
+			name: 'CUTOFF',  
 			description: 'Liczba wierszy do obcięcia ze zbioru danych')
+		string(
+            defaultValue: '--epochs 100 --batch_size 32 --learning_rate 0.01',
+            name: 'TRAINING_PARAMS',
+            description: 'Parametry trenowania'
+        )
+    }
+
+    environment {
+        DATASET_PROJECT = 's123456-create-dataset'
+        DATA_DIR = 'data'
     }
 
     stages {
@@ -16,6 +26,17 @@ pipeline {
                 checkout([$class: 'GitSCM', branches: [[name: '*/master']], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s464906/ium_464906']]])
             }
         }
+        stage('Copy Dataset') {
+            steps {
+                script {
+                    copyArtifacts(
+                        projectName: "${DATASET_PROJECT}",
+                        selector: lastSuccessful(),
+                        target: "${env.DATA_DIR}"
+                    )
+                }
+            }
+        }
         stage('Set execute permission') {
             steps {
                 script {
@@ -35,5 +56,29 @@ pipeline {
                 }
             }
         }
+
+        stage('Install Dependencies') {
+            steps {
+                sh 'pip install -r requirements.txt'
+            }
+        }
+
+        stage('Train Model') {
+            steps {
+                sh "python model.py ${params.TRAINING_PARAMS}"
+            }
+        }
+
+        stage('Archive Model') {
+            steps {
+                archiveArtifacts artifacts: 'orange_quality_model_tf.h5', allowEmptyArchive: true
+                archiveArtifacts artifacts: 'predictions_tf.json', allowEmptyArchive: true
+            }
+        }
+    }
+    post {
+        always {
+            cleanWs()
+        }
     }
 }