update

Dockerfile

@@ -1,19 +1,14 @@
 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 pip3 install kaggle pandas scikit-learn tensorflow
+RUN pip install --user kaggle pandas scikit-learn tensorflow
 
 WORKDIR /app
 
 COPY ./data_processing.sh ./
-COPY ./model.py ./
 COPY ./OrangeQualityData.csv ./
-COPY ./requirements.txt ./
+COPY ./orange_quality_model_tf.h5 ./
+COPY ./predictions_tf.json ./
 
-RUN pip3 install -r requirements.txt
-
-CMD ["python3", "model.py"]
+CMD ["python3", "data_processing.sh"]

Jenkinsfile

@@ -8,16 +8,6 @@ pipeline {
 			defaultValue: '10000',
 			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 {
@@ -26,17 +16,6 @@ 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 {
@@ -56,29 +35,5 @@ 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()
-        }
     }
 }