lab8 start, clean up old files

lab6/process_dataset.py

@@ -1,30 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-
-# usuwamy przy okazji puste pola
-lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
-
-# list_price moze byc do dwoch miejsc po przecinku
-lego['list_price'] = lego['list_price'].round(2)
-
-# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
-lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
-lego['piece_count'] = lego['piece_count'].apply(np.int64)
-lego['prod_id'] = lego['prod_id'].apply(np.int64)
-
-# wglad, statystyki
-print(lego)
-print(lego.describe(include='all'))
-
-# pierwszy podzial, wydzielamy zbior treningowy
-lego_train, lego_rem = train_test_split(lego, train_size=0.8, random_state=1)
-
-# drugi podział, wydzielamy walidacyjny i testowy
-lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5, random_state=1)
-
-# zapis
-lego.to_csv('lego_sets_clean.csv', index=None, header=True)
-lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
-lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
-lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

lab6/simple_regression.py

@@ -1,69 +0,0 @@
-import tensorflow as tf
-from keras import layers
-from keras.models import save_model
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import sys
-
-# Pobranie przykładowego argumentu trenowania
-EPOCHS_NUM = int(sys.argv[1])
-
-# Wczytanie danych
-data_train = pd.read_csv('lego_sets_clean_train.csv')
-data_test = pd.read_csv('lego_sets_clean_test.csv')
-
-# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
-train_piece_counts = np.array(data_train['piece_count'])
-train_prices = np.array(data_train['list_price'])
-test_piece_counts = np.array(data_test['piece_count'])
-test_prices = np.array(data_test['list_price'])
-
-# Normalizacja
-normalizer = layers.Normalization(input_shape=[1, ], axis=None)
-normalizer.adapt(train_piece_counts)
-
-# Inicjalizacja
-model = tf.keras.Sequential([
-    normalizer,
-    layers.Dense(units=1)
-])
-
-# Kompilacja
-model.compile(
-    optimizer=tf.optimizers.Adam(learning_rate=0.1),
-    loss='mean_absolute_error'
-)
-
-# Trening
-history = model.fit(
-    train_piece_counts,
-    train_prices,
-    epochs=EPOCHS_NUM,
-    verbose=0,
-    validation_split=0.2
-)
-
-# Wykonanie predykcji na danych ze zbioru testującego
-y_pred = model.predict(test_piece_counts)
-
-# Zapis predykcji do pliku
-results = pd.DataFrame({'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
-results.to_csv('lego_reg_results.csv', index=False, header=True)
-
-# Zapis modelu do pliku
-model.save('lego_reg_model')
-
-# Opcjonalne statystyki, wykresy
-'''
-hist = pd.DataFrame(history.history)
-hist['epoch'] = history.epoch
-print(hist.tail())
-
-plt.scatter(train_piece_counts, train_prices, label='Data')
-plt.plot(x, y_pred, color='k', label='Predictions')
-plt.xlabel('pieces')
-plt.ylabel('price')
-plt.legend()
-plt.show()
-'''

lab7/process_dataset.py

@@ -1,30 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-
-# usuwamy przy okazji puste pola
-lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
-
-# list_price moze byc do dwoch miejsc po przecinku
-lego['list_price'] = lego['list_price'].round(2)
-
-# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
-lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
-lego['piece_count'] = lego['piece_count'].apply(np.int64)
-lego['prod_id'] = lego['prod_id'].apply(np.int64)
-
-# wglad, statystyki
-print(lego)
-print(lego.describe(include='all'))
-
-# pierwszy podzial, wydzielamy zbior treningowy
-lego_train, lego_rem = train_test_split(lego, train_size=0.8, random_state=1)
-
-# drugi podział, wydzielamy walidacyjny i testowy
-lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5, random_state=1)
-
-# zapis
-lego.to_csv('lego_sets_clean.csv', index=None, header=True)
-lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
-lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
-lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

