Sacred

JenkinsfileDatasetStats

@@ -20,20 +20,27 @@ pipeline {
       }
       stage('Prepare stats') {
          agent { 
-            docker {
+            // no idea why it doesn't work
-               image 'mmoryl/ium:latest'
+            // docker {
+            //    image 'mmoryl/ium:latest'
+            //    reuseNode true
+            // }
+            dockerfile {
+               filename 'Lab4.dockerfile'
                reuseNode true
             }
          }
          steps {
             copyArtifacts projectName: 'z487183-create-dataset'
             sh './prepare-stats.sh'
-            archiveArtifacts 'stats.txt'
+            sh 'mkdir sacred-files'
             sh 'python3 property_model.py'
             sh 'python3 predict_values.py'
-            archiveArtifacts 'test_predictions.csv'
+            // archiveArtifacts 'test_predictions.csv'
-            archiveArtifacts 'train_predictions.csv'
+            // archiveArtifacts 'train_predictions.csv'
-            archiveArtifacts 'dev_predictions.csv'
+            // archiveArtifacts 'dev_predictions.csv'
+            // archiveArtifacts 'stats.txt'
+            archiveArtifacts 'my_runs/**'
          }
       }
    }

Lab4.dockerfile

@@ -7,6 +7,9 @@ RUN pip install scikit-learn
 RUN pip install kaggle
 RUN pip install keras
 RUN pip install tensorflow
+RUN apt-get install -y git
+RUN pip install sacred
+RUN pip install pymongo
 
 ARG DEBIAN_FRONTEND=noninteractive
 

property_model.py

@@ -1,33 +1,74 @@
 import pandas as pd
 from keras.models import Sequential
 from keras.layers import Dense
+from sacred import Experiment
+import json
+from sacred.observers import MongoObserver
+from sacred.observers import FileStorageObserver
 
-# prepare dataset
+ex = Experiment("z487183", interactive=True)
-features = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom']
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
-target = 'PriceAboveMedian'
+ex.observers.append(FileStorageObserver('my_runs'))
 
-X_train = pd.read_csv('X_train.csv').values
+@ex.config
-y_train = pd.read_csv('Y_train.csv').values
+def my_config():
+    optimizer = 'sgd'
+    loss_function = 'binary_crossentropy'
 
-X_dev = pd.read_csv('X_val.csv').values
+@ex.automain
-y_dev = pd.read_csv('Y_val.csv').values
+def my_main(optimizer, loss_function, _run):
+    # prepare dataset
+    features = ['Rooms', 'Distance', 'Bedroom2', 'Bathroom']
 
-X_test = pd.read_csv('X_test.csv').values
+    X_train_resource = ex.open_resource('X_train.csv')
-y_test = pd.read_csv('Y_test.csv').values
+    y_train_resource = ex.open_resource('Y_train.csv')
 
-# model definition
+    X_dev_resource = ex.open_resource('X_val.csv')
-model = Sequential([
+    y_dev_resource = ex.open_resource('Y_val.csv')
-    Dense(32, activation='relu', input_shape=(len(features),)),
-    Dense(32, activation='relu'),
-    Dense(1, activation='sigmoid'),
-])
 
-#compile and train
+    X_test_resource = ex.open_resource('X_test.csv')
-model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
+    y_test_resource = ex.open_resource('Y_test.csv')
-hist = model.fit(X_train, y_train,
-          batch_size=32, epochs=100,
-          validation_data=(X_dev, y_dev))
 
-model.evaluate(X_test, y_test)[1]
+    X_train = pd.read_csv(X_train_resource).values
+    y_train = pd.read_csv(y_train_resource).values
 
-model.save('model.h5')
+    X_dev = pd.read_csv(X_dev_resource).values
+    y_dev = pd.read_csv(y_dev_resource).values
+
+    X_test = pd.read_csv(X_test_resource).values
+    y_test = pd.read_csv(y_test_resource).values
+
+    # model definition
+    model = Sequential([
+        Dense(32, activation='relu', input_shape=(len(features),)),
+        Dense(32, activation='relu'),
+        Dense(1, activation='sigmoid'),
+    ])
+
+    #compile and train
+    model.compile(optimizer=optimizer, loss=loss_function, metrics=['accuracy'])
+    hist = model.fit(X_train, y_train,
+            batch_size=32, epochs=100,
+            validation_data=(X_dev, y_dev))
+    model.save('model.h5')
+
+    loss, accuracy = model.evaluate(X_test, y_test)
+    _run.log_scalar("training.loss", loss)
+    _run.log_scalar("training.accuracy", accuracy)
+
+    with open("sacred-files/parameters.json", 'w') as file:
+        json.dump(my_config(), file)
+
+    with open('property_model.py', 'r') as current_file:
+        file_content = current_file.read()
+        with open('sacred-files/source_code.py', 'w') as new_file:
+            new_file.write(file_content)
+
+    with open("sacred-files/metrics.txt", 'w') as file:
+        file.write(f"loss: {loss}")
+        file.write(f"accuracy: {accuracy}")
+
+    ex.add_artifact("model.h5")
+    ex.add_artifact("sacred-files/parameters.json")
+    ex.add_artifact("sacred-files/source_code.py")
+    ex.add_artifact("sacred-files/metrics.txt")