:)

Jenkinsfile

@@ -46,7 +46,6 @@ pipeline {
 	}
     post {
         success {
-            build job: 's434695-evaluation/evaluation'
             mail body: 'SUCCESS TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
         }
 

MLproject

@@ -0,0 +1,11 @@
+name: 434695-mlflow
+
+docker_env:
+    image: shroomy/ium:7
+
+entry_points:
+    main:
+        parameters:
+            epochs: {type: int, default: 15}
+            batch_size: {type: int, default: 16}
+        command: "python3 vgsales-mlflow.py {epochs} {batch_size}"

vgsales-mlflow.py

@@ -0,0 +1,68 @@
+import sys
+from tensorflow.keras.backend import batch_dot, mean
+import pandas as pd
+import numpy as np
+from six import int2byte
+from sklearn import preprocessing
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.layers import Input, Dense, Activation,Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.models import Sequential
+import mlflow
+
+
+
+def my_main(epochs, batch_size):
+
+    vgsales=pd.read_csv('vgsales.csv')
+
+    vgsales['Nintendo'] = vgsales['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
+
+    Y = vgsales['Nintendo']
+    X = vgsales.drop(['Rank','Name','Platform','Year','Genre','Publisher','Nintendo'],axis = 1)
+
+    X_train, X_test, y_train, y_test = train_test_split(X,Y , test_size=0.2,train_size=0.8, random_state=21)
+
+    model = Sequential()
+    model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
+    model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
+
+    early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
+
+
+    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
+
+
+    prediction = model.predict(X_test)
+
+
+    rmse = mean_squared_error(y_test, prediction)
+
+
+    model.save('vgsales_model.h5')
+
+    return rmse, model
+
+
+
+epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 15
+batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 16
+
+
+with mlflow.start_run():
+        
+    rmse, model = my_main(epochs, batch_size)
+
+    mlflow.log_param("epochs", epochs)
+    mlflow.log_param("batch_size", batch_size)
+    mlflow.log_metric("rmse", rmse) 
+    mlflow.keras.log_model(model, 'vgsales_model.h5')
+    mlflow.keras.save_model(model, "my_model")