lab7/simple_regression_lab7.py

@@ -1,86 +0,0 @@
-import tensorflow as tf
-from keras import layers
-from keras.models import save_model
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from sacred import Experiment
-from sacred.observers import FileStorageObserver
-from sacred.observers import MongoObserver
-
-# Stworzenie obiektu klasy Experiment do śledzenia przebiegu regresji narzędziem Sacred
-ex = Experiment(save_git_info=False)
-
-# Dodanie obserwatora FileObserver
-ex.observers.append(FileStorageObserver('runs'))
-
-#Dodanie obserwatora Mongo
-#ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017', db_name='sacred'))
-
-# Przykładowa modyfikowalna z Sacred konfiguracja wybranych parametrów treningu
-@ex.config
-def config():
-    epochs = 100
-    units = 1
-    learning_rate = 0.1
-
-
-# Reszta kodu wrzucona do udekorowanej funkcji train do wywołania przez Sacred, żeby coś było capture'owane
-@ex.capture
-def train(epochs, units, learning_rate, _run):
-
-    # Wczytanie danych
-    data_train = pd.read_csv('lego_sets_clean_train.csv')
-    data_test = pd.read_csv('lego_sets_clean_test.csv')
-
-    # Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
-    train_piece_counts = np.array(data_train['piece_count'])
-    train_prices = np.array(data_train['list_price'])
-    test_piece_counts = np.array(data_test['piece_count'])
-    test_prices = np.array(data_test['list_price'])
-
-    # Normalizacja
-    normalizer = layers.Normalization(input_shape=[1, ], axis=None)
-    normalizer.adapt(train_piece_counts)
-
-    # Inicjalizacja
-    model = tf.keras.Sequential([
-        normalizer,
-        layers.Dense(units=units)
-    ])
-
-    # Kompilacja
-    model.compile(
-        optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
-        loss='mean_absolute_error'
-    )
-
-    # Trening
-    history = model.fit(
-        train_piece_counts,
-        train_prices,
-        epochs=epochs,
-        verbose=0,
-        validation_split=0.2
-    )
-
-    # Wykonanie predykcji na danych ze zbioru testującego
-    y_pred = model.predict(test_piece_counts)
-
-    # Zapis predykcji do pliku
-    results = pd.DataFrame(
-        {'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
-    results.to_csv('lego_reg_results.csv', index=False, header=True)
-
-    # Zapis modelu do pliku standardowo poprzez metodę kerasa i poprzez metodę obiektu Experiment z Sacred
-    model.save('lego_reg_model')
-    ex.add_artifact('lego_reg_model/saved_model.pb')
-
-    # Przykładowo zwracamy loss ostatniej epoki w charakterze wyników, żeby było widoczne w plikach zapisanych przez obserwator
-    hist = pd.DataFrame(history.history)
-    hist['epoch'] = history.epoch
-    _run.log_scalar('final.training.loss', hist['loss'].iloc[-1])
-
-@ex.automain
-def main(units, learning_rate):
-    train()

lab8/Dockerfile

@@ -0,0 +1,20 @@
+FROM ubuntu:latest
+
+WORKDIR /ium
+
+RUN apt update && apt install -y python3-pip
+
+RUN pip3 install pandas
+RUN pip3 install numpy
+RUN pip3 install sklearn
+RUN pip3 install tensorflow
+RUN pip3 install matplotlib
+RUN pip3 install keras
+RUN pip3 install sacred
+RUN pip3 install pymongo
+RUN pip3 install GitPython
+RUN pip3 install mlflow
+
+COPY ./lego_sets.csv ./
+COPY ./process_dataset.py ./
+COPY ./simple_regression_lab8.py ./

