jenkins 2

Jenkinsfile_predict

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+pipeline {
+   agent  any
+    stages {
+      stage('Clear_Before') {
+         steps {
+            sh 'rm -rf *'
+         }
+      }
+      stage('Clone') {
+         steps {
+            sh 'git clone https://git.wmi.amu.edu.pl/s486867/ium_z486867.git'
+         }
+      }
+      stage('copy_artifacts_from_training') {
+         steps {
+            copyArtifacts(projectName: 'z-s486867-training', fingerprintArtifacts: true)
+         }
+      }
+      stage('copy_artifacts_from_evaluation') {
+         steps {
+            copyArtifacts(projectName: 'z-s486867-evaluation', fingerprintArtifacts: true, optional: true)
+         }
+      }
+      stage('copy_artifacts_test') {
+         steps {
+            copyArtifacts(projectName: 'z-s486867-create-dataset', filter: 'X_test.csv,Y_test.csv', fingerprintArtifacts: true)
+         }
+      }
+      stage('Docker') {
+         agent {
+            dockerfile {
+               filename 'Dockerfile'
+               dir      'ium_z486867'
+               reuseNode true
+            }
+         }
+         steps {
+            sh 'ls -a'
+            sh 'python ./ium_z486867/predict.py'
+            archiveArtifacts 'prediction.csv'
+            archiveArtifacts 'metrics.csv'
+         }
+      }
+
+      stage('clear_after') {
+         steps {
+            sh 'rm -rf *'
+         }
+      }
+   }
+}

Jenkinsfile_train

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+pipeline {
+   agent  any
+   parameters{
+         string(
+            defaultValue: '1000',
+            description: 'EPOCHS',
+            name: 'EPOCHS',
+            trim: false
+         )
+    }
+    stages {
+      stage('Clear_Before') {
+         steps {
+            sh 'rm -rf *'
+         }
+      }
+      stage('Clone') {
+         steps {
+            sh 'git clone https://git.wmi.amu.edu.pl/s486867/ium_z486867.git'
+         }
+      }
+
+      stage('copy_artifacts') {
+         steps {
+            copyArtifacts(projectName: 'z-s486867-create-dataset', fingerprintArtifacts: true)
+         }
+      }
+      stage('Docker') {
+         agent {
+            dockerfile {
+               filename 'Dockerfile'
+               dir      'ium_z486867'
+               reuseNode true
+            }
+         }
+         steps {
+                  sh 'python ./ium_z486867/train.py'
+                  archiveArtifacts 'model/'
+         }
+      }
+      stage('Clear_After') {
+         steps {
+            sh 'rm -rf *'
+         }
+      }
+   }
+  }

predict.py

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+import os
+import tensorflow as tf
+import pandas as pd
+from keras import utils
+
+build_number = int(os.environ['BUILD_NUMBER'])
+model = tf.keras.models.load_model('model')
+
+x_to_test = pd.read_csv('./X_test.csv')
+y_to_test = pd.read_csv('./Y_test.csv')
+
+y_to_test = utils.to_categorical(y_to_test)
+
+metrics = model.evaluate(x_to_test, y_to_test)
+
+with open('metrics.csv', 'a') as file:
+    file.write(f'{build_number},{metrics}\n')
+
+predictions = model.predict(x_to_test)
+
+predictions.tofile('prediction.csv', sep=',', format='%s')

train.py

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+import pandas as pd
+import tensorflow as tf
+from keras.models import Sequential
+from keras.layers import Dense
+from keras import utils
+import os
+
+EPOCHS = int(os.environ['EPOCHS'])
+if EPOCHS <= 0:
+    EPOCHS = 1000
+
+X_train = pd.read_csv('./X_train.csv',
+                              engine = 'python',
+                              encoding = 'ISO-8859-1',
+                              sep=',')
+
+X_val = pd.read_csv('./X_val.csv',
+                              engine = 'python',
+                              encoding = 'ISO-8859-1',
+                              sep=',')
+
+Y_train = pd.read_csv('./Y_train.csv',
+                              engine = 'python',
+                              encoding = 'ISO-8859-1',
+                              sep=',')
+
+Y_val = pd.read_csv('./Y_val.csv',
+                              engine = 'python',
+                              encoding = 'ISO-8859-1',
+                              sep=',')
+
+
+Y_train = utils.to_categorical(Y_train)
+Y_val = utils.to_categorical(Y_val)
+
+model = Sequential(
+     [               
+         Dense(100, input_dim=X_train.shape[1], activation='relu'),
+         Dense(70, activation='relu'),
+         Dense(50, activation='relu'),
+         Dense(4, activation='softmax')
+     ], name = "Powerlifters_model"
+)
+
+model.compile(
+    loss=tf.keras.losses.CategoricalCrossentropy(),
+    optimizer=tf.keras.optimizers.Adam(),
+    metrics=['accuracy'])
+
+model.fit(
+  X_train,Y_train,
+    epochs = EPOCHS,
+    validation_data=(X_val, Y_val)
+)
+
+model.save('model')