lab8/Jenkinsfile_eval

@@ -0,0 +1,61 @@
+pipeline {
+    agent {
+        dockerfile {
+            dir 'lab8'
+        }
+        }
+	parameters {
+        gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
+        buildSelector(
+			defaultSelector: lastSuccessful(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+    }
+    stages {
+        stage('Stage 1') {
+            steps {
+                    git branch: "${params.BRANCH}", url: 'https://git.wmi.amu.edu.pl/s449288/ium_s449288.git'
+				    sh 'chmod u+x ./evaluate.py'
+					echo 'Copying datasets from the create-dataset job...'
+					copyArtifacts filter: 'lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
+					echo 'Datasets copied'					
+					echo 'Copying model from the training job...'
+					copyArtifacts filter: 'lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR') 				
+					echo 'Model copied'
+					sh 'tar xvzf lego_reg_model.tar.gz'				
+					echo 'Optional copying of the metrics file from previous build...'
+					copyArtifacts filter: 'eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
+					echo 'Metrics file copied if it did not exist'
+					echo 'Evaluating model...'
+					sh 'python3 evaluate.py'
+					echo 'Model evaluated. Metrics saved. Plot saved.'
+                    sh 'head eval_results.txt'	
+					sh 'file error_plot.jpg'
+                    echo 'Archiving metrics file...'
+                    archiveArtifacts 'eval_results.txt'
+                    echo 'File archived'
+                    script {
+                        LAST_MAE = sh (
+                        script: 'tail -1 eval_results.txt',
+                        returnStdout: true
+                        ).trim()
+                    }
+				}
+            }
+        }
+	post {
+        success {
+            emailext body: "SUCCESS - ${LAST_MAE} MAE", subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            emailext body: "FAILURE - ${LAST_MAE} MAE", subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: "UNSTABLE - ${LAST_MAE} MAE", subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: "CHANGED - ${LAST_MAE} MAE", subject: 's449288-evaluation build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }		
+    }
+}

lab8/Jenkinsfile_train

@@ -0,0 +1,47 @@
+pipeline {
+    agent {
+        dockerfile {
+            dir 'lab8'
+        }
+        }
+    stages {
+        stage('Stage 1') {
+            steps {
+				    sh 'chmod u+x ./simple_regression_lab8.py'
+					echo 'Copying datasets from create-dataset...'
+					copyArtifacts filter: '*', projectName: 's449288-create-dataset'
+					echo 'Datasets copied'
+					echo 'Conducting simple regression model test'
+					sh 'python3 simple_regression_lab8.py'
+					echo 'Model and predictions saved'
+                    sh 'head lego_reg_results.csv'
+                    echo 'Archiving model...'
+                    sh 'ls -lh lego_reg_model'
+                    sh 'tar -czf lego_reg_model.tar.gz lego_reg_model/'
+                    archiveArtifacts 'lego_reg_model.tar.gz'
+                    echo 'Model archived'
+					echo 'Archiving Sacreds output repo...'
+					sh 'ls -lh runs/*/'
+					sh 'tar -czf sacred_runs.tar.gz runs/'
+                    archiveArtifacts 'sacred_runs.tar.gz'
+					echo 'Sacreds repo archived'
+					echo 'Launching the s449288-evaluation job...'
+					build job: 's449288-evaluation/master/'
+				}
+            }
+        }
+    post {
+        success {
+            emailext body: 'SUCCESS', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            emailext body: 'FAILURE', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: 'UNSTABLE', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: 'CHANGED', subject: 's449288-training build status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }		
+    }
+}

lab8/MLproject

@@ -0,0 +1 @@
+ 

simple_regression_lab8.py

@@ -0,0 +1,118 @@
+import tensorflow as tf
+from keras import layers
+from keras.models import save_model
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sacred.observers import MongoObserver
+import mlflow
+from urllib.parse import urlparse
+
+# Konfiguracja serwera i nazwy eksperymentu MLflow
+mlflow.set_tracking_uri('http://tzietkiewicz.vm.wmi.amu.edu.pl:5000/#/')
+mlflow.set_experiment('s449288')
+
+# Stworzenie obiektu klasy Experiment do śledzenia przebiegu regresji narzędziem Sacred
+ex = Experiment(save_git_info=False)
+
+# Dodanie obserwatora FileObserver
+ex.observers.append(FileStorageObserver('runs'))
+
+#Dodanie obserwatora Mongo
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+
+# Przykładowa modyfikowalna z Sacred konfiguracja wybranych parametrów treningu
+@ex.config
+def config():
+    epochs = 100
+    units = 1
+    learning_rate = 0.1
+
+
+# Reszta kodu wrzucona do udekorowanej funkcji train do wywołania przez Sacred, żeby coś było capture'owane
+@ex.capture
+def train(epochs, units, learning_rate, _run):
+
+    # Podpięcie treningu do MLflow
+    with mlflow.start_run() as run:
+        print('MLflow run experiment_id: {0}'.format(run.info.experiment_id))
+        print('MLflow run artifact_uri: {0}'.format(run.info.artifact_uri))
+
+        # Wczytanie danych
+        data_train = pd.read_csv('lego_sets_clean_train.csv')
+        data_test = pd.read_csv('lego_sets_clean_test.csv')
+
+        # Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
+        train_piece_counts = np.array(data_train['piece_count'])
+        train_prices = np.array(data_train['list_price'])
+        test_piece_counts = np.array(data_test['piece_count'])
+        test_prices = np.array(data_test['list_price'])
+
+        # Normalizacja
+        normalizer = layers.Normalization(input_shape=[1, ], axis=None)
+        normalizer.adapt(train_piece_counts)
+
+        # Inicjalizacja
+        model = tf.keras.Sequential([
+            normalizer,
+            layers.Dense(units=units)
+        ])
+
+        # Kompilacja
+        model.compile(
+            optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
+            loss='mean_absolute_error'
+        )
+
+        # Trening
+        history = model.fit(
+            train_piece_counts,
+            train_prices,
+            epochs=epochs,
+            verbose=0,
+            validation_split=0.2
+        )
+
+        # Wykonanie predykcji na danych ze zbioru testującego
+        y_pred = model.predict(test_piece_counts)
+
+        # Zapis predykcji do pliku
+        results = pd.DataFrame(
+            {'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
+        results.to_csv('lego_reg_results.csv', index=False, header=True)
+
+        # Zapis modelu do pliku standardowo poprzez metodę kerasa i poprzez metodę obiektu Experiment z Sacred
+        model.save('lego_reg_model')
+        ex.add_artifact('lego_reg_model/saved_model.pb')
+
+        # Przykładowo zwracamy loss ostatniej epoki w charakterze wyników, żeby było widoczne w plikach zapisanych przez obserwator
+        hist = pd.DataFrame(history.history)
+        hist['epoch'] = history.epoch
+        _run.log_scalar('final.training.loss', hist['loss'].iloc[-1])
+
+        # Ewaluacja MAE na potrzeby MLflow (kopia z evaluate.py)
+        mae = model.evaluate(
+            test_piece_counts,
+            test_prices, verbose=0)
+
+        # Zapis parametrów i metryk dla MLflow
+        mlflow.log_param('epochs', epochs)
+        mlflow.log_param('units', units)
+        mlflow.log_param('learning_rate', learning_rate)
+        mlflow.log_metric("mae", mae)
+
+        # Logowanie i zapis modelu dla Mlflow
+        signature = mlflow.models.signature.infer_signature(train_piece_counts, model.predict(train_piece_counts))
+        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
+        if tracking_url_type_store != 'file':
+            mlflow.keras.log_model(model, 'lego-model', registered_model_name='TFLegoModel',
+                                     signature=signature)
+        else:
+            mlflow.keras.log_model(model, 'model', signature=signature, input_example=500)
+
+
+@ex.automain
+def main(epochs, units, learning_rate):
+    train()